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1
Holmstrom, Lars, Patrick D. Roberts, and Christine V. Portfors. "Responses to Social Vocalizations in the Inferior Colliculus of the Mustached Bat Are Influenced by Secondary Tuning Curves." Journal of Neurophysiology 98, no. 6 (December 2007): 3461–72. http://dx.doi.org/10.1152/jn.00638.2007.
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Neurons in the inferior colliculus (IC) of the mustached bat integrate input from multiple frequency bands in a complex fashion. These neurons are important for encoding the bat's echolocation and social vocalizations. The purpose of this study was to quantify the contribution of complex frequency interactions on the responses of IC neurons to social vocalizations. Neural responses to single tones, two-tone pairs, and social vocalizations were recorded in the IC of the mustached bat. Three types of data driven stimulus-response models were designed for each neuron from single tone and tone pair stimuli to predict the responses of individual neurons to social vocalizations. The first model was generated only using the neuron's primary frequency tuning curve, whereas the second model incorporated the entire hearing range of the animal. The extended model often predicted responses to many social vocalizations more accurately for multiply tuned neurons. One class of multiply tuned neuron that likely encodes echolocation information also responded to many of the social vocalizations, suggesting that some neurons in the mustached bat IC have dual functions. The third model included two-tone frequency tunings of the neurons. The responses to vocalizations were better predicted by the two-tone models when the neuron had inhibitory frequency tuning curves that were not near the neuron's primary tuning curve. Our results suggest that complex frequency interactions in the IC determine neural responses to social vocalizations and some neurons in IC have dual functions that encode both echolocation and social vocalization signals.
2
Kirch, Christoph, and Leonardo L. Gollo. "Spatially resolved dendritic integration: towards a functional classification of neurons." PeerJ 8 (November 24, 2020): e10250. http://dx.doi.org/10.7717/peerj.10250.
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The vast tree-like dendritic structure of neurons allows them to receive and integrate input from many neurons. A wide variety of neuronal morphologies exist, however, their role in dendritic integration, and how it shapes the response of the neuron, is not yet fully understood. Here, we study the evolution and interactions of dendritic spikes in excitable neurons with complex real branch structures. We focus on dozens of digitally reconstructed illustrative neurons from the online repository NeuroMorpho.org, which contains over 130,000 neurons. Yet, our methods can be promptly extended to any other neuron. This approach allows us to estimate and map specific and heterogeneous patterns of activity observed across extensive dendritic trees with thousands of compartments. We propose a classification of neurons based on the location of the soma (centrality) and the number of branches connected to the soma. These are key topological factors in determining the neuron’s energy consumption, firing rate, and the dynamic range, which quantifies the range in synaptic input rate that can be reliably encoded by the neuron’s firing rate. Moreover, we find that bifurcations, the structural building blocks of complex dendrites, play a major role in increasing the dynamic range of neurons. Our results provide a better understanding of the effects of neuronal morphology in the diversity of neuronal dynamics and function.
3
Pesavento, Michael J., Cynthia D. Rittenhouse, and David J. Pinto. "Response Sensitivity of Barrel Neuron Subpopulations to Simulated Thalamic Input." Journal of Neurophysiology 103, no. 6 (June 2010): 3001–16. http://dx.doi.org/10.1152/jn.01053.2009.
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Our goal is to examine the relationship between neuron- and network-level processing in the context of a well-studied cortical function, the processing of thalamic input by whisker-barrel circuits in rodent neocortex. Here we focus on neuron-level processing and investigate the responses of excitatory and inhibitory barrel neurons to simulated thalamic inputs applied using the dynamic clamp method in brain slices. Simulated inputs are modeled after real thalamic inputs recorded in vivo in response to brief whisker deflections. Our results suggest that inhibitory neurons require more input to reach firing threshold, but then fire earlier, with less variability, and respond to a broader range of inputs than do excitatory neurons. Differences in the responses of barrel neuron subtypes depend on their intrinsic membrane properties. Neurons with a low input resistance require more input to reach threshold but then fire earlier than neurons with a higher input resistance, regardless of the neuron's classification. Our results also suggest that the response properties of excitatory versus inhibitory barrel neurons are consistent with the response sensitivities of the ensemble barrel network. The short response latency of inhibitory neurons may serve to suppress ensemble barrel responses to asynchronous thalamic input. Correspondingly, whereas neurons acting as part of the barrel circuit in vivo are highly selective for temporally correlated thalamic input, excitatory barrel neurons acting alone in vitro are less so. These data suggest that network-level processing of thalamic input in barrel cortex depends on neuron-level processing of the same input by excitatory and inhibitory barrel neurons.
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Cardi, P., and F. Nagy. "A rhythmic modulatory gating system in the stomatogastric nervous system of Homarus gammarus. III. Rhythmic control of the pyloric CPG." Journal of Neurophysiology 71, no. 6 (June 1, 1994): 2503–16. http://dx.doi.org/10.1152/jn.1994.71.6.2503.
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1. Two modulatory neurons, P and commissural pyloric (CP), known to be involved in the long-term maintenance of pyloric central pattern generator operation in the rock lobster Homarus gammarus, are members of the commissural pyloric oscillator (CPO), a higher-order oscillator influencing the pyloric network. 2. The CP neuron was endogenously oscillating in approximately 30% of the preparations in which its cell body was impaled. Rhythmic inhibitory feedback from the pyloric pacemaker anterior burster (AB) neuron stabilized the CP neuron's endogenous rhythm. 3. The organization of the CPO is described. Follower commissural neurons, the F cells, and the CP neuron receive a common excitatory postsynaptic potential from another commissural neuron, the large exciter (LE). When in oscillatory state, CP in turn excites the LE neuron. This positive feedback may maintain long episodes of CP oscillations. 4. The pyloric pacemaker neurons follow the CPO rhythm with variable coordination modes (i.e., 1:1, 1:2) and switch among these modes when their membrane potential is modified. The CPO inputs strongly constrain the pyloric period, which as a result may adopt only a few discrete values. This effect is based on mechanisms of entrainment between the CPO and the pyloric oscillator. 5. Pyloric constrictor neurons show differential sensitivity from the pyloric pacemaker neurons with respect to the CPO inputs. Consequently, their bursting period can be a shorter harmonic of the bursting period of the pyloric pacemakers neurons. 6. The CPO neurons seem to be the first example of modulatory gating neurons that also give timing cues to a rhythmic pattern generating network.
5
Stiefel, Klaus M., and G. Bard Ermentrout. "Neurons as oscillators." Journal of Neurophysiology 116, no. 6 (December 1, 2016): 2950–60. http://dx.doi.org/10.1152/jn.00525.2015.
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Regularly spiking neurons can be described as oscillators. In this article we review some of the insights gained from this conceptualization and their relevance for systems neuroscience. First, we explain how a regularly spiking neuron can be viewed as an oscillator and how the phase-response curve (PRC) describes the response of the neuron's spike times to small perturbations. We then discuss the meaning of the PRC for a single neuron's spiking behavior and review the PRCs measured from a variety of neurons in a range of spiking regimes. Next, we show how the PRC can be related to a number of common measures used to quantify neuronal firing, such as the spike-triggered average and the peristimulus histogram. We further show that the response of a neuron to correlated inputs depends on the shape of the PRC. We then explain how the PRC of single neurons can be used to predict neural network behavior. Given the PRC, conduction delays, and the waveform and time course of the synaptic potentials, it is possible to predict neural population behavior such as synchronization. The PRC also allows us to quantify the robustness of the synchronization to heterogeneity and noise. We finally ask how to combine the measured PRCs and the predictions based on PRC to further the understanding of systems neuroscience. As an example, we discuss how the change of the PRC by the neuromodulator acetylcholine could lead to a destabilization of cortical network dynamics. Although all of these studies are grounded in mathematical abstractions that do not strictly hold in biology, they provide good estimates for the emergence of the brain's network activity from the properties of individual neurons. The study of neurons as oscillators can provide testable hypotheses and mechanistic explanations for systems neuroscience.
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Ruff, Douglas A., and Richard T. Born. "Feature attention for binocular disparity in primate area MT depends on tuning strength." Journal of Neurophysiology 113, no. 5 (March 1, 2015): 1545–55. http://dx.doi.org/10.1152/jn.00772.2014.
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Attending to a stimulus modulates the responses of sensory neurons that represent features of that stimulus, a phenomenon named “feature attention.” For example, attending to a stimulus containing upward motion enhances the responses of upward-preferring direction-selective neurons in the middle temporal area (MT) and suppresses the responses of downward-preferring neurons, even when the attended stimulus is outside of the spatial receptive fields of the recorded neurons (Treue S, Martinez-Trujillo JC. Nature 399: 575–579, 1999). This modulation renders the representation of sensory information across a neuronal population more selective for the features present in the attended stimulus (Martinez-Trujillo JC, Treue S. Curr Biol 14: 744–751, 2004). We hypothesized that if feature attention modulates neurons according to their tuning preferences, it should also be sensitive to their tuning strength, which is the magnitude of the difference in responses to preferred and null stimuli. We measured how the effects of feature attention on MT neurons in rhesus monkeys ( Macaca mulatta) depended on the relationship between features—in our case, direction of motion and binocular disparity—of the attended stimulus and a neuron's tuning for those features. We found that, as for direction, attention to stimuli containing binocular disparity cues modulated the responses of MT neurons and that the magnitude of the modulation depended on both a neuron's tuning preferences and its tuning strength. Our results suggest that modulation by feature attention may depend not just on which features a neuron represents but also on how well the neuron represents those features.
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JOHNSTON, DAVID, SIMON PETER MEKHAIL, MARY ANN GO, and VINCENT R. DARIA. "MODELING NEURONAL RESPONSE TO SIMULTANEOUS AND SEQUENTIAL MULTI-SITE SYNAPTIC STIMULATION." International Journal of Modern Physics: Conference Series 17 (January 2012): 1–8. http://dx.doi.org/10.1142/s2010194512007878.
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The flow of information in the brain theorizes that each neuron in a network receives synaptic inputs and sends off its processed signals to neighboring neurons. Here, we model these synaptic inputs to understand how each neuron processes these inputs and transmits neurotransmitters to neighboring neurons. We use the NEURON simulation package to stimulate a neuron at multiple synaptic locations along its dendritic tree. Accumulation of multiple synaptic inputs causes changes in the neuron's membrane potential leading to firing of an action potential. Our simulations show that simultaneous synaptic stimulation approaches firing of an action potential at lesser inputs compared to sequential stimulation at multiple sites distributed along several dendritic branches.
8
Baker, Curtis L. "Spatial- and temporal-frequency selectivity as a basis for velocity preference in cat striate cortex neurons." Visual Neuroscience 4, no. 02 (February 1990): 101–13. http://dx.doi.org/10.1017/s0952523800002273.
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AbstractMeasurements were made of the optimal velocity for drifting bar-shaped stimuli to excite striate cortex neurons of the cat. These data were compared to the optimal spatial and temporal frequencies of the same neurons, as determined with drifting sine-wave grating stimuli. A systematic relationship was revealed, whereby those neurons preferring higher velocities of bar motion also preferred lower spatial and higher temporal frequencies of gratings. The optimal bar velocity for a given neuron could be quantitatively predicted from the ratio of that neuron's optimal temporal frequency to its optimal spatial frequency.
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Wright, Nathaniel C., Mahmood S. Hoseini, Tansel Baran Yasar, and Ralf Wessel. "Coupling of synaptic inputs to local cortical activity differs among neurons and adapts after stimulus onset." Journal of Neurophysiology 118, no. 6 (December 1, 2017): 3345–59. http://dx.doi.org/10.1152/jn.00398.2017.
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Cortical activity contributes significantly to the high variability of sensory responses of interconnected pyramidal neurons, which has crucial implications for sensory coding. Yet, largely because of technical limitations of in vivo intracellular recordings, the coupling of a pyramidal neuron’s synaptic inputs to the local cortical activity has evaded full understanding. Here we obtained excitatory synaptic conductance ( g) measurements from putative pyramidal neurons and local field potential (LFP) recordings from adjacent cortical circuits during visual processing in the turtle whole brain ex vivo preparation. We found a range of g-LFP coupling across neurons. Importantly, for a given neuron, g-LFP coupling increased at stimulus onset and then relaxed toward intermediate values during continued visual stimulation. A model network with clustered connectivity and synaptic depression reproduced both the diversity and the dynamics of g-LFP coupling. In conclusion, these results establish a rich dependence of single-neuron responses on anatomical, synaptic, and emergent network properties. NEW & NOTEWORTHY Cortical neurons are strongly influenced by the networks in which they are embedded. To understand sensory processing, we must identify the nature of this influence and its underlying mechanisms. Here we investigate synaptic inputs to cortical neurons, and the nearby local field potential, during visual processing. We find a range of neuron-to-network coupling across cortical neurons. This coupling is dynamically modulated during visual processing via biophysical and emergent network properties.
10
Hong, En, Fatma Gurel Kazanci, and Astrid A. Prinz. "Different Roles of Related Currents in Fast and Slow Spiking of Model Neurons From Two Phyla." Journal of Neurophysiology 100, no. 4 (October 2008): 2048–61. http://dx.doi.org/10.1152/jn.90567.2008.
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Neuronal activity arises from the interplay of membrane and synaptic currents. Although many channel proteins conducting these currents are phylogenetically conserved, channels of the same type in different animals can have different voltage dependencies and dynamics. What does this mean for our ability to derive rules about the role of different types of ion channels in neuronal activity? Can results about the role of a particular channel type in a particular type of neuron be generalized to other neuron types? We compare spiking model neurons in two databases constructed by exploring the maximal conductance spaces of two models. The first is a model of crustacean stomatogastric neurons, and the second is a model of rodent thalamocortical neurons, but both models contain similar types of membrane currents. Spiking neurons in both databases show distinct fast and slow subpopulations, but our analysis reveals that related currents play different roles in fast and slow spiking in the stomatogastric versus thalamocortical neurons. This analysis involved conductance-space visualization and comparison of voltage traces, current traces, and frequency-current relationships from all spiker subpopulations. Our results are consistent with previous work indicating that the role a membrane current plays in shaping a neuron's behavior depends on the voltage dependence and dynamics of that current and may be different in different neuron types depending on the properties of other currents it is interacting with. Conclusions about the function of a type of membrane current based on experiments or simulations in one type of neuron may therefore not generalize to other neuron types.
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Nykamp, Duane Q., and Daniel Tranchina. "A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Extension to Slow Inhibitory Synapses." Neural Computation 13, no. 3 (March 1, 2001): 511–46. http://dx.doi.org/10.1162/089976601300014448.
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A previously developed method for efficiently simulating complex networks of integrate-and-fire neurons was specialized to the case in which the neurons have fast unitary postsynaptic conductances. However, inhibitory synaptic conductances are often slower than excitatory ones for cortical neurons, and this difference can have a profound effect on network dynamics that cannot be captured with neurons that have only fast synapses. We thus extend the model to include slow inhibitory synapses. In this model, neurons are grouped into large populations of similar neurons. For each population, we calculate the evolution of a probability density function (PDF), which describes the distribution of neurons over state-space. The population firing rate is given by the flux of probability across the threshold voltage for firing an action potential. In the case of fast synaptic conductances, the PDF was one-dimensional, as the state of a neuron was completely determined by its transmembrane voltage. An exact extension to slow inhibitory synapses increases the dimension of the PDF to two or three, as the state of a neuron now includes the state of its inhibitory synaptic conductance. However, by assuming that the expected value of a neuron's inhibitory conductance is independent of its voltage, we derive a reduction to a one-dimensional PDF and avoid increasing the computational complexity of the problem. We demonstrate that although this assumption is not strictly valid, the results of the reduced model are surprisingly accurate.
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HERRMANN, CHRISTOPH S., and ANDREAS KLAUS. "AUTAPSE TURNS NEURON INTO OSCILLATOR." International Journal of Bifurcation and Chaos 14, no. 02 (February 2004): 623–33. http://dx.doi.org/10.1142/s0218127404009338.
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Recently, neurobiologists have discovered axons on neurons which synapse on the same neuron's dendrites — so-called autapses. It is not yet clear what functional significance autapses offer for neural behavior. This is an ideal case for using a physical simulation to investigate how an autapse alters the firing of a neuron. We simulated a neural basket cell via the Hodgkin–Huxley equations and implemented an autapse which feeds back onto the soma of the neuron. The behavior of the cell was compared with and without autaptic feedback. Our artificial autapse neuron (AAN) displays oscillatory behavior which is not observed for the same model neuron without autapse. The neuron oscillates between two functional states: one where it fires at high frequency and another where firing is suppressed. This behavior is called "spike bursting" and represents a common pattern recorded from cerebral neurons.
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Mazurek, Kevin A., and Marc H. Schieber. "Mirror neurons precede non-mirror neurons during action execution." Journal of Neurophysiology 122, no. 6 (December 1, 2019): 2630–35. http://dx.doi.org/10.1152/jn.00653.2019.
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Mirror neurons are thought to represent an individual’s ability to understand the actions of others by discharging as one individual performs or observes another individual performing an action. Studies typically have focused on mirror neuron activity during action observation, examining activity during action execution primarily to validate mirror neuron involvement in the motor act. As a result, little is known about the precise role of mirror neurons during action execution. In this study, during execution of reach-grasp-manipulate movements, we found activity of mirror neurons generally preceded that of non-mirror neurons. Not only did the onset of task-related modulation occur earlier in mirror neurons, but state transitions detected by hidden Markov models also occurred earlier in mirror neuron populations. Our findings suggest that mirror neurons may be at the forefront of action execution. NEW & NOTEWORTHY Mirror neurons commonly are thought to provide a neural substrate for understanding the actions of others, but mirror neurons also are active during action execution, when additional, non-mirror neurons are active as well. Examining the timing of activity during execution of a naturalistic reach-grasp-manipulate task, we found that mirror neuron activity precedes that of non-mirror neurons at both the unit and the population level. Thus mirror neurons may be at the leading edge of action execution.
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Masuda, Naoki, and Kazuyuki Aihara. "Spatiotemporal Spike Encoding of a Continuous External Signal." Neural Computation 14, no. 7 (July 1, 2002): 1599–628. http://dx.doi.org/10.1162/08997660260028638.
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Interspike intervals of spikes emitted from an integrator neuron model of sensory neurons can encode input information represented as a continuous signal from a deterministic system. If a real brain uses spike timing as a means of information processing, other neurons receiving spatiotemporal spikes from such sensory neurons must also be capable of treating information included in deterministic interspike intervals. In this article, we examine functions of neurons modeling cortical neurons receiving spatiotemporal spikes from many sensory neurons. We show that such neuron models can encode stimulus information passed from the sensory model neurons in the form of interspike intervals. Each sensory neuron connected to the cortical neuron contributes equally to the information collection by the cortical neuron. Although the incident spike train to the cortical neuron is a superimposition of spike trains from many sensory neurons, it need not be decomposed into spike trains according to the input neurons. These results are also preserved for generalizations of sensory neurons such as a small amount of leak, noise, inhomogeneity in firing rates, or biases introduced in the phase distributions.
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Pubols, B. H., J. H. Haring, and M. J. Rowinski. "Patterns of resting discharge in neurons of the raccoon main cuneate nucleus." Journal of Neurophysiology 61, no. 6 (June 1, 1989): 1131–41. http://dx.doi.org/10.1152/jn.1989.61.6.1131.
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1. The presence and pattern of resting discharge were examined in 100 single neurons of the raccoon main cuneate nucleus (MCN). Of these, 66 were activated, either antidromically or synaptically, by electrical stimulation of the contralateral thalamic ventrobasal complex (VB), and 34 were activated by stimulation of the ipsilateral cerebellum (CB). 2. Forty-one percent of VB-activated neurons displayed a resting discharge, whereas 32% of CB-activated neurons did. Most neurons activated from VB and showing a resting discharge fired in bursts of 2-5 spikes, whereas those activated from CB and showing a resting discharge generally fired as single, irregularly spaced spikes, with occasional bursts in some neurons. 3. All neurons antidromically activated from VB were histologically localized within the clusters region of the MCN, whereas those antidromically activated from CB were confined to its polymorphic region. Neurons synaptically activated from either VB or CB were located in either of these regions. 4. Differences in the proportions of neurons displaying a resting discharge did not vary significantly as a function of type of preparation: methoxyflurane anesthesia, pentobarbital sodium anesthesia, decerebrate (the latter CB-activated only). 5. Although the sample sizes were too small to demonstrate statistical significance, neurons exhibiting a resting discharge were more likely to show a bursting pattern in methoxyflurane-anesthetized preparations than were neurons in pentobarbital sodium-anesthetized preparations. 6. The probability of having no resting discharge, firing in bursts, or firing in single spikes was not related to cutaneous submodality [rapidly adapting (RA), slowly adapting (SA), Pacinian (Pc)], or to receptive field (RF) locus (glabrous versus hairy skin). 7. The overall mean rate of firing (11.8 Hz) was not significantly different for bursting versus nonbursting neurons. 8. In bursting neurons, median interspike intervals (ISIs) varied between 1.3 and 2.3 ms. Most bursting neurons also had a range of short or minimal interburst intervals (MIBIs), characteristic for each neuron, whose medians varied from neuron to neuron between 34 and 90 ms. Distributions of within-burst ISIs and MIBIs had comparable coefficients of variation, varying between 0.031 and 0.223. 9. The application of a mechanical stimulus to a neuron's peripheral RF led to a decrease in interburst intervals, accompanied, depending upon the unit, by either an increase or a decrease in the number of spikes per burst. 10. Results are discussed in terms of the functional significance of resting discharge, including bursting, and possible roles in somatosensory information
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Márquez-Vera, Carlos Antonio, Zaineb Yakoub, Marco Antonio Márquez Vera, and Alfian Ma'arif. "Spiking PID Control Applied in the Van de Vusse Reaction." International Journal of Robotics and Control Systems 1, no. 4 (November 25, 2021): 488–500. http://dx.doi.org/10.31763/ijrcs.v1i4.490.
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Artificial neural networks (ANN) can approximate signals and give interesting results in pattern recognition; some works use neural networks for control applications. However, biological neurons do not generate similar signals to the obtained by ANN. The spiking neurons are an interesting topic since they simulate the real behavior depicted by biological neurons. This paper employed a spiking neuron to compute a PID control, which is further applied to the Van de Vusse reaction. This reaction, as the inverse pendulum, is a benchmark used to work with systems that has inverse response producing the output to undershoot. One problem is how to code information that the neuron can interpret and decode the peak generated by the neuron to interpret the neuron's behavior. In this work, a spiking neuron is used to compute a PID control by coding in time the peaks generated by the neuron. The neuron has as synaptic weights the PID gains, and the peak observed in the axon is the coded control signal. The neuron adaptation tries to obtain the necessary weights to generate the peak instant necessary to control the chemical reaction. The simulation results show the possibility of using this kind of neuron for control issues and the possibility of using a spiking neural network to overcome the undershoot obtained due to the inverse response of the chemical reaction.
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Garashchuk, I. R., and D. I. Sinelshchikov. "Excitation of a Group of Two Hindmarsh – Rose Neurons with a Neuron-Generated Signal." Nelineinaya Dinamika 18, no. 4 (2022): 0. http://dx.doi.org/10.20537/nd220901.
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We study a model of three Hindmarsh – Rose neurons with directional electrical connections. We consider two fully-connected neurons that form a slave group which receives the signal from the master neuron via a directional coupling. We control the excitability of the neurons by setting the constant external currents. We study the possibility of excitation of the slave system in the stable resting state by the signal coming from the master neuron, to make it fire spikes/bursts tonically. We vary the coupling strength between the master and the slave systems as another control parameter. We calculate the borderlines of excitation by different types of signal in the control parameter space. We establish which of the resulting dynamical regimes are chaotic. We also demonstrate the possibility of excitation by a single burst or a spike in areas of control parameters, where the slave system is bistable. We calculate the borderlines of excitation by a single period of the excitatory signal.
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Kumar, Devesh, Michael Candlish, Vinod Periasamy, Nergiz Avcu, Christian Mayer, and Ulrich Boehm. "Specialized Subpopulations of Kisspeptin Neurons Communicate With GnRH Neurons in Female Mice." Endocrinology 156, no. 1 (January 1, 2015): 32–38. http://dx.doi.org/10.1210/en.2014-1671.
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Abstract The neuropeptide kisspeptin is a potent stimulator of GnRH neurons and has been implicated as a major regulator of the hypothalamus-pituitary-gonadal axis. There are mainly two anatomically segregated populations of neurons that express kisspeptin in the female hypothalamus: one in the anteroventral periventricular nucleus (AVPV) and the other in the arcuate nucleus (ARC). Distinct roles have been proposed for AVPV and ARC kisspeptin neurons during reproductive maturation and in mediating estrogen feedback on the hypothalamus-pituitary-gonadal axis in adults. Despite their pivotal role in the regulation of reproductive physiology, little is known about kisspeptin neuron connectivity. Although previous data suggest heterogeneity within the AVPV and ARC kisspeptin neuron populations, how many and which of these potential kisspeptin neuron subpopulations are actually communicating with GnRH neurons is not known. Here we used a combinatorial genetic transsynaptic tracing strategy to start to analyze the connectivity of individual kisspeptin neurons with the GnRH neuron population in female mice with a single-cell resolution. We find that only subsets of AVPV and ARC kisspeptin neurons are synaptically connected with GnRH neurons. We demonstrate that the majority of kisspeptin neurons within the AVPV and ARC does not communicate with GnRH neurons. Furthermore, we show that all kisspeptin neurons within the AVPV connected to GnRH neurons are estrogen sensitive and that most of these express tyrosine hydroxylase. Our data demonstrate functional specialization within the two kisspeptin neuron populations.
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Geisler, Caroline, Nicolas Brunel, and Xiao-Jing Wang. "Contributions of Intrinsic Membrane Dynamics to Fast Network Oscillations With Irregular Neuronal Discharges." Journal of Neurophysiology 94, no. 6 (December 2005): 4344–61. http://dx.doi.org/10.1152/jn.00510.2004.
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During fast oscillations in the local field potential (40–100 Hz gamma, 100–200 Hz sharp-wave ripples) single cortical neurons typically fire irregularly at rates that are much lower than the oscillation frequency. Recent computational studies have provided a mathematical description of such fast oscillations, using the leaky integrate-and-fire (LIF) neuron model. Here, we extend this theoretical framework to populations of more realistic Hodgkin–Huxley-type conductance-based neurons. In a noisy network of GABAergic neurons that are connected randomly and sparsely by chemical synapses, coherent oscillations emerge with a frequency that depends sensitively on the single cell's membrane dynamics. The population frequency can be predicted analytically from the synaptic time constants and the preferred phase of discharge during the oscillatory cycle of a single cell subjected to noisy sinusoidal input. The latter depends significantly on the single cell's membrane properties and can be understood in the context of the simplified exponential integrate-and-fire (EIF) neuron. We find that 200-Hz oscillations can be generated, provided the effective input conductance of single cells is large, so that the single neuron's phase shift is sufficiently small. In a two-population network of excitatory pyramidal cells and inhibitory neurons, recurrent excitation can either decrease or increase the population rhythmic frequency, depending on whether in a neuron the excitatory synaptic current follows or precedes the inhibitory synaptic current in an oscillatory cycle. Detailed single-cell properties have a substantial impact on population oscillations, even though rhythmicity does not originate from pacemaker neurons and is an emergent network phenomenon.
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Bott, C. J., C. G. Johnson, C. C. Yap, N. D. Dwyer, K. A. Litwa, and B. Winckler. "Nestin in immature embryonic neurons affects axon growth cone morphology and Semaphorin3a sensitivity." Molecular Biology of the Cell 30, no. 10 (May 2019): 1214–29. http://dx.doi.org/10.1091/mbc.e18-06-0361.
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Correct wiring in the neocortex requires that responses to an individual guidance cue vary among neurons in the same location, and within the same neuron over time. Nestin is an atypical intermediate filament expressed strongly in neural progenitors and is thus used widely as a progenitor marker. Here we show a subpopulation of embryonic cortical neurons that transiently express nestin in their axons. Nestin expression is thus not restricted to neural progenitors, but persists for 2–3 d at lower levels in newborn neurons. We found that nestin-expressing neurons have smaller growth cones, suggesting that nestin affects cytoskeletal dynamics. Nestin, unlike other intermediate filament subtypes, regulates cdk5 kinase by binding the cdk5 activator p35. Cdk5 activity is induced by the repulsive guidance cue Semaphorin3a (Sema3a), leading to axonal growth cone collapse in vitro. Therefore, we tested whether nestin-expressing neurons showed altered responses to Sema3a. We find that nestin-expressing newborn neurons are more sensitive to Sema3a in a roscovitine-sensitive manner, whereas nestin knockdown results in lowered sensitivity to Sema3a. We propose that nestin functions in immature neurons to modulate cdk5 downstream of the Sema3a response. Thus, the transient expression of nestin could allow temporal and/or spatial modulation of a neuron’s response to Sema3a, particularly during early axon guidance.
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Mulloney, Brian, and Wendy M. Hall. "Local and Intersegmental Interactions of Coordinating Neurons and Local Circuits in the Swimmeret System." Journal of Neurophysiology 98, no. 1 (July 2007): 405–13. http://dx.doi.org/10.1152/jn.00345.2007.
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During forward swimming, periodic movements of swimmerets on different segments of the crayfish abdomen progress from back to front with the same period. Information encoded as bursts of spikes by coordinating neurons in each segmental ganglion is necessary for this coherent organization. This information is conducted to targets in other ganglia. When an individual coordinating neuron is stimulated at different phases in the system's cycle of activity, the timing of motor output from other ganglia may be altered. In models of this coordinating circuit, we assumed that each coordinating neuron encodes information about the state of the local pattern-generating circuit in its home ganglion but is not part of that local circuit. We tested this assumption by stimulating individual coordinating neurons of two kinds—ASCE and DSC—at different phases under two conditions: with the target ganglion functional, and with the target ganglion silenced. Blocking a DSC neuron's target ganglion did not alter its negligible influence on the output from its home ganglion; the phase-response curves (PRC) remained flat. Blocking an ASCE neuron's target ganglion significantly affected its influence on the output from its home ganglion. We had predicted that ASCE's modest phase-dependent influence would disappear with the target silenced, but instead the amplitude of the PRCs increased significantly. Thus we have two different results: DSC neurons conformed to prediction based on the models’ assumptions, but ASCE neurons showed an unexpected property, one that is partially masked when the bidirectional flow of information between neighboring ganglia is operating normally.
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Zavitz, Elizabeth, and Nicholas S. C. Price. "Weighting neurons by selectivity produces near-optimal population codes." Journal of Neurophysiology 121, no. 5 (May 1, 2019): 1924–37. http://dx.doi.org/10.1152/jn.00504.2018.
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Perception is produced by “reading out” the representation of a sensory stimulus contained in the activity of a population of neurons. To examine experimentally how populations code information, a common approach is to decode a linearly weighted sum of the neurons’ spike counts. This approach is popular because of the biological plausibility of weighted, nonlinear integration. For neurons recorded in vivo, weights are highly variable when derived through optimization methods, but it is unclear how the variability affects decoding performance in practice. To address this, we recorded from neurons in the middle temporal area (MT) of anesthetized marmosets ( Callithrix jacchus) viewing stimuli comprising a sheet of dots that moved coherently in 1 of 12 different directions. We found that high peak response and direction selectivity both predicted that a neuron would be weighted more highly in an optimized decoding model. Although learned weights differed markedly from weights chosen according to a priori rules based on a neuron’s tuning profile, decoding performance was only marginally better for the learned weights. In the models with a priori rules, selectivity is the best predictor of weighting, and defining weights according to a neuron’s preferred direction and selectivity improves decoding performance to very near the maximum level possible, as defined by the learned weights. NEW & NOTEWORTHY We examined which aspects of a neuron’s tuning account for its contribution to sensory coding. Strongly direction-selective neurons are weighted most highly by optimal decoders trained to discriminate motion direction. Models with predefined decoding weights demonstrate that this weighting scheme causally improved direction representation by a neuronal population. Optimizing decoders (using a generalized linear model or Fisher’s linear discriminant) led to only marginally better performance than decoders based purely on a neuron’s preferred direction and selectivity.
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Shang, Xiao Jing. "The Identification of Neurons Research." Advanced Materials Research 756-759 (September 2013): 2813–18. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2813.
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In view of the present medical neurons characteristic cognition and human brain plan in the neurons of the limitation of recognition, this paper puts forward the neurons identification method. First the L - Measure software to neuron geometry feature extraction, and then to extract high dimensional feature through the principal component analysis dimension reduction processing. Combined classifier with pyramidal neurons, general Ken wild neurons, motor neuron, sensory neurons, double neurons, level 3 neurons and multistage neurons 7 kinds of neurons are classified. Experimental results prove that the probabilistic neural network, the BP neural network, fuzzy classifier composed of classifier recognition effect is superior to the arbitrary single classifier.
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SHIAU, LIEJUNE, and CARLO R. LAING. "PERIODICALLY FORCED PIECEWISE-LINEAR ADAPTIVE EXPONENTIAL INTEGRATE-AND-FIRE NEURON." International Journal of Bifurcation and Chaos 23, no. 10 (October 2013): 1350171. http://dx.doi.org/10.1142/s021812741350171x.
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Although variability is a ubiquitous characteristic of the nervous system, under appropriate conditions neurons can generate precisely timed action potentials. Thus considerable attention has been given to the study of a neuron's output in relation to its stimulus. In this study, we consider an increasingly popular spiking neuron model, the adaptive exponential integrate-and-fire neuron. For analytical tractability, we consider its piecewise-linear variant in order to understand the responses of such neurons to periodic stimuli. There exist regions in parameter space in which the neuron is mode locked to the periodic stimulus, and instabilities of the mode locked states lead to an Arnol'd tongue structure in parameter space. We analyze mode locked solutions and examine the bifurcations that define the boundaries of the tongue structures. The theoretical analysis is in excellent agreement with numerical simulations, and this study can be used to further understand the functional features related to responses of such a model neuron to biologically realistic inputs.
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Nagy, F., and P. Cardi. "A rhythmic modulatory gating system in the stomatogastric nervous system of Homarus gammarus. II. Modulatory control of the pyloric CPG." Journal of Neurophysiology 71, no. 6 (June 1, 1994): 2490–502. http://dx.doi.org/10.1152/jn.1994.71.6.2490.
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1. In the European rock lobster, Homarus gammarus, two bilaterally symmetrical pairs of commissural neurons, P and commissural pyloric (CP), evoke excitatory postsynaptic potentials in the neurons of the pyloric motor network. The present paper shows that the two commissural neurons also exert a modulatory control over the pyloric network. 2. The P and CP neurons were active during ongoing pyloric rhythms. Ongoing pyloric activity was terminated when the neurons were hyperpolarized to inhibit their firing. 3. When the pyloric network was quiescent, depolarizing either the P or CP neuron induced a robust pyloric rhythm. 4. We studied the actions of the P and CP neurons on individual pyloric neurons isolated in situ from network interactions by a photoinactivation techniques. The P neuron induced oscillatory properties in the pacemaker pyloric dilator (PD) neurons and the motor neuron, ventricular dilator (VD), whereas the CP neuron induced rhythmogenic properties in all the network neurons but VD. Together, the P-CP neurons modulated the entire pyloric network. The modulatory effects of the P-CP neurons did not outlast the duration of their discharge. 5. The P and CP neurons also controlled the firing frequency of all the pyloric neurons. They may, in addition, control phasing of the constrictor neurons discharges, but this effect was state-dependent and occurred only when the pyloric central pattern generator was functioning weakly. Their role in providing flexibility to the network operation appeared relatively limited. 6. We conclude that the P and CP neurons are good candidates for insuring long-term maintenance of pyloric network activity patterns.
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Zhou, Tao, Bainan Xu, Haiping Que, Qiuxia Lin, Shuanghong Lv, and And Liu. "Neurons derived from PC12 cells have the potential to develop synapses with primary neurons from rat cortex." Acta Neurobiologiae Experimentalis 66, no. 2 (June 30, 2006): 105–12. http://dx.doi.org/10.55782/ane-2006-1596.
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Neuron transplantation is considered to be a promising therapeutic method to replace functions lost due to central nervous system (CNS) damage. However, little is known about the extent to which implanted neuron-like cells can develop into mature neurons and acquire essential properties, and especially formation of synapses with host neurons. In this investigation we seeded PC12 cells labeled with GFP into primary cultured neurons isolated from rat cerebral cortex to build a co-culture system, and then induced the PC12 cells to differentiate into neuron-like cells with NGF. Seven days later, we observed the relationship between the PC12-derived neurons and primary neurons using FM1-43 imaging and immunoelectron microscopy, and found that GFP-labeled neurons could form typical synapses with host primary neurons. These observations showed that immigrant neurons differentiated from PC12 cells could develop into mature neurons and could form intercellular contacts with host neurons. Both the immigrant and host neurons could construct neuronal networks in vitro. The correspondence should be addressed to S. Liu, Email: liusj@nic.bmi.ac.cn
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Liu, Wenshu, J. Franklin Bailey, Visaka Limwongse, and Mark DeSantis. "Scanning Electron Microscopy of neuronal cell bodies isolated from the adult mammalian central nervous system." Proceedings, annual meeting, Electron Microscopy Society of America 48, no. 3 (August 12, 1990): 426–27. http://dx.doi.org/10.1017/s0424820100159679.
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Action potentials generated in a motor neuron reflect the summation of synaptic inputs it receives from other neurons. Those synapses occur at points of contiguity between the presynaptic boutons and the surface of the motor neuron. Evidence that the density of axosomalic boutons on motor neurons varies directly with the size of the motor neuronal soma is indirect. Counts of the number of boutons per unit area at the surface of the motor neuron’s cell body using scanning electron microscopy (SEM) would allow an independent, direct assessment of that inference. We describe here procedures for consistently isolating the somas of CNS neurons, specifically those associated with the adult rat’s trigeminal nerve, so that axosomatic boutons can be seen by SEM (Figures 1 and 2).Adult male and female rats were anesthetized and then perfused with saline followed by 4% paraformaldehyde. The brain stem was removed and sectioned at 200 um thickness on a vibratome. Sections containing the trigeminal motor and mesencephalic nuclei were pinned to Sylgard-lined dishes containing phosphate buffer (0.1 M, pH 7.2).
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Todo, Yuki, Zheng Tang, Hiroyoshi Todo, Junkai Ji, and Kazuya Yamashita. "Neurons with Multiplicative Interactions of Nonlinear Synapses." International Journal of Neural Systems 29, no. 08 (September 25, 2019): 1950012. http://dx.doi.org/10.1142/s0129065719500126.
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Neurons are the fundamental units of the brain and nervous system. Developing a good modeling of human neurons is very important not only to neurobiology but also to computer science and many other fields. The McCulloch and Pitts neuron model is the most widely used neuron model, but has long been criticized as being oversimplified in view of properties of real neuron and the computations they perform. On the other hand, it has become widely accepted that dendrites play a key role in the overall computation performed by a neuron. However, the modeling of the dendritic computations and the assignment of the right synapses to the right dendrite remain open problems in the field. Here, we propose a novel dendritic neural model (DNM) that mimics the essence of known nonlinear interaction among inputs to the dendrites. In the model, each input is connected to branches through a distance-dependent nonlinear synapse, and each branch performs a simple multiplication on the inputs. The soma then sums the weighted products from all branches and produces the neuron’s output signal. We show that the rich nonlinear dendritic response and the powerful nonlinear neural computational capability, as well as many known neurobiological phenomena of neurons and dendrites, may be understood and explained by the DNM. Furthermore, we show that the model is capable of learning and developing an internal structure, such as the location of synapses in the dendritic branch and the type of synapses, that is appropriate for a particular task — for example, the linearly nonseparable problem, a real-world benchmark problem — Glass classification and the directional selectivity problem.
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Tamarkin, D. A., and R. B. Levine. "Synaptic interactions between a muscle-associated proprioceptor and body wall muscle motor neurons in larval and Adult manduca sexta." Journal of Neurophysiology 76, no. 3 (September 1, 1996): 1597–610. http://dx.doi.org/10.1152/jn.1996.76.3.1597.
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1. Synaptic remodeling of a proprioceptive circuit during metamorphosis of the insect, Manduca sexta, is described. The stretch receptor organ is a muscle-associated proprioceptor that is innervated by a single sensory neuron. It inserts dorsolaterally in the abdomen in parallel with the intersegmental muscles of each abdominal segment. The synaptic input from the stretch receptor sensory neuron to select abdominal internal (intersegmental) and external muscle motor neurons was characterized in both the larva and adult. 2. In the larva, the sensory neuron provides excitatory synaptic input to motor neurons that innervate muscles ipsilateral to the stretch receptor organ in the body wall; the strongest excitatory synaptic input is to motor neurons that innervate targets in close proximity to the stretch receptor organ. The sensory neuron also provides excitatory synaptic input to motor neurons that innervate contralateral, dorsal targets. However, it inhibits, apparently through a polysynaptic pathway, motor neurons innervating contralateral, lateral, and ventral targets. 3. The synaptic input to intersegmental muscle motor neurons from the stretch receptor sensory neuron changes during metamorphosis. In contrast to the larva, all motor neurons recorded in the adult (both ipsilateral and contralateral) were excited by the sensory neuron. As in the larva, the adult sensory neuron provides the strongest excitatory synaptic input to motor neurons innervating targets in close proximity to the stretch receptor organ. 4. The proprioceptive input to the body wall muscle motor neurons was evaluated to determine whether the pathway is monosynaptic, as has been described in other systems. Spike-triggered signal averaging and synaptic latency measurements suggested that the strongest excitatory synaptic input to motor neurons involves a monosynaptic pathway.
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Cheng, Lifang, and Hongjun Cao. "Synchronization Dynamics of Two Heterogeneous Chaotic Rulkov Neurons with Electrical Synapses." International Journal of Bifurcation and Chaos 27, no. 02 (February 2017): 1730009. http://dx.doi.org/10.1142/s0218127417300099.
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Two heterogeneous chaotic Rulkov neurons with electrical synapses are investigated in this paper. First, we study the ability of the second neuron to modify the dynamics of the first neuron. It is shown that when the parameters of the first neuron are located at the vicinity of the Neimark–Sacker bifurcation curves the first firing neuron can be controlled into the quiescent state when coupled with the second neuron. While the parameters of the first neuron are near the flip bifurcation curves the first firing neuron cannot be suppressed. Second, we discuss burst synchronization for two bursting neurons and two tonic spiking neurons. It is shown that two heterogeneous chaotic Rulkov neurons with tonic spiking firing cannot reach anti-phase synchronization under the inhibitory coupling, which is different from the property of nonchaotic Rulkov neurons. Finally, we show that for two bursting neurons if the coupling is strong enough then burst synchronization can be converted into spike synchronization. However, complete synchronization cannot be achieved for any strong coupling.
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Miranda-Domínguez, Óscar, and Theoden I. Netoff. "Parameterized phase response curves for characterizing neuronal behaviors under transient conditions." Journal of Neurophysiology 109, no. 9 (May 1, 2013): 2306–16. http://dx.doi.org/10.1152/jn.00942.2012.
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Phase response curves (PRCs) are a simple model of how a neuron's spike time is affected by synaptic inputs. PRCs are useful in predicting how networks of neurons behave when connected. One challenge in estimating a neuron's PRCs experimentally is that many neurons do not have stationary firing rates. In this article we introduce a new method to estimate PRCs as a function of firing rate of the neuron. We call the resulting model a parameterized PRC (pPRC). Experimentally, we perturb the neuron applying a current with two parts: 1) a current held constant between spikes but changed at the onset of a spike, used to make the neuron fire at different rates, and 2) a pulse to emulate a synaptic input. A model of the applied constant current and the history is made to predict the interspike interval (ISI). A second model is then made to fit the modulation of the spike time from the expected ISI by the pulsatile stimulus. A polynomial with two independent variables, the stimulus phase and the expected ISI, is used to model the pPRC. The pPRC is validated in a computational model and applied to pyramidal neurons from the CA1 region of the hippocampal slices from rat. The pPRC can be used to model the effect of changing firing rates on network synchrony. It can also be used to characterize the effects of neuromodulators and genetic mutations (among other manipulations) on network synchrony. It can also easily be extended to account for more variables.
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Weaver, Adam L., and Scott L. Hooper. "Follower Neurons in Lobster (Panulirus interruptus) Pyloric Network Regulate Pacemaker Period in Complementary Ways." Journal of Neurophysiology 89, no. 3 (March 1, 2003): 1327–38. http://dx.doi.org/10.1152/jn.00704.2002.
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Distributed neural networks (ones characterized by high levels of interconnectivity among network neurons) are not well understood. Increased insight into these systems can be obtained by perturbing network activity so as to study the functions of specific neurons not only in the network's “baseline” activity but across a range of network activities. We applied this technique to study cycle period control in the rhythmic pyloric network of the lobster, Panulirus interruptus. Pyloric rhythmicity is driven by an endogenous oscillator, the Anterior Burster (AB) neuron. Two network neurons feed back onto the pacemaker, the Lateral Pyloric (LP) neuron by inhibition and the Ventricular Dilator (VD) neuron by electrical coupling. LP and VD neuron effects on pyloric cycle period can be studied across a range of periods by altering period by injecting current into the AB neuron and functionally removing (by hyperpolarization) the LP and VD neurons from the network at each period. Within a range of pacemaker periods, the LP and VD neurons regulate period in complementary ways. LP neuron removal speeds the network and VD neuron removal slows it. Outside this range, network activity is disrupted because the LP neuron cannot follow slow periods, and the VD neuron cannot follow fast periods. These neurons thus also limit, in complementary ways, normal pyloric activity to a certain period range. These data show that follower neurons in pacemaker networks can play central roles in controlling pacemaker period and suggest that in some cases specific functions can be assigned to individual network neurons.
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Green, Adam. "Mirror Neurons, Simulation, and Goldman." History & Philosophy of Psychology 11, no. 2 (2009): 1–11. http://dx.doi.org/10.53841/bpshpp.2009.11.2.1.
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Mirror neurons have congruent sensory and motor functions, if not other endogenous functions. Shortly after the discovery of these neurons, Alvin Goldman argued that mirror neurons are simulators, and he has used the mirror neuron literature to support a simulation theory for how we understand the minds of other people. This use of the mirror neuron literature, however, is premature at best and confused at worst because even if it were established that mirror neurons were simulators, that would not necessarily vindicate the simulation theory of mindreading and the simulation interpretation of mirror neuron activity itself overreaches the evidence.
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Christensen, Thomas A., and John G. Hildebrand. "Coincident Stimulation With Pheromone Components Improves Temporal Pattern Resolution in Central Olfactory Neurons." Journal of Neurophysiology 77, no. 2 (February 1, 1997): 775–81. http://dx.doi.org/10.1152/jn.1997.77.2.775.
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Christensen, Thomas A. and John G. Hildebrand. Coincident stimulation with pheromone components improves temporal pattern resolution in central olfactory neurons. J. Neurophysiol. 77: 775–781, 1997. Male moths must detect and resolve temporal discontinuities in the sex pheromonal odor signal emitted by a conspecific female moth to orient to and locate the odor source. We asked how sensory information about two key components of the pheromone influences the ability of certain sexually dimorphic projection (output) neurons in the primary olfactory center of the male moth's brain to encode the frequency and duration of discrete pulses of pheromone blends. Most of the male-specific projection neurons examined gave mixed postsynaptic responses, consisting of an early suppressive phase followed by activation of firing, to stimulation of the ipsilateral antenna with a blend of the two behaviorally essential pheromone components. Of 39 neurons tested, 33 were excited by the principal (most abundant) pheromone component but inhibited by another, less abundant but nevertheless essential component of the blend. We tested the ability of each neuron to encode intermittent pheromonal stimuli by delivering trains of 50-ms pulses of the two-component blend at progressively higher rates from 1 to 10 per second. There was a strong correlation between 1) the amplitude of the early inhibitory postsynaptic potential evoked by the second pheromone component and 2) the maximal rate of odor pulses that neuron could resolve ( r = 0.92). Projection neurons receiving stronger inhibitory input encoded the temporal pattern of the stimulus with higher fidelity. With the principal, excitatory component of the pheromone alone as the stimulus, the dynamic range for encoding stimulus intermittency was reduced in nearly 60% of the neurons tested. The greatest reductions were observed in those neurons that could be shown to receive the strongest inhibitory input from the second behaviorally essential component of the blend. We also tested the ability of these neurons to encode stimulus duration. Again there was a strong correlation between the strength of the inhibitory input to a neuron mediated by the second pheromone component and that neuron's ability to encode stimulus duration. Neurons that were strongly inhibited by the second component could accurately encode pulses of the blend from 50 to 500 ms in duration ( r = 0.94), but that ability was reduced in neurons receiving little or no inhibitory input ( r = 0.23). This study confirms that certain olfactory projection neurons respond optimally to a particular odor blend rather than to the individual components of the blend. The key components activate opposing synaptic inputs that enable this subset of central neurons to copy the duration and frequency of intermittent odor pulses that are a fundamental feature of airborne olfactory stimuli.
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Seki, Soju, Yoshihiro Kitaoka, Sou Kawata, Akira Nishiura, Toshihiro Uchihashi, Shin-ichiro Hiraoka, Yusuke Yokota, Emiko Tanaka Isomura, Mikihiko Kogo, and Susumu Tanaka. "Characteristics of Sensory Neuron Dysfunction in Amyotrophic Lateral Sclerosis (ALS): Potential for ALS Therapy." Biomedicines 11, no. 11 (November 3, 2023): 2967. http://dx.doi.org/10.3390/biomedicines11112967.
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Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder characterised by the progressive degeneration of motor neurons, resulting in muscle weakness, paralysis, and, ultimately, death. Presently, no effective treatment for ALS has been established. Although motor neuron dysfunction is a hallmark of ALS, emerging evidence suggests that sensory neurons are also involved in the disease. In clinical research, 30% of patients with ALS had sensory symptoms and abnormal sensory nerve conduction studies in the lower extremities. Peroneal nerve biopsies show histological abnormalities in 90% of the patients. Preclinical research has reported several genetic abnormalities in the sensory neurons of animal models of ALS, as well as in motor neurons. Furthermore, the aggregation of misfolded proteins like TAR DNA-binding protein 43 has been reported in sensory neurons. This review aims to provide a comprehensive description of ALS-related sensory neuron dysfunction, focusing on its clinical changes and underlying mechanisms. Sensory neuron abnormalities in ALS are not limited to somatosensory issues; proprioceptive sensory neurons, such as MesV and DRG neurons, have been reported to form networks with motor neurons and may be involved in motor control. Despite receiving limited attention, sensory neuron abnormalities in ALS hold potential for new therapies targeting proprioceptive sensory neurons.
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Hu, Xiaolin, and Zhigang Zeng. "Bridging the Functional and Wiring Properties of V1 Neurons Through Sparse Coding." Neural Computation 34, no. 1 (January 1, 2022): 104–37. http://dx.doi.org/10.1162/neco_a_01453.
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Abstract The functional properties of neurons in the primary visual cortex (V1) are thought to be closely related to the structural properties of this network, but the specific relationships remain unclear. Previous theoretical studies have suggested that sparse coding, an energy-efficient coding method, might underlie the orientation selectivity of V1 neurons. We thus aimed to delineate how the neurons are wired to produce this feature. We constructed a model and endowed it with a simple Hebbian learning rule to encode images of natural scenes. The excitatory neurons fired sparsely in response to images and developed strong orientation selectivity. After learning, the connectivity between excitatory neuron pairs, inhibitory neuron pairs, and excitatory-inhibitory neuron pairs depended on firing pattern and receptive field similarity between the neurons. The receptive fields (RFs) of excitatory neurons and inhibitory neurons were well predicted by the RFs of presynaptic excitatory neurons and inhibitory neurons, respectively. The excitatory neurons formed a small-world network, in which certain local connection patterns were significantly overrepresented. Bidirectionally manipulating the firing rates of inhibitory neurons caused linear transformations of the firing rates of excitatory neurons, and vice versa. These wiring properties and modulatory effects were congruent with a wide variety of data measured in V1, suggesting that the sparse coding principle might underlie both the functional and wiring properties of V1 neurons.
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Allman, Brian L., and M. Alex Meredith. "Multisensory Processing in “Unimodal” Neurons: Cross-Modal Subthreshold Auditory Effects in Cat Extrastriate Visual Cortex." Journal of Neurophysiology 98, no. 1 (July 2007): 545–49. http://dx.doi.org/10.1152/jn.00173.2007.
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Historically, the study of multisensory processing has examined the function of the definitive neuron type, the bimodal neuron. These neurons are excited by inputs from more than one sensory modality, and when multisensory stimuli are present, they can integrate their responses in a predictable manner. However, recent studies have revealed that multisensory processing in the cortex is not restricted to bimodal neurons. The present investigation sought to examine the potential for multisensory processing in nonbimodal (unimodal) neurons in the retinotopically organized posterolateral lateral suprasylvian (PLLS) area of the cat. Standard extracellular recordings were used to measure responses of all neurons encountered to both separate- and combined-modality stimulation. Whereas bimodal neurons behaved as predicted, the surprising result was that 16% of unimodal visual neurons encountered were significantly facilitated by auditory stimuli. Because these unimodal visual neurons did not respond to an auditory stimulus presented alone but had their visual responses modulated by concurrent auditory stimulation, they represent a new form of multisensory neuron: the subthreshold multisensory neuron. These data also demonstrate that bimodal neurons can no longer be regarded as the exclusive basis for multisensory processing.
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Xing, Hong, Jennifer Ling, Meng Chen, and Jianguo G. Gu. "Chemical and Cold Sensitivity of Two Distinct Populations of TRPM8-Expressing Somatosensory Neurons." Journal of Neurophysiology 95, no. 2 (February 2006): 1221–30. http://dx.doi.org/10.1152/jn.01035.2005.
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The cold- and menthol-sensing TRPM8 receptor has been proposed to have both nonnociceptive and nociceptive functions. However, one puzzle is how this single type of receptor may be used by somatosensory neurons to code for two distinct sensory modalities. Using acutely dissociated rat dorsal root ganglion (DRG) neurons without culture, we show that TRPM8 receptors are expressed on two distinct classes of somatosensory neurons. One class is sensitive to menthol and features nonnociceptive neuron properties, including capsaicin-insensitive, ATP-insensitive, transient acid response, and expression of TTX-sensitive sodium channels only. This class is termed the menthol-sensitive/capsaicin-insensitive neuron class (MS/CIS). The other class is also sensitive to menthol but has characteristics of nociceptive neurons including capsaicin-sensitive, ATP-sensitive, prolonged acid response, and expression of both TTX-sensitive and TTX-resistant sodium channels. This class is termed the menthol-sensitive/capsaicin-sensitive neuron class (MS/CS). The presence of these two neuron classes in acutely dissociated DRG neurons support the idea that TRPM8 receptors can have both nonnociceptive and nociceptive functions. While both neuron classes respond to menthol and cold, the overall responses induced by menthol and cold are significantly larger in MS/CIS than in MS/CS neurons. Furthermore, low concentrations of menthol produce strong selection of the MS/CIS neuron population over the MS/CS neuron population. On the other hand, the population selection becomes weaker with higher concentrations of menthol. TRPM8 current density shows significant higher in MS/CIS neurons than in MS/CS neurons, suggesting different expression levels of TRPM8 receptors between the two neuron populations, and this difference may provide a mean of selective activation of MS/CIS neurons at low stimulation intensity.
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Roy, Jefferson E., and Kathleen E. Cullen. "Brain Stem Pursuit Pathways: Dissociating Visual, Vestibular, and Proprioceptive Inputs During Combined Eye-Head Gaze Tracking." Journal of Neurophysiology 90, no. 1 (July 2003): 271–90. http://dx.doi.org/10.1152/jn.01074.2002.
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Eye-head (EH) neurons within the medial vestibular nuclei are thought to be the primary input to the extraocular motoneurons during smooth pursuit: they receive direct projections from the cerebellar flocculus/ventral paraflocculus, and in turn, project to the abducens motor nucleus. Here, we recorded from EH neurons during head-restrained smooth pursuit and head-unrestrained combined eye-head pursuit (gaze pursuit). During head-restrained smooth pursuit of sinusoidal and step-ramp target motion, each neuron's response was well described by a simple model that included resting discharge (bias), eye position, and velocity terms. Moreover, eye acceleration, as well as eye position, velocity, and acceleration error (error = target movement – eye movement) signals played no role in shaping neuronal discharges. During head-unrestrained gaze pursuit, EH neuron responses reflected the summation of their head-movement sensitivity during passive whole-body rotation in the dark and gaze-movement sensitivity during smooth pursuit. Indeed, EH neuron responses were well predicted by their head- and gaze-movement sensitivity during these two paradigms across conditions (e.g., combined eye-head gaze pursuit, smooth pursuit, whole-body rotation in the dark, whole-body rotation while viewing a target moving with the head (i.e., cancellation), and passive rotation of the head-on-body). Thus our results imply that vestibular inputs, but not the activation of neck proprioceptors, influence EH neuron responses during head-on-body movements. This latter proposal was confirmed by demonstrating a complete absence of modulation in the same neurons during passive rotation of the monkey's body beneath its neck. Taken together our results show that during gaze pursuit EH neurons carry vestibular- as well as gaze-related information to extraocular motoneurons. We propose that this vestibular-related modulation is offset by inputs from other premotor inputs, and that the responses of vestibuloocular reflex interneurons (i.e., position-vestibular-pause neurons) are consistent with such a proposal.
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Course, Meredith M., and Xinnan Wang. "Transporting mitochondria in neurons." F1000Research 5 (July 18, 2016): 1735. http://dx.doi.org/10.12688/f1000research.7864.1.
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Neurons demand vast and vacillating supplies of energy. As the key contributors of this energy, as well as primary pools of calcium and signaling molecules, mitochondria must be where the neuron needs them, when the neuron needs them. The unique architecture and length of neurons, however, make them a complex system for mitochondria to navigate. To add to this difficulty, mitochondria are synthesized mainly in the soma, but must be transported as far as the distant terminals of the neuron. Similarly, damaged mitochondria—which can cause oxidative stress to the neuron—must fuse with healthy mitochondria to repair the damage, return all the way back to the soma for disposal, or be eliminated at the terminals. Increasing evidence suggests that the improper distribution of mitochondria in neurons can lead to neurodegenerative and neuropsychiatric disorders. Here, we will discuss the machinery and regulatory systems used to properly distribute mitochondria in neurons, and how this knowledge has been leveraged to better understand neurological dysfunction.
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Solís-Perales, Gualberto, and Jairo Sánchez Estrada. "A Model for Evolutionary Structural Plasticity and Synchronization of a Network of Neurons." Computational and Mathematical Methods in Medicine 2021 (June 16, 2021): 1–12. http://dx.doi.org/10.1155/2021/9956319.
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A model of time-dependent structural plasticity for the synchronization of neuron networks is presented. It is known that synchronized oscillations reproduce structured communities, and this synchronization is transient since it can be enhanced or suppressed, and the proposed model reproduces this characteristic. The evolutionary behavior of the couplings is comparable to those of a network of biological neurons. In the structural network, the physical connections of axons and dendrites between neurons are modeled, and the evolution in the connections depends on the neurons’ potential. Moreover, it is shown that the coupling force’s function behaves as an adaptive controller that leads the neurons in the network to synchronization. The change in the node’s degree shows that the network exhibits time-dependent structural plasticity, achieved through the evolutionary or adaptive change of the coupling force between the nodes. The coupling force function is based on the computed magnitude of the membrane potential deviations with its neighbors and a threshold that determines the neuron’s connections. These rule the functional network structure along the time.
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Pfeiffer, Keram, Izabela Panek, Ulli Höger, Andrew S. French, and Päivi H. Torkkeli. "Random Stimulation of Spider Mechanosensory Neurons Reveals Long-Lasting Excitation by GABA and Muscimol." Journal of Neurophysiology 101, no. 1 (January 2009): 54–66. http://dx.doi.org/10.1152/jn.91020.2008.
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γ-Aminobutyric acid type A (GABAA) receptor activation inhibits many primary afferent neurons by depolarization and increased membrane conductance. Deterministic (step and sinusoidal) functions are commonly used as stimuli to test such inhibition. We found that when the VS-3 mechanosensory neurons innervating the spider lyriform slit-sense organ were stimulated by randomly varying white-noise mechanical or electrical signals, their responses to GABAA receptor agonists were more complex than the inhibition observed during deterministic stimulation. Instead, there was rapid excitation, then brief inhibition, followed by long-lasting excitation. During the final excitatory phase, VS-3 neuron sensitivity to high-frequency signals increased selectively and their linear information capacity also increased. Using experimental and simulation approaches we found that the excitatory effect could also be achieved by depolarizing the neurons without GABA application and that excitation could override the inhibitory effect produced by increased membrane conductance (shunting). When the VS-3 neurons were exposed to bumetanide, an antagonist of the Cl− transporter NKCC1, the GABA-induced depolarization decreased without any change in firing rate, suggesting that the effects of GABA can be maintained for a long time without additional Cl− influx. Our results show that the VS-3 neuron's response to GABA depends profoundly on the type of signals the neuron is conveying while the transmitter binds to its receptors.
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Fuhrmann, Galit, Henry Markram, and Misha Tsodyks. "Spike Frequency Adaptation and Neocortical Rhythms." Journal of Neurophysiology 88, no. 2 (August 1, 2002): 761–70. http://dx.doi.org/10.1152/jn.2002.88.2.761.
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Spike-frequency adaptation in neocortical pyramidal neurons was examined using the whole cell patch-clamp technique and a phenomenological model of neuronal activity. Noisy current was injected to reproduce the irregular firing typically observed under in vivo conditions. The response was quantified by computing the poststimulus histogram (PSTH). To simulate the spiking activity of a pyramidal neuron, we considered an integrate-and-fire model to which an adaptation current was added. A simplified model for the mean firing rate of an adapting neuron under noisy conditions is also presented. The mean firing rate model provides a good fit to both experimental and simulation PSTHs and may therefore be used to study the response characteristics of adapting neurons to various input currents. The models enable identification of the relevant parameters of adaptation that determine the shape of the PSTH and allow the computation of the response to any change in injected current. The results suggest that spike frequency adaptation determines a preferred frequency of stimulation for which the phase delay of a neuron's activity relative to an oscillatory input is zero. Simulations show that the preferred frequency of single neurons dictates the frequency of emergent population rhythms in large networks of adapting neurons. Adaptation could therefore be one of the crucial factors in setting the frequency of population rhythms in the neocortex.
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Heil, Peter, and Dexter R. F. Irvine. "Functional Specialization in Auditory Cortex: Responses to Frequency-Modulated Stimuli in the Cat's Posterior Auditory Field." Journal of Neurophysiology 79, no. 6 (June 1, 1998): 3041–59. http://dx.doi.org/10.1152/jn.1998.79.6.3041.
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Heil, Peter and Dexter R. F. Irvine. Functional specialization in auditory cortex: responses to frequency-modulated stimuli in the cat's posterior auditory field. J. Neurophysiol. 79: 3041–3059, 1998. The mammalian auditory cortex contains multiple fields but their functional role is poorly understood. Here we examine the responses of single neurons in the posterior auditory field (P) of barbiturate- and ketamine-anesthetized cats to frequency-modulated (FM) sweeps. FM sweeps traversed the excitatory response area of the neuron under study, and FM direction and the linear rate of change of frequency (RCF) were varied systematically. In some neurons, sweeps of different sound pressure levels (SPLs) also were tested. The response magnitude (number of spikes corrected for spontaneous activity) of nearly all field P neurons varied with RCF. RCF response functions displayed a variety of shapes, but most functions were of low-pass characteristic or peaked at rather low RCFs (<100 kHz/s). Neurons with strong responses to high RCFs (high-pass or nonselective RCF response function characteristics) all displayed spike count—SPL functions to tone burst onsets that were monotonic or weakly nonmonotonic. RCF response functions and best RCFs often changed with SPL. For most neurons, FM directional sensitivity, quantified by a directional sensitivity (DS) index, also varied with RCF and SPL, but the mean and width of the distribution of DS indices across all neurons was independent of RCF. Analysis of response timing revealed that the phasic response of a neuron is triggered when the instantaneous frequency of the sweep reaches a particular value, the effective F i. For a given neuron, values of effective F i were independent of RCF, but depended on FM direction and SPL and were associated closely with the boundaries of the neuron's frequency versus amplitude response area. The standard deviation (SD) of the latency of the first spike of the response decreased with RCF. When SD was expressed relative to the rate of change of stimulus frequency, the resulting index of frequency jitter increased with RCF and did so rather uniformly in all neurons and largely independent of SPL. These properties suggest that many FM parameters are represented by, and may be encoded in, orderly temporal patterns across different neurons in addition to the strength of responses. When compared with neurons in primary and anterior auditory fields, field P neurons respond better to relatively slow FMs. Together with previous studies of responses to modulations of amplitude, such as tone onsets, our findings suggest more generally that field P may be best suited for processing signals that vary relatively slowly over time.
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Yip, Siew Hoong, Ulrich Boehm, Allan E. Herbison, and Rebecca E. Campbell. "Conditional Viral Tract Tracing Delineates the Projections of the Distinct Kisspeptin Neuron Populations to Gonadotropin-Releasing Hormone (GnRH) Neurons in the Mouse." Endocrinology 156, no. 7 (April 9, 2015): 2582–94. http://dx.doi.org/10.1210/en.2015-1131.
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Kisspeptin neurons play an essential role in the regulation of fertility through direct regulation of the GnRH neurons. However, the relative contributions of the two functionally distinct kisspeptin neuron subpopulations to this critical regulation are not fully understood. Here we analyzed the specific projection patterns of kisspeptin neurons originating from either the rostral periventricular nucleus of the third ventricle (RP3V) or the arcuate nucleus (ARN) using a cell-specific, viral-mediated tract-tracing approach. We stereotaxically injected a Cre-dependent recombinant adenovirus encoding farnesylated enhanced green fluorescent protein into the ARN or RP3V of adult male and female mice expressing Cre recombinase in kisspeptin neurons. Fibers from ARN kisspeptin neurons projected widely; however, we did not find any evidence for direct contact with GnRH neuron somata or proximal dendrites in either sex. In contrast, we identified RP3V kisspeptin fibers in close contact with GnRH neuron somata and dendrites in both sexes. Fibers originating from both the RP3V and ARN were observed in close contact with distal GnRH neuron processes in the ARN and in the lateral and internal aspects of the median eminence. Furthermore, GnRH nerve terminals were found in close contact with the proximal dendrites of ARN kisspeptin neurons in the ARN, and ARN kisspeptin fibers were found contacting RP3V kisspeptin neurons in both sexes. Together these data delineate selective zones of kisspeptin neuron inputs to GnRH neurons and demonstrate complex interconnections between the distinct kisspeptin populations and GnRH neurons.
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Tal, Doron, and Eric L. Schwartz. "Computing with the Leaky Integrate-and-Fire Neuron: Logarithmic Computation and Multiplication." Neural Computation 9, no. 2 (February 1, 1997): 305–18. http://dx.doi.org/10.1162/neco.1997.9.2.305.
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The leaky integrate-and-fire (LIF) model of neuronal spiking (Stein 1967) provides an analytically tractable formalism of neuronal firing rate in terms of a neuron's membrane time constant, threshold, and refractory period. LIF neurons have mainly been used to model physiologically realistic spike trains, but little application of the LIF model appears to have been made in explicitly computational contexts. In this article, we show that the transfer function of a LIF neuron provides, over a wide parameter range, a compressive nonlinearity sufficiently close to that of the logarithm so that LIF neurons can be used to multiply neural signals by mere addition of their outputs yielding the logarithm of the product. A simulation of the LIF multiplier shows that under a wide choice of parameters, a LIF neuron can log-multiply its inputs to within a 5% relative error.
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Jung, R., T. Kiemel, and A. H. Cohen. "Dynamic behavior of a neural network model of locomotor control in the lamprey." Journal of Neurophysiology 75, no. 3 (March 1, 1996): 1074–86. http://dx.doi.org/10.1152/jn.1996.75.3.1074.
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1. Experimental studies have shown that a central pattern generator in the spinal cord of the lamprey can produce the basic rhythm for locomotion. This pattern generator interacts with the reticular neurons forming a spinoreticulospinal loop. To better understand and investigate the mechanisms for locomotor pattern generation in the lamprey, we examine the dynamic behavior of a simplified neural network model representing a unit spinal pattern generator (uPG) and its interaction with the reticular system. We use the techniques of bifurcation analysis and specifically examine the effects on the dynamic behavior of the system of 1) changing tonic drives to the different neurons of the uPG; 2) altering inhibitory and excitatory interconnection strengths among the uPG neurons; and 3) feedforward-feedback interactions between the uPG and the reticular neurons. 2. The model analyzed is a qualitative left-right symmetric network based on proposed functional architecture with one class of phasic reticular neurons and three classes of uPG neurons: excitatory (E), lateral (L), and crossed (C) interneurons. In the model each class is represented by one left and one right neuron. Each neuron has basic passive properties akin to biophysical neurons and receives tonic synaptic drive and weighted synaptic input from other connecting neurons. The neuron's output as a function of voltage is given by a nonlinear function with a strict threshold and saturation. 3. With an appropriate set of parameter values, the voltage of each neuron can oscillate periodically with phase relationships among the different neurons that are qualitatively similar to those observed experimentally. The uPG alone can also oscillate, as observed experimentally in isolated lamprey spinal cords. Varying the parameters can, however, profoundly change the state of the system via different kinds of bifurcations. Change in a single parameter can move the system from nonoscillatory to oscillatory states via different kinds of bifurcations. For some parameter values the system can also exhibit multistable behavior (e.g., an oscillatory state and a nonoscillatory state). The analysis also shows us how the amplitudes of the oscillations vary and the periods of limit cycles change as different bifurcation points are approached. 4. Altering tonic drive to just one class of uPG neurons (without altering the interconnections) can change the state of the system by altering the stability of fixed points, converting fixed points to oscillations, single oscillations to two stable oscillations, etc. Two-parameter bifurcation diagrams show the critical regions in which a balance between the tonic drives is necessary to maintain stable oscillations. A minimum tonic drive is necessary to obtain stable oscillatory output. With appropriate changes in the tonic drives to the L and C neurons, stable oscillatory output can be obtained even after eliminating the E neurons. Indeed, the presence of active E neurons in the biological system does not prove they play a functional role in the system, because tonic drive from other sources can substitute for them. On the other hand, very high excitation of any one class of neurons can terminate oscillations. Appropriate balance of tonic drives to different neuron classes can help sustain stable oscillations for larger tonic drives. Published experimental results concerning changes in amplitude and swimming frequency with increased tonic drives are mimicked by the model's responses to increased tonic drive. 5. Interconnectivity among the neurons plays a crucial role. The analysis indicates that the C and L classes of neurons are essential components of the model network. Sufficient inhibition from the L to C neurons as well as mutual inhibition between the left and right halves is necessary to obtain stable oscillatory output. When the E neurons are present in the model network, they must receive appropriate tonic drive and provide appropriate excitation
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Naudin, Loïs. "Biological emergent properties in non-spiking neural networks." AIMS Mathematics 7, no. 10 (2022): 19415–39. http://dx.doi.org/10.3934/math.20221066.
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<abstract><p>A central goal of neuroscience is to understand the way nervous systems work to produce behavior. Experimental measurements in freely moving animals (<italic>e.g.</italic> in the <italic>C. elegans</italic> worm) suggest that ON- and OFF-states in non-spiking nervous tissues underlie many physiological behaviors. Such states are defined by the collective activity of non-spiking neurons with correlated up- and down-states of their membrane potentials. How these network states emerge from the intrinsic neuron dynamics and their couplings remains unclear. In this paper, we develop a rigorous mathematical framework for better understanding their emergence. To that end, we use a recent simple phenomenological model capable of reproducing the experimental behavior of non-spiking neurons. The analysis of the stationary points and the bifurcation dynamics of this model are performed. Then, we give mathematical conditions to monitor the impact of network activity on intrinsic neuron properties. From then on, we highlight that ON- and OFF-states in non-spiking coupled neurons could be a consequence of bistable synaptic inputs, and not of intrinsic neuron dynamics. In other words, the apparent up- and down-states in the neuron's bimodal voltage distribution do not necessarily result from an intrinsic bistability of the cell. Rather, these states could be driven by bistable presynaptic neurons, ubiquitous in non-spiking nervous tissues, which dictate their behaviors to their postsynaptic ones.</p></abstract>
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Shosaku, A. "Cross-correlation analysis of a recurrent inhibitory circuit in the rat thalamus." Journal of Neurophysiology 55, no. 5 (May 1, 1986): 1030–43. http://dx.doi.org/10.1152/jn.1986.55.5.1030.
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Spontaneous activities of vibrissa-responding neurons in the rat ventrobasal complex (VB) and somatosensory part of the thalamic reticular nucleus (S-TR) were simultaneously recorded and subjected to cross-correlation analysis to investigate the functional organization of recurrent inhibitory action of the S-TR on VB neurons. Excitatory and/or inhibitory interactions were found between approximately 75% (25/34) of the pairs of S-TR and VB neurons with receptive fields (RFs) on the same vibrissa. In contrast, there was no significant interaction between 54 pairs of neurons having RFs on different vibrissae. Among the pairs of neurons with RFs on the same vibrissa, there were four types of correlations, which indicate the following connections: monosynaptic excitation from a VB to an S-TR neuron (7 pairs), monosynaptic inhibition from an S-TR to a VB neuron (10 pairs), reciprocal connection combining the above two types (7 pairs), and common excitation in addition to inhibition from an S-TR to a VB neuron (1 pair). Examples of divergence and convergence of connections between S-TR and VB neurons were demonstrated by testing one S-TR (VB) neuron with more than one VB (S-TR) neuron. Vibrissa-suppressed VB cells, which had exclusively inhibitory RFs, were included in eight pairs of the above samples. These VB cells were more likely to receive inhibitory inputs from S-TR neurons than other VB neurons. Cells with RFs on multiple vibrissae were included in the other 10 pairs. These multiple-vibrissa cells had no interaction with single-vibrissa cells but did with multiple-vibrissa cells. From the incidence of four types of correlation between S-TR and VB neurons with RFs on the same vibrissa, the following connection pattern is suggested: One S-TR neuron receives excitatory inputs from approximately 40% of the VB neurons with RFs on the same vibrissa and sends inhibitory outputs to approximately 55%. Since these two groups of VB neurons were overlapping, the S-TR neuron has reciprocal connections with approximately 20% of the VB neurons with RFs on the same vibrissa. The same estimate was applied to connectivity of one VB neuron. These results indicate that both inputs and outputs of S-TR neurons are precisely and topographically organized, although there is convergence to and divergence from a substantial number of VB neurons with RFs on the same vibrissa. It is proposed that the recurrent inhibitory circuit through the S-TR plays a role in improving discrimination of sensory information transmitted through the VB.
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Sun, Wensheng, Ellisha N. Marongelli, Paul V. Watkins, and Dennis L. Barbour. "Decoding sound level in the marmoset primary auditory cortex." Journal of Neurophysiology 118, no. 4 (October 1, 2017): 2024–33. http://dx.doi.org/10.1152/jn.00670.2016.
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Neurons that respond favorably to a particular sound level have been observed throughout the central auditory system, becoming steadily more common at higher processing areas. One theory about the role of these level-tuned or nonmonotonic neurons is the level-invariant encoding of sounds. To investigate this theory, we simulated various subpopulations of neurons by drawing from real primary auditory cortex (A1) neuron responses and surveyed their performance in forming different sound level representations. Pure nonmonotonic subpopulations did not provide the best level-invariant decoding; instead, mixtures of monotonic and nonmonotonic neurons provided the most accurate decoding. For level-fidelity decoding, the inclusion of nonmonotonic neurons slightly improved or did not change decoding accuracy until they constituted a high proportion. These results indicate that nonmonotonic neurons fill an encoding role complementary to, rather than alternate to, monotonic neurons. NEW & NOTEWORTHY Neurons with nonmonotonic rate-level functions are unique to the central auditory system. These level-tuned neurons have been proposed to account for invariant sound perception across sound levels. Through systematic simulations based on real neuron responses, this study shows that neuron populations perform sound encoding optimally when containing both monotonic and nonmonotonic neurons. The results indicate that instead of working independently, nonmonotonic neurons complement the function of monotonic neurons in different sound-encoding contexts.