Relevant bibliographies by topics / Feedback neuron

Academic literature on the topic 'Feedback neuron'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Feedback neuron"

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Vidybida, Alexander. "Relation Between Firing Statistics of Spiking Neuron with Instantaneous Feedback and Without Feedback." Fluctuation and Noise Letters 14, no. 04 (November 9, 2015): 1550034. http://dx.doi.org/10.1142/s0219477515500340.

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We consider a class of spiking neuron models, defined by a set of conditions which are typical for basic threshold-type models like leaky integrate-and-fire, or binding neuron model and also for some artificial neurons. A neuron is fed with a point renewal process. A relation between the three probability density functions (PDF): (i) PDF of input interspike intervals ISIs, (ii) PDF of output interspike intervals of a neuron with a feedback and (iii) PDF for that same neuron without feedback is derived. This allows to calculate any one of the three PDFs provided the remaining two are given. Similar relation between corresponding means and variances is derived. The relations are checked exactly for the binding neuron model stimulated with Poisson stream.
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Spencer, Robert M., and Dawn M. Blitz. "Network feedback regulates motor output across a range of modulatory neuron activity." Journal of Neurophysiology 115, no. 6 (June 1, 2016): 3249–63. http://dx.doi.org/10.1152/jn.01112.2015.

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Modulatory projection neurons alter network neuron synaptic and intrinsic properties to elicit multiple different outputs. Sensory and other inputs elicit a range of modulatory neuron activity that is further shaped by network feedback, yet little is known regarding how the impact of network feedback on modulatory neurons regulates network output across a physiological range of modulatory neuron activity. Identified network neurons, a fully described connectome, and a well-characterized, identified modulatory projection neuron enabled us to address this issue in the crab ( Cancer borealis) stomatogastric nervous system. The modulatory neuron modulatory commissural neuron 1 (MCN1) activates and modulates two networks that generate rhythms via different cellular mechanisms and at distinct frequencies. MCN1 is activated at rates of 5–35 Hz in vivo and in vitro. Additionally, network feedback elicits MCN1 activity time-locked to motor activity. We asked how network activation, rhythm speed, and neuron activity levels are regulated by the presence or absence of network feedback across a physiological range of MCN1 activity rates. There were both similarities and differences in responses of the two networks to MCN1 activity. Many parameters in both networks were sensitive to network feedback effects on MCN1 activity. However, for most parameters, MCN1 activity rate did not determine the extent to which network output was altered by the addition of network feedback. These data demonstrate that the influence of network feedback on modulatory neuron activity is an important determinant of network output and feedback can be effective in shaping network output regardless of the extent of network modulation.
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Vidybida, Alexander, and Olha Shchur. "Relation Between Firing Statistics of Spiking Neuron with Delayed Fast Inhibitory Feedback and Without Feedback." Fluctuation and Noise Letters 17, no. 01 (January 23, 2018): 1850005. http://dx.doi.org/10.1142/s0219477518500050.

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We consider a class of spiking neuronal models, defined by a set of conditions typical for basic threshold-type models, such as the leaky integrate-and-fire or the binding neuron model and also for some artificial neurons. A neuron is fed with a Poisson process. Each output impulse is applied to the neuron itself after a finite delay [Formula: see text]. This impulse acts as being delivered through a fast Cl-type inhibitory synapse. We derive a general relation which allows calculating exactly the probability density function (pdf) [Formula: see text] of output interspike intervals of a neuron with feedback based on known pdf [Formula: see text] for the same neuron without feedback and on the properties of the feedback line (the [Formula: see text] value). Similar relations between corresponding moments are derived.Furthermore, we prove that the initial segment of pdf [Formula: see text] for a neuron with a fixed threshold level is the same for any neuron satisfying the imposed conditions and is completely determined by the input stream. For the Poisson input stream, we calculate that initial segment exactly and, based on it, obtain exactly the initial segment of pdf [Formula: see text] for a neuron with feedback. That is the initial segment of [Formula: see text] is model-independent as well. The obtained expressions are checked by means of Monte Carlo simulation. The course of [Formula: see text] has a pronounced peculiarity, which makes it impossible to approximate [Formula: see text] by Poisson or another simple stochastic process.
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HAYOT, FERNAND, and DANIEL TRANCHINA. "Modeling corticofugal feedback and the sensitivity of lateral geniculate neurons to orientation discontinuity." Visual Neuroscience 18, no. 6 (November 2001): 865–77. http://dx.doi.org/10.1017/s0952523801186037.

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We model feedback from primary visual cortex to the dorsal lateral geniculate nucleus (dLGN). This feedback makes dLGN neurons sensitive to orientation discontinuity (Sillito et al., 1993; Cudeiro & Sillito, 1996). In the model, each dLGN neuron receives retinotopic input driven by layer 6 cortical neurons in a full set of orientation columns. Excitation is monosynaptic, while inhibition is through perigeniculate neurons and dLGN interneurons. The stimulus consists of drifting gratings, one within and the other outside a circular region centered over the receptive field of the model dLGN relay neuron we study. They appear as a single grating when they are aligned with equal contrast. The model reproduces experimental results showing an increasing inhibitory effect of feedback on the firing rate of dLGN neurons as the two gratings move towards the aligned position. Moreover, enhancement of dLGN cell center-surround antagonism by feedback is revealed by measuring the responses to drifting gratings inside a circular window, as a function of window radius. This effect is related to the observed length tuning of dLGN cells. Sensitivity to orientation discontinuity could be mediated in the model by feedback from either simple or complex cells. The model puts constraints on the feedback synaptic footprint and shows that its elongated shape does not play a crucial role in sensitivity to orientation discontinuity. The inhibitory component of feedback must predominate overall, but the feedback signal from a cortical neuron to a dLGN neuron with the same or nearby receptive-field center can be dominated by excitation. Predictions of the model include (1) robust stimuli for layer 6 cortical neurons give pronounced nonlinearities in the responses of dLGN neurons; (2) the sensitivity to orientation discontinuity at low contrast is twice that at high contrast.
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Blitz, Dawn M. "Circuit feedback increases activity level of a circuit input through interactions with intrinsic properties." Journal of Neurophysiology 118, no. 2 (August 1, 2017): 949–63. http://dx.doi.org/10.1152/jn.00772.2016.

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Feedback from central pattern generator (CPG) circuits patterns activity of their projection neuron inputs. However, whether the intraburst firing rate between rhythmic feedback inhibition is also impacted by CPG feedback was not known. I establish that CPG feedback can alter the projection neuron intraburst firing rate through interactions with projection neuron intrinsic properties. The contribution of feedback to projection neuron activity level is specific to the modulatory condition, demonstrating a state dependence for this novel role of circuit feedback.
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Torres-Treviño, Luis M., Angel Rodríguez-Liñán, Luis González-Estrada, and Gustavo González-Sanmiguel. "Single Gaussian Chaotic Neuron: Numerical Study and Implementation in an Embedded System." Discrete Dynamics in Nature and Society 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/318758.

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Artificial Gaussian neurons are very common structures of artificial neural networks like radial basis function. These artificial neurons use a Gaussian activation function that includes two parameters called the center of mass (cm) and sensibility factor (λ). Changes on these parameters determine the behavior of the neuron. When the neuron has a feedback output, complex chaotic behavior is displayed. This paper presents a study and implementation of this particular neuron. Stability of fixed points, bifurcation diagrams, and Lyapunov exponents help to determine the dynamical nature of the neuron, and its implementation on embedded system illustrates preliminary results toward embedded chaos computation.
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Xu, Yao-Qun, Xin-Xin Zhen, and Meng Tang. "Dynamical System in Chaotic Neurons with Time Delay Self-Feedback and Its Application in Color Image Encryption." Complexity 2022 (July 1, 2022): 1–28. http://dx.doi.org/10.1155/2022/2832104.

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The time delay caused by transmission in neurons is often ignored, but it is demonstrated by theories and practices that time delay is unavoidable. A new chaotic neuron model with time delay self-feedback is proposed based on Chen’s chaotic neuron. The bifurcation diagram and Lyapunov exponential diagram are used to analyze the chaotic characteristics of neurons in the model when they receive the output signals at different times. The experimental results exhibit that it has a rich dynamic behavior. In addition, the randomness of chaotic series generated by chaotic neurons with time delay self-feedback under different conditions is verified. In order to investigate the application of this model in image encryption, an image encryption scheme is proposed. The security analysis of the simulation results shows that the encryption algorithm has an excellent anti-attack ability. Therefore, it is necessary and practical to study chaotic neurons with time delay self-feedback.
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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.
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Rybak, Ilya A., Julian F. R. Paton, and James S. Schwaber. "Modeling Neural Mechanisms for Genesis of Respiratory Rhythm and Pattern. II. Network Models of the Central Respiratory Pattern Generator." Journal of Neurophysiology 77, no. 4 (April 1, 1997): 2007–26. http://dx.doi.org/10.1152/jn.1997.77.4.2007.

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Rybak, Ilya A., Julian F. R. Paton, and James S. Schwaber. Modeling neural mechanisms for genesis of respiratory rhythm and pattern. II. Network models of the central respiratory pattern generator. J. Neurophysiol. 77: 2007–2026, 1997. The present paper describes several models of the central respiratory pattern generator (CRPG) developed employing experimental data and current hypotheses for respiratory rhythmogenesis. Each CRPG model includes a network of respiratory neuron types (e.g., early inspiratory; ramp inspiratory; late inspiratory; decrementing expiratory; postinspiratory; stage II expiratory; stage II constant firing expiratory; preinspiratory) and simplified models of lung and pulmonary stretch receptors (PSR), which provide feedback to the respiratory network. The used models of single respiratory neurons were developed in the Hodgkin-Huxley style as described in the previous paper. The mechanism for termination of inspiration (the inspiratory off-switch) in all models operates via late-I neuron, which is considered to be the inspiratory off-switching neuron. Several two- and three-phase CRPG models have been developed using different accepted hypotheses of the mechanism for termination of expiration. The key elements in the two-phase models are the early-I and dec-E neurons. The expiratory off-switch mechanism in these models is based on the mutual inhibitory connections between early-I and dec-E and adaptive properties of the dec-E neuron. The difference between the two-phase models concerns the mechanism for ramp firing patterns of E2 neurons resulting either from the intrinsic neuronal properties of the E2 neuron or from disinhibition from the adapting dec-E neuron. The key element of the three-phase models is the pre-I neuron, which acts as the expiratory off-switching neuron. The three-phase models differ by the mechanisms used for termination of expiration and for the ramp firing patterns of E2 neurons. Additional CRPG models were developed employing a dual switching neuron that generates two bursts per respiratory cycle to terminate both inspiration and expiration. Although distinctly different each model generates a stable respiratory rhythm and shows physiologically plausible firing patterns of respiratory neurons with and without PSR feedback. Using our models, we analyze the roles of different respiratory neuron types and their interconnections for the respiratory rhythm and pattern generation. We also investigate the possible roles of intrinsic biophysical properties of different respiratory neurons in controlling the duration of respiratory phases and timing of switching between them. We show that intrinsic membrane properties of respiratory neurons are integrated with network properties of the CRPG at three hierarchical levels: at the cellular level to provide the specific firing patterns of respiratory neurons (e.g., ramp firing patterns); at the network level to provide switching between the respiratory phases; and at the systems level to control the duration of inspiration and expiration under different conditions (e.g., lack of PSR feedback).
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Chambers, Jordan D., Joel C. Bornstein, Henrik Sjövall, and Evan A. Thomas. "Recurrent networks of submucous neurons controlling intestinal secretion: a modeling study." American Journal of Physiology-Gastrointestinal and Liver Physiology 288, no. 5 (May 2005): G887—G896. http://dx.doi.org/10.1152/ajpgi.00491.2004.

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Secretomotor neurons, immunoreactive for vasoactive intestinal peptide (VIP), are important in controlling chloride secretion in the small intestine. These neurons form functional synapses with other submucosal VIP neurons and transmit via slow excitatory postsynaptic potentials (EPSPs). Thus they form a recurrent network with positive feedback. Intrinsic sensory neurons within the submucosa are also likely to form recurrent networks with positive feedback, provide substantial output to VIP neurons, and receive input from VIP neurons. If positive feedback within recurrent networks is sufficiently large, then neurons in the network respond to even small stimuli by firing at their maximum possible rate, even after the stimulus is removed. However, it is not clear whether such a mechanism operates within the recurrent networks of submucous neurons. We investigated this question by performing computer simulations of realistic models of VIP and intrinsic sensory neuron networks. In the expected range of electrophysiological properties, we found that activity in the VIP neuron network decayed slowly after cessation of a stimulus, indicating that positive feedback is not strong enough to support the uncontrolled firing state. The addition of intrinsic sensory neurons produced a low stable firing rate consistent with the common finding that basal secretory activity is, in part, neurogenic. Changing electrophysiological properties enables these recurrent networks to support the uncontrolled firing state, which may have implications with hypersecretion in the presence of enterotoxins such as cholera-toxin.
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Dissertations / Theses on the topic "Feedback neuron"

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Dickson, Scott M. "Stochastic neural network dynamics : synchronisation and control." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/16508.

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Biological brains exhibit many interesting and complex behaviours. Understanding of the mechanisms behind brain behaviours is critical for continuing advancement in fields of research such as artificial intelligence and medicine. In particular, synchronisation of neuronal firing is associated with both improvements to and degeneration of the brain's performance; increased synchronisation can lead to enhanced information-processing or neurological disorders such as epilepsy and Parkinson's disease. As a result, it is desirable to research under which conditions synchronisation arises in neural networks and the possibility of controlling its prevalence. Stochastic ensembles of FitzHugh-Nagumo elements are used to model neural networks for numerical simulations and bifurcation analysis. The FitzHugh-Nagumo model is employed because of its realistic representation of the flow of sodium and potassium ions in addition to its advantageous property of allowing phase plane dynamics to be observed. Network characteristics such as connectivity, configuration and size are explored to determine their influences on global synchronisation generation in their respective systems. Oscillations in the mean-field are used to detect the presence of synchronisation over a range of coupling strength values. To ensure simulation efficiency, coupling strengths between neurons that are identical and fixed with time are investigated initially. Such networks where the interaction strengths are fixed are referred to as homogeneously coupled. The capacity of controlling and altering behaviours produced by homogeneously coupled networks is assessed through the application of weak and strong delayed feedback independently with various time delays. To imitate learning, the coupling strengths later deviate from one another and evolve with time in networks that are referred to as heterogeneously coupled. The intensity of coupling strength fluctuations and the rate at which coupling strengths converge to a desired mean value are studied to determine their impact upon synchronisation performance. The stochastic delay differential equations governing the numerically simulated networks are then converted into a finite set of deterministic cumulant equations by virtue of the Gaussian approximation method. Cumulant equations for maximal and sub-maximal connectivity are used to generate two-parameter bifurcation diagrams on the noise intensity and coupling strength plane, which provides qualitative agreement with numerical simulations. Analysis of artificial brain networks, in respect to biological brain networks, are discussed in light of recent research in sleep theory.
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Shin, Jiyun. "Perirhinal feedback input controls neocortical memory formation via layer 1." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22312.

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Das deklarative Gedächtnis beruht auf Wechselwirkungen zwischen dem medialen Temporallappens (MTL) und Neokortex. Aufgrund der verteilten Natur neokortikaler Netzwerke bleiben zelluläre Ziele und Mechanismen der Gedächtnisbildung im Neokortex jedoch schwer fassbar. Im sechsschichtigen Säugetier-Neokortex konvergieren die Top-Down-Inputs auf Schicht 1 (L1). Wir untersuchten, wie Top-Down-Inputs von MTL die neokortikale Aktivität während der Gedächtnisbildung modulieren. Wir haben zunächst ein Kortex- und Hippocampus-abhängiges Lernparadigma angepasst, in dem Tiere gelernt haben, direkte kortikale Mikrostimulation und Belohnung zu assoziieren. Neuronen in den tiefen Schichten des perirhinalen Kortex lieferten monosynaptische Eingaben in L1 des primären somatosensorischen Kortex (S1), wo die Mikrostimulation vorgestellt wurde. Die chemogenetische Unterdrückung der perirhinalen Inputs in L1 von S1 störte die Gedächtnisbildung, hatte jedoch keinen Einfluss auf die Leistung der Tiere nach abgeschlossenem Lernen. Dem Lernen folgte das Auftreten einer klaren Subpopulation von Pyramidenneuronen der Schicht 5 (L5), die durch hochfrequentes Burst-Feuern gekennzeichnet war und durch Blockieren der perirhinalen Inputs zu L1 reduziert werden konnte. Interessanterweise zeigte ein ähnlicher Anteil an apikalen Dendriten von L5-Pyramidenneuronen ebenfalls eine signifikant erhöhte Ca2+-Aktivität während des Gedächtnisabrufs bei Expertentieren. Wichtig ist, dass die Störung der dendritischen Ca2+-Aktivität das Lernen beeinträchtigte, was darauf hindeutet, dass apikale Dendriten von L5-Pyramidenneuronen eine entscheidende Rolle bei der Bildung des neokortikalen Gedächtnisses spielen. Wir schließen daraus, dass MTL-Eingaben das Lernen über einen perirhinalen vermittelten Gating-Prozess in L1 steuern, der sich in einer erhöhten dendritischen Ca2+-Aktivität und einem Burst-Firing in pyramidalen L5-Neuronen manifestiert.
Declarative memory relies on interactions between the medial temporal lobe (MTL) and neocortex. However, due the distributed nature of neocortical networks, cellular targets and mechanisms of memory formation in the neocortex remain elusive. In the six-layered mammalian neocortex, top-down inputs converge on its outermost layer, layer 1 (L1). We examined how layer-specific top-down inputs from MTL modulate neocortical activity during memory formation. We first adapted a cortical- and hippocampal-dependent learning paradigm, in which animals learned to associate direct cortical microstimulation and reward, and characterized the learning behavior of rats and mice. We next showed that neurons in the deep layers of the perirhinal cortex not only provide monosynaptic inputs to L1 of the primary somatosensory cortex (S1), where microstimulation was presented, but also actively reflect the behavioral outcome. Chemogenetic suppression of perirhinal inputs to L1 of S1 disrupted early memory formation but did not affect animals’ performance after learning. The learning was followed by an emergence of a distinct subpopulation of layer 5 (L5) pyramidal neurons characterized by high-frequency burst firing, which could be reduced by blocking perirhinal inputs to L1. Interestingly, a similar proportion of apical dendrites (~10%) of L5 pyramidal neurons also displayed significantly enhanced calcium (Ca2+) activity during memory retrieval in expert animals. Importantly, disrupting dendritic Ca2+ activity impaired learning, suggesting that apical dendrites of L5 pyramidal neurons have a critical role in neocortical memory formation. Taken together, these results suggest that MTL inputs control learning via a perirhinal-mediated gating process in L1, manifested by elevated dendritic Ca2+ activity and burst firing in L5 pyramidal neurons. The present study provides insights into cellular mechanisms of learning and memory representations in the neocortex.
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Kromer, Justus Alfred. "Noise in adaptive excitable systems and small neural networks." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17683.

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Neuronen sind erregbare Systeme. Ihre Antwort auf Anregungen oberhalb eines bestimmten Schwellwertes sind Pulse. Häufig wird die Pulserzeugung von verschiedenen Rückkopplungsmechanismen beeinflusst, die auf langsamen Zeitskalen agieren. Das kann zu Phänomenen wie Feuerraten-Adaptation, umgekehrter Feuerraten-Adaptation oder zum Feuern von Pulsen in Salven führen. Weiterhin sind Neuronen verschiedenen Rauschquellen ausgesetzt und wechselwirken mit anderen Neuronen, in neuronalen Netzen. Doch wie beeinflusst das Zusammenspiel von Rückkopplungsmechanismen, Rauschen und der Wechselwirkung mit anderen Neuronen die Pulserzeugung? Diese Arbeit untersucht, wie die Pulserzeugung in rauschgetriebenen erregbaren Systemen von langsamen Rückkopplungsmechanismen und der Wechselwirkung mit anderen erregbaren Systemen beeinflusst wird. Dabei wird die Pulserzeugung in drei Szenarien betrachtet: (i) in einem einzelnen erregbaren System, das um einen langsamen Rückkopplungsmechanismus erweitert wurde, (ii) in gekoppelten erregbaren Systemen und (iii) in stark gekoppelten salvenfeuernden Neuronen. In jedem dieser Szenarien wird die Pulsstatistik mit Hilfe von analytischen Methoden und Computersimulationen untersucht. Das wichtigste Resultat im ersten Szenario ist, dass das Zusammenspiel von einer stark anregenden Rückkopplung und Rauschen zu rauschkontrollierter Bistabilität führt. Das erlaubt es dem System zwischen verschiedenen Modi der Pulserzeugung zu wechseln. In (ii) wird die Pulserzeugung stark von der Wahl der Kopplungsstärken und der Anzahl der Verbindungen beeinflusst. Analytische Näherungen werden abgeleitet, die einen Zusammenhang zwischen der Anzahl der Verbindungen und der Pulsrate, sowie der Pulszugvariabilität herstellen. In (iii) wird festgestellt, dass eine hemmende Rückkopplung zu sehr unregelmäßigem Verhalten der isolierten Neuronen führt, wohingegen eine starke Kopplung mit dem Netzwerk ein regelmäßigeres Feuern von Salven hervorruft.
Neurons are excitable systems. Their responses to excitations above a certain threshold are spikes. Usually, spike generation is shaped by several feedback mechanisms that can act on slow time scales. These can lead to phenomena such as spike-frequency adaptation, reverse spike-frequency adaptation, or bursting. In addition to these, neurons are subject to several sources of noise and interact with other neurons, in the connected complexity of a neural network. Yet how does the interplay of feedback mechanisms, noise as well as interaction with other neurons affect spike generation? This thesis examines how spike generation in noise-driven excitable systems is influenced by slow feedback processes and coupling to other excitable systems. To this end, spike generation in three setups is considered: (i) in a single excitable system, which is complemented by a slow feedback mechanism, (ii) in a set of coupled excitable systems, and (iii) in a set of strongly-coupled bursting neurons. In each of these setups, the statistics of spiking is investigated by a combination of analytical methods and computer simulations. The main result of the first setup is that the interplay of strong positive (excitatory) feedback and noise leads to noise-controlled bistability. It enables excitable systems to switch between different modes of spike generation. In (ii), spike generation is strongly affected by the choice of the coupling strengths and the number of connections. Analytical approximations are derived that relate the number of connections to the firing rate and the spike train variability. In (iii), it is found that negative (inhibitory) feedback causes very irregular behavior of the isolated bursters, while strong coupling to the network regularizes the bursting.
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Gill, Jeffrey Paul. "Neural Correlates of Adaptive Responses to Changing Load in Feeding Aplysia." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1579795905638273.

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Newman, Jonathan P. "Optogenetic feedback control of neural activity." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52973.

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Optogenetics is a set of technologies that enable optically triggered gain or loss of function in genetically specified populations of cells. Optogenetic methods have revolutionized experimental neuroscience by allowing precise excitation or inhibition of firing in specified neuronal populations embedded within complex, heterogeneous tissue. Although optogenetic tools have greatly improved our ability manipulate neural activity, they do not offer control of neural firing in the face of ongoing changes in network activity, plasticity, or sensory input. In this thesis, I develop a feedback control technology that automatically adjusts optical stimulation in real-time to precisely control network activity levels. I describe hardware and software tools, modes of optogenetic stimulation, and control algorithms required to achieve robust neural control over timescales ranging from seconds to days. I then demonstrate the scientific utility of these technologies in several experimental contexts. First, I investigate the role of connectivity in shaping the network encoding process using continuously-varying optical stimulation. I show that synaptic connectivity linearizes the neuronal response, verifying previous theoretical predictions. Next, I use long-term optogenetic feedback control to show that reductions in excitatory neurotransmission directly trigger homeostatic increases in synaptic strength. This result opposes a large body of literature on the subject and has significant implications for memory formation and maintenance. The technology presented in this thesis greatly enhances the precision with which optical stimulation can control neural activity, and allows causally related variables within neural circuits to be studied independently.
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Sutherland, Connie. "Spatio-temporal feedback in stochastic neural networks." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27559.

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The mechanisms by which groups of neurons interact is an important facet to understanding how the brain functions. Here we study stochastic neural networks with delayed feedback. The first part of our study looks at how feedback and noise affect the mean firing rate of the network. Secondly we look at how the spatial profile of the feedback affects the behavior of the network. Our numerical and theoretical results show that negative (inhibitory) feedback linearizes the frequency vs input current (f-I) curve via the divisive gain effect it has on the network. The interaction of the inhibitory feedback and the input bias is what produces the divisive decrease in the slope (known as the gain) of the f-I curve. Our work predicts that an increase in noise is required along with increase in inhibitory feedback to attain a divisive and subtractive shift of the gain as seen in experiments [1]. Our results also show that, although the spatial profile of the feedback does not effect the mean activity of the network, it does influence the overall dynamics of the network. Local feedback generates a network oscillation, which is more robust against disruption by noise or uncorrelated input or network heterogeneity, than that for the global feedback (all-to-all coupling) case. For example uncorrelated input completely disrupts the network oscillation generated by global feedback, but only diminishes the network oscillation due to local feedback. This is characterized by 1st and 2nd order spike train statistics. Further, our theory agrees well with numerical simulations of network dynamics.
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Williams, Ian. "Methods and microelectronics for proprioceptive neural feedback." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24566.

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A neural implant giving an amputee a sense of feeling back in their prosthetic limb could help millions of people live happier, more productive lives. Tactile feedback is commonly targeted, however, it is the lesser known sense of proprioception that is crucial for smooth, coordinated limb control and non-visual limb awareness - both of which are high priorities for amputees. This thesis describes research carried out to progress the development and creation of aproprioceptive neural prosthesis targeted at trans-humeral upper limb amputees. Firstly a review of proprioceptive neural prosthesis design considerations and challenges is presented. The purpose of which is to identify areas requiring further development and to identify a prototype target system that focuses and scopes design effort. Then 3 technical chapters cover research into: (1) Combining efficient implementations of biomechanical and proprioceptor models in order to generate signals that mimic human muscular proprioceptive patterns. A neuromusculoskeletal model of the upper limb with 7 degrees of freedom and 17 muscles is presented and generates real time estimates of muscle spindle and Golgi Tendon Organ neural firing patterns. (2) An 8 channel energy-efficient neural stimulator for generating charge-balanced asymmetric pulses. Power consumption is reduced by implementing a fully-integrated DC-DC converter that uses a reconfigurable switched capacitor topology to provide 4 output voltages for Dynamic Voltage Scaling (DVS). A novel charge balancing method is implemented which has a low level of accuracy on a single pulse and a much higher accuracy over a series of pulses. The method used is robust to process and component variation and does not require any initial or ongoing calibration. (3) A non-invasive proprioceptive feedback trial platform (using vibration induced proprioception) for testing modelled neural signals. A low cost vibration device is designed and tested, identifying key issues with this form of non-invasive feedback.
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Fumuro, Tomoyuki. "Bereitschaftspotential augmentation by neuro-feedback training in Parkinson's disease." Kyoto University, 2013. http://hdl.handle.net/2433/174832.

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Habte, Samson. "Snap-drift neural computing for intelligent diagnostic feedback." Thesis, London Metropolitan University, 2017. http://repository.londonmet.ac.uk/1247/.

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Information and communication technologies have been playing a crucial role in improving the efficiency and effectiveness of learning and teaching in higher education. Two decades ago, research studies were focused on how to use artificial intelligence techniques to imitate teachers or tutors in delivering learning sessions. Machine learning techniques have been applied in several research studies to construct a student model in the context of intelligent tutoring systems. However, the usage of intelligent tutoring systems has been very limited in higher education as most educational institutions are in favour of using virtual learning environments (VLEs). VLEs are computer-based systems that support all aspects of teaching and learning from provision of course materials to managing coursework. In this research study, the emphasis is on the assessment aspect of VLEs. A literature review revealed that existing computer-based formative assessments have never utilised unsupervised machine learning to improve their feedback mechanisms. Machine learning techniques have been applied to construct student models, which is represented as categories of knowledge levels such as beginning, intermediate and advanced. The student model does not specify what concepts are understood, the gap of understanding and misconceptions. Previously, a snap-drift modal learning neural network has been applied to improve the feedback mechanisms of computer-based formative assessments. This study investigated the application of snap-drift modal learning neural network for analysing student responses to a set of multiple choice questions to identify student groups. This research study builds on this previous study and its aim is to improve the effectiveness of the application of snap-drift modal learning neural network in modelling student responses to a set of multiple choice questions and to extend its application in modelling student responses gathered from object-oriented programming exercises. A novel method was proposed and evaluated using trials that improves the effectiveness of snap-drift modal learning neural network in identifying useful student group profiles, representing them to facilitate generation of diagnostic feedback and assigning an appropriate diagnostic feedback automatically based on a given student response. Based on the insight gained into the use of this novel method, we extend it to identify useful student group profiles that represent different programming abilities for writing an object-oriented class. The purpose of identifying student group profiles is to facilitate construction of diagnostic feedback that improves the development of basic object-oriented programming abilities. Overall, the main objectives of this research project were addressed successfully. New insights are gained into the application of unsupervised learning in general and snap-drift modal learning in particular. The proposed methods are capable of improving the feedback mechanisms of existing computer-based formative assessment tools. The improved computer-based formative assessments could have a huge impact on students in improving conceptual understanding of topics and development of basic object-oriented programming abilities.
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Andréasson, Per. "Emotional Empathy, Facial Reactions, and Facial Feedback." Doctoral thesis, Uppsala universitet, Institutionen för psykologi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-126825.

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The human face has a fascinating capability to express emotions. The facial feedback hypothesis suggests that the human face not only expresses emotions but is also able to send feedback to the brain and modulate the ongoing emotional experience. It has furthermore been suggested that this feedback from the facial muscles could be involved in empathic reactions. This thesis explores the concept of emotional empathy and relates it to two aspects concerning activity in the facial muscles. First, do people high versus low in emotional empathy differ in regard to in what degree they spontaneously mimic emotional facial expressions? Second, is there any difference between people with high as compared to low emotional empathy in respect to how sensitive they are to feedback from their own facial muscles? Regarding the first question, people with high emotional empathy were found to spontaneously mimic pictures of emotional facial expressions while people with low emotional empathy were lacking this mimicking reaction. The answer to the second question is a bit more complicated. People with low emotional empathy were found to rate humorous films as funnier in a manipulated sulky facial expression than in a manipulated happy facial expression, whereas people with high emotional empathy did not react significantly. On the other hand, when the facial manipulations were a smile and a frown, people with low as well as high emotional empathy reacted in line with the facial feedback hypothesis. In conclusion, the experiments in the present thesis indicate that mimicking and feedback from the facial muscles may be involved in emotional contagion and thereby influence emotional empathic reactions. Thus, differences in emotional empathy may in part be accounted for by different degree of mimicking reactions and different emotional effects of feedback from the facial muscles.
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Books on the topic "Feedback neuron"

1

Ansari, Mohd Samar. Non-Linear Feedback Neural Networks. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1563-9.

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1973-, Garces Freddy, ed. Strategies for feedback linearisation: A dynamic neural network approach. London: Springer, 2003.

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Kwan, Hon C. Network relaxation as behavioral action: Some conjectures on the control of movement by the nervous system. Toronto: University of Toronto, Dept. of Physiology, Computer Science and Anatomy, 1988.

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Kuridan, Ramadan Muftah. Computational neutron transport and thermal-hydraulics feedback and transient models for the safe integral reactor concept. Birmingham: University of Birmingham, 1995.

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Marios, Polycarpou, ed. Adaptive approximation based control: Unifying neural, fuzzy and traditional adaptive approximation approaches. Hoboken, NJ: Wiley, 2006.

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Suresh, Jagannathan, and Yeşildirek A, eds. Neural network control of robot manipulators and nonlinear systems. London: Taylor & Francis, 1999.

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Leigh, J. R. Control Theory. 2nd ed. Stevenage: IET, 2004.

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Montgomery, Erwin B. Discrete Neural Oscillators. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190259600.003.0017.

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The therapeutic mechanisms of action of DBS likely involve neural and neuronal oscillators. “Neuronal oscillators” describes periodic fluctuations of electrical potentials across the neuronal membrane, particularly in the soma, which is reflected in an action-potential-initiating segment. “Neural oscillators” describes closed loop (feedback) multi-neuronal polysynaptic circuits, on account of the propagations of action potentials through the circuit. Neural oscillators are the focus of this chapter. The features, properties and dyanmics introduced in Chapter 16 – Basic Oscillators are extended from continuous harmonic oscillators to discrete neural oscillators. While discrete oscillators received scant attention to date, systems of discrete oscillators have much richer set of dynamics that could provide better understanding of the pathophysiology and physiology of neural systems, such as the basal ganglia-thalamic-cortical system as well as greater insights into the therapeutic mechanisms of action underlying DBS.
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Fyfe, Colin. Hebbian Learning and Negative Feedback Networks. Springer, 2010.

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Fyfe, Colin. Hebbian Learning and Negative Feedback Networks. Springer London, Limited, 2007.

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Book chapters on the topic "Feedback neuron"

1

Struppler, A. "Feedback Mechanisms Controlling Skeletal Muscle Tone." In From Neuron to Action, 71–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02601-4_9.

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Kornhuber, A. W., W. Becker, and R. Jürgens. "The Role of Visual Feedback and Preprogramming for Smooth Pursuit Eye Movements: Experiments with Velocity Steps." In From Neuron to Action, 175–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02601-4_21.

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Clarke, Iain. "Generation of the GnRH Surge and LH Surge by the Positive Feedback Effect of Estrogen." In The GnRH Neuron and its Control, 325–56. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119233275.ch13.

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Xiao, Min. "Hopf Bifurcation Control for a Single Neuron Model with Delay-Dependent Parameters via State Feedback." In Advances in Neural Networks – ISNN 2011, 132–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21105-8_17.

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Lindblad, Thomas, and Jason M. Kinser. "Feedback." In Image Processing using Pulse-Coupled Neural Networks, 59–63. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-3617-0_7.

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Zhang, Xiang-Sun. "Feedback Neural Networks." In Nonconvex Optimization and Its Applications, 137–75. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3167-5_7.

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Almeida, Luís B. "Backpropagation in Perceptrons with Feedback." In Neural Computers, 199–208. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-83740-1_22.

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Segovia, J., J. Rios, M. Lerma, and D. Barrios. "Feedback in Single Continuous Neurons." In ICANN ’93, 686. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-2063-6_191.

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Womble, Steve, and Stefan Wermter. "Mirror neurons and feedback learning." In Mirror Neurons and the Evolution of Brain and Language, 353–62. Amsterdam: John Benjamins Publishing Company, 2002. http://dx.doi.org/10.1075/aicr.42.28wom.

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Ansari, Mohd Samar. "Introduction." In Non-Linear Feedback Neural Networks, 1–11. New Delhi: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1563-9_1.

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Conference papers on the topic "Feedback neuron"

1

Kang, Tae Seung, and Arunava Banerjee. "Learning deterministic spiking neuron feedback controllers." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966153.

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Fei Li, Chengjie Xie, Dongsheng Zheng, and Baoyu Zheng. "Feedback Quantum Neuron for Multiuser Detection." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.247252.

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Demir, Cenk, Shumon Koga, and Miroslav Krstic. "Neuron Growth Output-Feedback Control by PDE Backstepping." In 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867218.

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Lixian Liu, Bingxin Han, Liqiang Du, and Zhanfeng Gao. "A neural network structure and learning algorithms with the neuron output feedback." In 2010 Third International Workshop on Advanced Computational Intelligence (IWACI). IEEE, 2010. http://dx.doi.org/10.1109/iwaci.2010.5585172.

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Qi, Chi, Zhongsheng Hou, and Xingyi Li. "Freeway Feedback Ramp Metering Based on Neuron Adaptive Control Algorithm." In 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2008. http://dx.doi.org/10.1109/icicta.2008.259.

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Hall, Daniel L., and Biswanath Samanta. "Nonlinear Control of a Magnetic Levitation System Using Single Multiplicative Neuron Models." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64066.

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The paper presents an approach to nonlinear control of a magnetic levitation system using artificial neural networks (ANN). A novel form of ANN, namely, single multiplicative neuron (SMN) model is proposed in place of more traditional multi-layer perceptron (MLP). SMN derives its inspiration from the single neuron computation model in neuroscience. SMN model is trained off-line, to estimate the network weights and biases, using a population based stochastic optimization technique, namely, particle swarm optimization (PSO). Both off-line training and on-line learning of SMN have been considered. The ANN based techniques have been compared with a feedback linearization approach. The development of the control algorithms is illustrated through the hardware-in-the-loop (HIL) implementation of magnetic levitation in LabVIEW environment. The controllers based on ANN performed quite well and better than the one based on feedback linearization. However, the SMN structure was much simpler than the MLP for similar performance. The simple structure and faster computation of SMN have the potential to make it a preferred candidate for implementation of real-life complex magnetic levitation systems.
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Krstic, Vladimir R., and Miroslav Dukic. "On complex domain decision feedback equalizer based on Bell-Sejnowski neuron." In 2008 3rd International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2008. http://dx.doi.org/10.1109/isccsp.2008.4537402.

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Masaev, Dinar, Max Talanov, Evgeniy Zykov, Alina Suleimanova, Alexander Toschev, and Victor Erokhin. "Design and implementation of memristive neuron leakage integrator, and learning feedback." In 2021 International Siberian Conference on Control and Communications (SIBCON). IEEE, 2021. http://dx.doi.org/10.1109/sibcon50419.2021.9438877.

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Lin, J., Annadi, S. Sonde, C. Chen, L. Stan, K. V. L. V. Achari, S. Ramanathan, and S. Guha. "Low-voltage artificial neuron using feedback engineered insulator-to-metal-transition devices." In 2016 IEEE International Electron Devices Meeting (IEDM). IEEE, 2016. http://dx.doi.org/10.1109/iedm.2016.7838541.

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Kommalapati, Roopeswar, and Konstantinos P. Michmizos. "Virtual reality for pediatric neuro-rehabilitation: Adaptive visual feedback of movement to engage the mirror neuron system." In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7592058.

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Reports on the topic "Feedback neuron"

1

Lukow, Steven, Ross Lee, David Grow, and Jonathan Gigax. Advancing Vision-based Feedback and Convolutional Neural Networks for Visual Outlier Detection. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1889960.

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Xang, Yunduan, Kazuhiko Fumoko, and Fumio Kvazimoto. Invariance in specialized deep quaternion neural networks for kinematics feedback control with scant connections. Web of Open Science, February 2020. http://dx.doi.org/10.37686/ser.v1i1.5.

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