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Journal articles on the topic 'Neuronal coding and decoding'

Author: Grafiati

Published: 14 March 2023

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1

Wu, Si, Hiroyuki Nakahara, and Shun-ichi Amari. "Population Coding with Correlation and an Unfaithful Model." Neural Computation 13, no. 4 (April 1, 2001): 775–97. http://dx.doi.org/10.1162/089976601300014349.

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This study investigates a population decoding paradigm in which the maximum likelihood inference is based on an unfaithful decoding model (UMLI). This is usually the case for neural population decoding because the encoding process of the brain is not exactly known or because a simplified decoding model is preferred for saving computational cost. We consider an unfaithful decoding model that neglects the pair-wise correlation between neuronal activities and prove that UMLI is asymptotically efficient when the neuronal correlation is uniform or of limited range. The performance of UMLI is compared with that of the maximum likelihood inference based on the faithful model and that of the center-of-mass decoding method. It turns out that UMLI has advantages of decreasing the computational complexity remarkably and maintaining high-leveldecoding accuracy. Moreover, it can be implemented by a biologically feasible recurrent network (Pouget, Zhang, Deneve, & Latham, 1998). The effect of correlation on the decoding accuracy is also discussed.

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2

Fang, Huijuan, Yongji Wang, and Jiping He. "Spiking Neural Networks for Cortical Neuronal Spike Train Decoding." Neural Computation 22, no. 4 (April 2010): 1060–85. http://dx.doi.org/10.1162/neco.2009.10-08-885.

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Recent investigation of cortical coding and computation indicates that temporal coding is probably a more biologically plausible scheme used by neurons than the rate coding used commonly in most published work. We propose and demonstrate in this letter that spiking neural networks (SNN), consisting of spiking neurons that propagate information by the timing of spikes, are a better alternative to the coding scheme based on spike frequency (histogram) alone. The SNN model analyzes cortical neural spike trains directly without losing temporal information for generating more reliable motor command for cortically controlled prosthetics. In this letter, we compared the temporal pattern classification result from the SNN approach with results generated from firing-rate-based approaches: conventional artificial neural networks, support vector machines, and linear regression. The results show that the SNN algorithm can achieve higher classification accuracy and identify the spiking activity related to movement control earlier than the other methods. Both are desirable characteristics for fast neural information processing and reliable control command pattern recognition for neuroprosthetic applications.

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3

Koyama, Shinsuke. "On the Relation Between Encoding and Decoding of Neuronal Spikes." Neural Computation 24, no. 6 (June 2012): 1408–25. http://dx.doi.org/10.1162/neco_a_00279.

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Neural coding is a field of study that concerns how sensory information is represented in the brain by networks of neurons. The link between external stimulus and neural response can be studied from two parallel points of view. The first, neural encoding, refers to the mapping from stimulus to response. It focuses primarily on understanding how neurons respond to a wide variety of stimuli and constructing models that accurately describe the stimulus-response relationship. Neural decoding refers to the reverse mapping, from response to stimulus, where the challenge is to reconstruct a stimulus from the spikes it evokes. Since neuronal response is stochastic, a one-to-one mapping of stimuli into neural responses does not exist, causing a mismatch between the two viewpoints of neural coding. Here we use these two perspectives to investigate the question of what rate coding is, in the simple setting of a single stationary stimulus parameter and a single stationary spike train represented by a renewal process. We show that when rate codes are defined in terms of encoding, that is, the stimulus parameter is mapped onto the mean firing rate, the rate decoder given by spike counts or the sample mean does not always efficiently decode the rate codes, but it can improve efficiency in reading certain rate codes when correlations within a spike train are taken into account.

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Bethge, M., D. Rotermund, and K. Pawelzik. "Optimal Short-Term Population Coding: When Fisher Information Fails." Neural Computation 14, no. 10 (October 1, 2002): 2317–51. http://dx.doi.org/10.1162/08997660260293247.

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Efficient coding has been proposed as a first principle explaining neuronal response properties in the central nervous system. The shape of optimal codes, however, strongly depends on the natural limitations of the particular physical system. Here we investigate how optimal neuronal encoding strategies are influenced by the finite number of neurons N (place constraint), the limited decoding time window length T (time constraint), the maximum neuronal firing rate fmax (power constraint), and the maximal average rate fmax (energy constraint). While Fisher information provides a general lower bound for the mean squared error of unbiased signal reconstruction, its use to characterize the coding precision is limited. Analyzing simple examples, we illustrate some typical pitfalls and thereby show that Fisher information provides a valid measure for the precision of a code only if the dynamic range (fmin T, fmax T) is sufficiently large. In particular, we demonstrate that the optimal width of gaussian tuning curves depends on the available decoding time T. Within the broader class of unimodal tuning functions, it turns out that the shape of a Fisher-optimal coding scheme is not unique. We solve this ambiguity by taking the minimum mean square error into account, which leads to flat tuning curves. The tuning width, however, remains to be determined by energy constraints rather than by the principle of efficient coding.

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Curreli, Sebastiano, Jacopo Bonato, Sara Romanzi, Stefano Panzeri, and Tommaso Fellin. "Complementary encoding of spatial information in hippocampal astrocytes." PLOS Biology 20, no. 3 (March 3, 2022): e3001530. http://dx.doi.org/10.1371/journal.pbio.3001530.

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Calcium dynamics into astrocytes influence the activity of nearby neuronal structures. However, because previous reports show that astrocytic calcium signals largely mirror neighboring neuronal activity, current information coding models neglect astrocytes. Using simultaneous two-photon calcium imaging of astrocytes and neurons in the hippocampus of mice navigating a virtual environment, we demonstrate that astrocytic calcium signals encode (i.e., statistically reflect) spatial information that could not be explained by visual cue information. Calcium events carrying spatial information occurred in topographically organized astrocytic subregions. Importantly, astrocytes encoded spatial information that was complementary and synergistic to that carried by neurons, improving spatial position decoding when astrocytic signals were considered alongside neuronal ones. These results suggest that the complementary place dependence of localized astrocytic calcium signals may regulate clusters of nearby synapses, enabling dynamic, context-dependent variations in population coding within brain circuits.

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Lottem, Eran, Erez Gugig, and Rony Azouz. "Parallel coding schemes of whisker velocity in the rat's somatosensory system." Journal of Neurophysiology 113, no. 6 (March 15, 2015): 1784–99. http://dx.doi.org/10.1152/jn.00485.2014.

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The function of rodents' whisker somatosensory system is to transform tactile cues, in the form of vibrissa vibrations, into neuronal responses. It is well established that rodents can detect numerous tactile stimuli and tell them apart. However, the transformation of tactile stimuli obtained through whisker movements to neuronal responses is not well-understood. Here we examine the role of whisker velocity in tactile information transmission and its coding mechanisms. We show that in anaesthetized rats, whisker velocity is related to the radial distance of the object contacted and its own velocity. Whisker velocity is accurately and reliably coded in first-order neurons in parallel, by both the relative time interval between velocity-independent first spike latency of rapidly adapting neurons and velocity-dependent first spike latency of slowly adapting neurons. At the same time, whisker velocity is also coded, although less robustly, by the firing rates of slowly adapting neurons. Comparing first- and second-order neurons, we find similar decoding efficiencies for whisker velocity using either temporal or rate-based methods. Both coding schemes are sufficiently robust and hardly affected by neuronal noise. Our results suggest that whisker kinematic variables are coded by two parallel coding schemes and are disseminated in a similar way through various brain stem nuclei to multiple brain areas.

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7

Doron, Guy, and Michael Brecht. "What single-cell stimulation has told us about neural coding." Philosophical Transactions of the Royal Society B: Biological Sciences 370, no. 1677 (September 19, 2015): 20140204. http://dx.doi.org/10.1098/rstb.2014.0204.

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In recent years, single-cell stimulation experiments have resulted in substantial progress towards directly linking single-cell activity to movement and sensation. Recent advances in electrical recording and stimulation techniques have enabled control of single neuron spiking in vivo and have contributed to our understanding of neuronal coding schemes in the brain. Here, we review single neuron stimulation effects in different brain structures and how they vary with artificially inserted spike patterns. We briefly compare single neuron stimulation with other brain stimulation techniques. A key advantage of single neuron stimulation is the precise control of the evoked spiking patterns. Systematically varying spike patterns and measuring evoked movements and sensations enables ‘decoding’ of the single-cell spike patterns and provides insights into the readout mechanisms of sensory and motor cortical spikes.

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Hartig, Renée, David Wolf, Michael J. Schmeisser, and Wolfgang Kelsch. "Genetic influences of autism candidate genes on circuit wiring and olfactory decoding." Cell and Tissue Research 383, no. 1 (January 2021): 581–95. http://dx.doi.org/10.1007/s00441-020-03390-8.

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AbstractOlfaction supports a multitude of behaviors vital for social communication and interactions between conspecifics. Intact sensory processing is contingent upon proper circuit wiring. Disturbances in genetic factors controlling circuit assembly and synaptic wiring can lead to neurodevelopmental disorders, such as autism spectrum disorder (ASD), where impaired social interactions and communication are core symptoms. The variability in behavioral phenotype expression is also contingent upon the role environmental factors play in defining genetic expression. Considering the prevailing clinical diagnosis of ASD, research on therapeutic targets for autism is essential. Behavioral impairments may be identified along a range of increasingly complex social tasks. Hence, the assessment of social behavior and communication is progressing towards more ethologically relevant tasks. Garnering a more accurate understanding of social processing deficits in the sensory domain may greatly contribute to the development of therapeutic targets. With that framework, studies have found a viable link between social behaviors, circuit wiring, and altered neuronal coding related to the processing of salient social stimuli. Here, the relationship between social odor processing in rodents and humans is examined in the context of health and ASD, with special consideration for how genetic expression and neuronal connectivity may regulate behavioral phenotypes.

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Manwani, Amit, Peter N. Steinmetz, and Christof Koch. "The Impact of Spike Timing Variability on the Signal-Encoding Performance of Neural Spiking Models." Neural Computation 14, no. 2 (February 1, 2002): 347–67. http://dx.doi.org/10.1162/08997660252741158.

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It remains unclear whether the variability of neuronal spike trains in vivo arises due to biological noise sources or represents highly precise encoding of temporally varying synaptic input signals. Determining the variability of spike timing can provide fundamental insights into the nature of strategies used in the brain to represent and transmit information in the form of discrete spike trains. In this study, we employ a signal estimation paradigm to determine how variability in spike timing affects encoding of random time-varying signals. We assess this for two types of spiking models: an integrate-and-fire model with random threshold and a more biophysically realistic stochastic ion channel model. Using the coding fraction and mutual information as information-theoretic measures, we quantify the efficacy of optimal linear decoding of random inputs from the model outputs and study the relationship between efficacy and variability in the output spike train. Our findings suggest that variability does not necessarily hinder signal decoding for the biophysically plausible encoders examined and that the functional role of spiking variability depends intimately on the nature of the encoder and the signal processing task; variability can either enhance or impede decoding performance.

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Zeldenrust, Fleur, Boris Gutkin, and Sophie Denéve. "Efficient and robust coding in heterogeneous recurrent networks." PLOS Computational Biology 17, no. 4 (April 30, 2021): e1008673. http://dx.doi.org/10.1371/journal.pcbi.1008673.

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Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumptions: 1) every spike is decoded linearly and 2) the network aims to reduce the mean-squared error between the input and the estimate. From this we derive a class of predictive coding networks, that unifies encoding and decoding and in which we can investigate the difference between homogeneous networks and heterogeneous networks, in which each neurons represents different features and has different spike-generating properties. We find that in this framework, ‘type 1’ and ‘type 2’ neurons arise naturally and networks consisting of a heterogeneous population of different neuron types are both more efficient and more robust against correlated noise. We make two experimental predictions: 1) we predict that integrators show strong correlations with other integrators and resonators are correlated with resonators, whereas the correlations are much weaker between neurons with different coding properties and 2) that ‘type 2’ neurons are more coherent with the overall network activity than ‘type 1’ neurons.

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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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12

Novellino, A., P. D'Angelo, L. Cozzi, M. Chiappalone, V. Sanguineti, and S. Martinoia. "Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface." Computational Intelligence and Neuroscience 2007 (2007): 1–13. http://dx.doi.org/10.1155/2007/12725.

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One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason x201C;embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.

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13

Meyers, Ethan M., David J. Freedman, Gabriel Kreiman, Earl K. Miller, and Tomaso Poggio. "Dynamic Population Coding of Category Information in Inferior Temporal and Prefrontal Cortex." Journal of Neurophysiology 100, no. 3 (September 2008): 1407–19. http://dx.doi.org/10.1152/jn.90248.2008.

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Most electrophysiology studies analyze the activity of each neuron separately. While such studies have given much insight into properties of the visual system, they have also potentially overlooked important aspects of information coded in changing patterns of activity that are distributed over larger populations of neurons. In this work, we apply a population decoding method to better estimate what information is available in neuronal ensembles and how this information is coded in dynamic patterns of neural activity in data recorded from inferior temporal cortex (ITC) and prefrontal cortex (PFC) as macaque monkeys engaged in a delayed match-to-category task. Analyses of activity patterns in ITC and PFC revealed that both areas contain “abstract” category information (i.e., category information that is not directly correlated with properties of the stimuli); however, in general, PFC has more task-relevant information, and ITC has more detailed visual information. Analyses examining how information coded in these areas show that almost all category information is available in a small fraction of the neurons in the population. Most remarkably, our results also show that category information is coded by a nonstationary pattern of activity that changes over the course of a trial with individual neurons containing information on much shorter time scales than the population as a whole.

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Bouton, Sophie, Valérian Chambon, Rémi Tyrand, Adrian G. Guggisberg, Margitta Seeck, Sami Karkar, Dimitri van de Ville, and Anne-Lise Giraud. "Focal versus distributed temporal cortex activity for speech sound category assignment." Proceedings of the National Academy of Sciences 115, no. 6 (January 23, 2018): E1299—E1308. http://dx.doi.org/10.1073/pnas.1714279115.

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Percepts and words can be decoded from distributed neural activity measures. However, the existence of widespread representations might conflict with the more classical notions of hierarchical processing and efficient coding, which are especially relevant in speech processing. Using fMRI and magnetoencephalography during syllable identification, we show that sensory and decisional activity colocalize to a restricted part of the posterior superior temporal gyrus (pSTG). Next, using intracortical recordings, we demonstrate that early and focal neural activity in this region distinguishes correct from incorrect decisions and can be machine-decoded to classify syllables. Crucially, significant machine decoding was possible from neuronal activity sampled across different regions of the temporal and frontal lobes, despite weak or absent sensory or decision-related responses. These findings show that speech-sound categorization relies on an efficient readout of focal pSTG neural activity, while more distributed activity patterns, although classifiable by machine learning, instead reflect collateral processes of sensory perception and decision.

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Rao, Naveen G., and John P. Donoghue. "Cue to action processing in motor cortex populations." Journal of Neurophysiology 111, no. 2 (January 15, 2014): 441–53. http://dx.doi.org/10.1152/jn.00274.2013.

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The primary motor cortex (MI) commands motor output after kinematics are planned from goals, thought to occur in a larger premotor network. However, there is a growing body of evidence that MI is involved in processes beyond action generation, and neuronal subpopulations may perform computations related to cue-to-action processing. From multielectrode array recordings in awake behaving Macaca mulatta monkeys, our results suggest that early MI ensemble activity during goal-directed reaches is driven by target information when cues are closely linked in time to action. Single-neuron activity spanned cue presentation to movement, with the earliest responses temporally aligned to cue and the later responses better aligned to arm movements. Population decoding revealed that MI's coding of cue direction evolved temporally, likely going from cue to action generation. We confirmed that a portion of MI activity is related to visual target processing by showing changes in MI activity related to the extinguishing of a continuously pursued visual target. These findings support a view that MI is an integral part of a cue-to-action network for immediate responses to environmental stimuli.

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Murray, John D., Alberto Bernacchia, Nicholas A. Roy, Christos Constantinidis, Ranulfo Romo, and Xiao-Jing Wang. "Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex." Proceedings of the National Academy of Sciences 114, no. 2 (December 27, 2016): 394–99. http://dx.doi.org/10.1073/pnas.1619449114.

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Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain’s WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.

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Zhang, Kechen, Iris Ginzburg, Bruce L. McNaughton, and Terrence J. Sejnowski. "Interpreting Neuronal Population Activity by Reconstruction: Unified Framework With Application to Hippocampal Place Cells." Journal of Neurophysiology 79, no. 2 (February 1, 1998): 1017–44. http://dx.doi.org/10.1152/jn.1998.79.2.1017.

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Zhang, Kechen, Iris Ginzburg, Bruce L. McNaughton, and Terrence J. Sejnowski. Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. J. Neurophysiol. 79: 1017–1044, 1998. Physical variables such as the orientation of a line in the visual field or the location of the body in space are coded as activity levels in populations of neurons. Reconstruction or decoding is an inverse problem in which the physical variables are estimated from observed neural activity. Reconstruction is useful first in quantifying how much information about the physical variables is present in the population and, second, in providing insight into how the brain might use distributed representations in solving related computational problems such as visual object recognition and spatial navigation. Two classes of reconstruction methods, namely, probabilistic or Bayesian methods and basis function methods, are discussed. They include important existing methods as special cases, such as population vector coding, optimal linear estimation, and template matching. As a representative example for the reconstruction problem, different methods were applied to multi-electrode spike train data from hippocampal place cells in freely moving rats. The reconstruction accuracy of the trajectories of the rats was compared for the different methods. Bayesian methods were especially accurate when a continuity constraint was enforced, and the best errors were within a factor of two of the information-theoretic limit on how accurate any reconstruction can be and were comparable with the intrinsic experimental errors in position tracking. In addition, the reconstruction analysis uncovered some interesting aspects of place cell activity, such as the tendency for erratic jumps of the reconstructed trajectory when the animal stopped running. In general, the theoretical values of the minimal achievable reconstruction errors quantify how accurately a physical variable is encoded in the neuronal population in the sense of mean square error, regardless of the method used for reading out the information. One related result is that the theoretical accuracy is independent of the width of the Gaussian tuning function only in two dimensions. Finally, all the reconstruction methods considered in this paper can be implemented by a unified neural network architecture, which the brain feasibly could use to solve related problems.

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Aggelopoulos, Nikolaos C., Leonardo Franco, and Edmund T. Rolls. "Object Perception in Natural Scenes: Encoding by Inferior Temporal Cortex Simultaneously Recorded Neurons." Journal of Neurophysiology 93, no. 3 (March 2005): 1342–57. http://dx.doi.org/10.1152/jn.00553.2004.

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The firing of inferior temporal cortex neurons is tuned to objects and faces, and in a complex scene, their receptive fields are reduced to become similar to the size of an object being fixated. These two properties may underlie how objects in scenes are encoded. An alternative hypothesis suggests that visual perception requires the binding of features of the visual target through spike synchrony in a neuronal assembly. To examine possible contributions of firing synchrony of inferior temporal neurons, we made simultaneous recordings of the activity of several neurons while macaques performed a visual discrimination task. The stimuli were presented in either plain or complex backgrounds. The encoding of information of neurons was analyzed using a decoding algorithm. Ninety-four percent to 99% of the total information was available in the firing rate spike counts, and the contribution of spike timing calculated as stimulus-dependent synchronization (SDS) added only 1–6% of information to the total that was independent of the spike counts in the complex background. Similar results were obtained in the plain background. The quantitatively small contribution of spike timing to the overall information available in spike patterns suggests that information encoding about which stimulus was shown by inferior temporal neurons is achieved mainly by rate coding. Furthermore, it was shown that there was little redundancy (6%) between the information provided by the spike counts of the simultaneously recorded neurons, making spike counts an efficient population code with a high encoding capacity.

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Bi, Zedong, and Changsong Zhou. "Understanding the computation of time using neural network models." Proceedings of the National Academy of Sciences 117, no. 19 (April 27, 2020): 10530–40. http://dx.doi.org/10.1073/pnas.1921609117.

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To maximize future rewards in this ever-changing world, animals must be able to discover the temporal structure of stimuli and then anticipate or act correctly at the right time. How do animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multiseconds in working memory? How is temporal information processed concurrently with spatial information and decision making? Why are there strong neuronal temporal signals in tasks in which temporal information is not required? A systematic understanding of the underlying neural mechanisms is still lacking. Here, we addressed these problems using supervised training of recurrent neural network models. We revealed that neural networks perceive elapsed time through state evolution along stereotypical trajectory, maintain time intervals in working memory in the monotonic increase or decrease of the firing rates of interval-tuned neurons, and compare or produce time intervals by scaling state evolution speed. Temporal and nontemporal information is coded in subspaces orthogonal with each other, and the state trajectories with time at different nontemporal information are quasiparallel and isomorphic. Such coding geometry facilitates the decoding generalizability of temporal and nontemporal information across each other. The network structure exhibits multiple feedforward sequences that mutually excite or inhibit depending on whether their preferences of nontemporal information are similar or not. We identified four factors that facilitate strong temporal signals in nontiming tasks, including the anticipation of coming events. Our work discloses fundamental computational principles of temporal processing, and it is supported by and gives predictions to a number of experimental phenomena.

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Dechery, Joseph B., and Jason N. MacLean. "Emergent cortical circuit dynamics contain dense, interwoven ensembles of spike sequences." Journal of Neurophysiology 118, no. 3 (September 1, 2017): 1914–25. http://dx.doi.org/10.1152/jn.00394.2017.

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Temporal codes are theoretically powerful encoding schemes, but their precise form in the neocortex remains unknown in part because of the large number of possible codes and the difficulty in disambiguating informative spikes from statistical noise. A biologically plausible and computationally powerful temporal coding scheme is the Hebbian assembly phase sequence (APS), which predicts reliable propagation of spikes between functionally related assemblies of neurons. Here, we sought to measure the inherent capacity of neocortical networks to produce reliable sequences of spikes, as would be predicted by an APS code. To record microcircuit activity, the scale at which computation is implemented, we used two-photon calcium imaging to densely sample spontaneous activity in murine neocortical networks ex vivo. We show that the population spike histogram is sufficient to produce a spatiotemporal progression of activity across the population. To more comprehensively evaluate the capacity for sequential spiking that cannot be explained by the overall population spiking, we identify statistically significant spike sequences. We found a large repertoire of sequence spikes that collectively comprise the majority of spiking in the circuit. Sequences manifest probabilistically and share neuron membership, resulting in unique ensembles of interwoven sequences characterizing individual spatiotemporal progressions of activity. Distillation of population dynamics into its constituent sequences provides a way to capture trial-to-trial variability and may prove to be a powerful decoding substrate in vivo. Informed by these data, we suggest that the Hebbian APS be reformulated as interwoven sequences with flexible assembly membership due to shared overlapping neurons. NEW & NOTEWORTHY Neocortical computation occurs largely within microcircuits comprised of individual neurons and their connections within small volumes (<500 μm3). We found evidence for a long-postulated temporal code, the Hebbian assembly phase sequence, by identifying repeated and co-occurring sequences of spikes. Variance in population activity across trials was explained in part by the ensemble of active sequences. The presence of interwoven sequences suggests that neuronal assembly structure can be variable and is determined by previous activity.

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Habeeb, Rawnaq. "Coding-Decoding Ternary Logic." Iraqi Journal for Electrical and Electronic Engineering 10, no. 1 (June 1, 2014): 24–32. http://dx.doi.org/10.37917/ijeee.10.1.3.

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In this paper ternary logic is encoded into binary and certain processes were conducted on binary logic after which the binary is decoded to ternary. General purpose digital devices were used and the circuit is designed back to front starting from ternary logic provided by transistor pairs at output side back to front end. This provided easier design technique in this particular paper. Practical and simulation results are recorded.

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A. Habeeb, Rawnaq. "Coding-Decoding Ternary Logic." Iraqi Journal for Electrical And Electronic Engineering 10, no. 1 (June 28, 2014): 24–32. http://dx.doi.org/10.33762/eeej.2014.93015.

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Theunissen, F. E., and J. P. Miller. "Representation of sensory information in the cricket cercal sensory system. II. Information theoretic calculation of system accuracy and optimal tuning-curve widths of four primary interneurons." Journal of Neurophysiology 66, no. 5 (November 1, 1991): 1690–703. http://dx.doi.org/10.1152/jn.1991.66.5.1690.

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1. Principles of information theory were used to calculate the limit of accuracy achievable by a subset of the wind-sensitive primary interneurons in the cricket cercal sensory system. For these calculations, an ensemble of four neurons was treated as an information channel, which encoded the direction of air-current stimuli for a defined range of air-current velocities. The specific information theoretic parameter that was calculated was the ,transin-formation- or ,mutual information- between the air-current directions and the neuronal spike trains, which were characterized in the preceding report. Under the assumptions used for these calculations, the ensemble of four interneurons was demonstrated to be capable of encoding between 4.2 and 3.5 bits of information about wind direction. This corresponds to an average directional accuracy of 4.7 and 7.7 degrees, respectively. 2. The same principles were applied to estimate the extent to which any variation in the width of the tuning curves would affect the transfer of information. As the widths of simulated tuning curves were varied, the mean ensemble accuracy showed a clear global maximum. This maximum corresponds to tuning curves widths of 110 degrees wide (at half maximum), which was remarkably close to the actual mean widths of the tuning curves observed in the cricket of 130 degrees. 3. The effect of varying the parametric ,spacing- of the tuning curves within the stimulus range was also examined through a series of simulations. The configuration allowing the maximum information transfer corresponded to equal spacing of the tuning curves around the stimulus range (i.e., 90 degrees separation of peak sensitivity points). This theoretically optimum spacing corresponded exactly to the values observed in the experiments presented in the preceding report. 4. These simulations also showed that the degradation in the accuracy resulting from a shift in the tuning-curve spacing would depend on the plasticity of the higher order decoder of directional information. If there were no plasticity in the interneurons making up the higher order decoder, then the accuracy would be degraded by 50% for a mean tuning-curve shift of only 3.5 degrees. However, if the higher order decoding network were capable of being reoptimized to any arbitrary shift in tuning curves, the degradation in attainable accuracy would be much less severe as shifts of up to 10 degrees would result in virtually no degradation in the accuracy. 5. From these results, two general conclusions can be drawn about the coding of specific stimulus parameters by arrays of sensory cells.(ABSTRACT TRUNCATED AT 400 WORDS)

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24

Takashima, Makoto, and Yoshiaki Asakawa. "Method for coding/decoding, coding/decoding device, and videoconferencing apparatus using such device." Journal of the Acoustical Society of America 108, no. 2 (2000): 476. http://dx.doi.org/10.1121/1.429544.

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25

Sarkar, Sahotra. "Decoding "Coding": Information and DNA." BioScience 46, no. 11 (December 1996): 857–64. http://dx.doi.org/10.2307/1312971.

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Taniguchi, Tomohiko, and Mark Johnson. "Speech coding and decoding system." Journal of the Acoustical Society of America 105, no. 5 (1999): 2554. http://dx.doi.org/10.1121/1.426938.

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27

Akamine, Masami, and Kimio Miseki. "Speech coding and decoding apparatus." Journal of the Acoustical Society of America 96, no. 5 (November 1994): 3210. http://dx.doi.org/10.1121/1.411226.

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28

Papoutsi, Athanasia, George Kastellakis, Maria Psarrou, Stelios Anastasakis, and Panayiota Poirazi. "Coding and decoding with dendrites." Journal of Physiology-Paris 108, no. 1 (February 2014): 18–27. http://dx.doi.org/10.1016/j.jphysparis.2013.05.003.

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29

Halpern, Bruce P. "Sensory coding, decoding, and representations." Physiology & Behavior 69, no. 1-2 (April 2000): 115–18. http://dx.doi.org/10.1016/s0031-9384(00)00195-5.

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30

Tanigichi, Tomohiko. "Speech coding and decoding system." Journal of the Acoustical Society of America 96, no. 1 (July 1994): 620. http://dx.doi.org/10.1121/1.410422.

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Borkiewicz, Lidia, Joanna Kalafut, Karolina Dudziak, Alicja Przybyszewska-Podstawka, and Ilona Telejko. "Decoding LncRNAs." Cancers 13, no. 11 (May 27, 2021): 2643. http://dx.doi.org/10.3390/cancers13112643.

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Non-coding RNAs (ncRNAs) have been considered as unimportant additions to the transcriptome. Yet, in light of numerous studies, it has become clear that ncRNAs play important roles in development, health and disease. Long-ignored, long non-coding RNAs (lncRNAs), ncRNAs made of more than 200 nucleotides have gained attention due to their involvement as drivers or suppressors of a myriad of tumours. The detailed understanding of some of their functions, structures and interactomes has been the result of interdisciplinary efforts, as in many cases, new methods need to be created or adapted to characterise these molecules. Unlike most reviews on lncRNAs, we summarize the achievements on lncRNA studies by taking into consideration the approaches for identification of lncRNA functions, interactomes, and structural arrangements. We also provide information about the recent data on the involvement of lncRNAs in diseases and present applications of these molecules, especially in medicine.

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32

Inoue, Takeo. "Audio signal coding and decoding device." Journal of the Acoustical Society of America 101, no. 2 (February 1997): 659. http://dx.doi.org/10.1121/1.419444.

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33

He, S., C. Liu, G. Skogerbo, H. Zhao, J. Wang, T. Liu, B. Bai, Y. Zhao, and R. Chen. "NONCODE v2.0: decoding the non-coding." Nucleic Acids Research 36, Database (December 23, 2007): D170—D172. http://dx.doi.org/10.1093/nar/gkm1011.

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34

Wake, Yasuhiro. "Pause compressing speech coding/decoding apparatus." Journal of the Acoustical Society of America 103, no. 6 (June 1998): 3136. http://dx.doi.org/10.1121/1.423098.

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35

Skinner, G. K. "Coding (and decoding) coded mask telescopes." Experimental Astronomy 6, no. 4 (1995): 1–7. http://dx.doi.org/10.1007/bf00419252.

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36

McBain, C. J. "Decoding the Neuronal Tower of Babel." Science 338, no. 6106 (October 25, 2012): 482–83. http://dx.doi.org/10.1126/science.1230338.

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37

Whitney, D. "Neuronal coding and robotics." Science 237, no. 4812 (July 17, 1987): 300–302. http://dx.doi.org/10.1126/science.3603020.

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38

Hayashi, Nobuhiro. "A coding and decoding method of track address with viterbi decoding." Electronics and Communications in Japan (Part II: Electronics) 79, no. 4 (1996): 67–76. http://dx.doi.org/10.1002/ecjb.4420790408.

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39

Jiang, Hong Rui, and Kyung Sup Kwak. "Space–Time Block Coding Iterative Multiuser Receiver." Journal of Circuits, Systems and Computers 12, no. 01 (February 2003): 19–30. http://dx.doi.org/10.1142/s0218126603000817.

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We present a multiuser receiver for CDMA systems with the combination of turbo channel coding and space–time block coding. A turbo scheme based on multiuser detection, soft interference cancellation and decoding is provided, and the algorithms for space–time decoding and separately interference suppressing are derived in this paper. The multiuser detection consists of multiuser interference suppression and single-user space–time decoding. Then we develop the iterative multiuser receiver based on the soft estimates of the interfering users' symbols. Moreover, simulation is given to verify the effectiveness of the multiuser receiver.

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40

Semerenko, Vasyl, and Oleksandr Voinalovich. "The simplification of computationals in error correction coding." Technology audit and production reserves 3, no. 2(59) (June 30, 2021): 24–28. http://dx.doi.org/10.15587/2706-5448.2021.233656.

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The object of research is the processes of error correction transformation of information in automated systems. The research is aimed at reducing the complexity of decoding cyclic codes by combining modern mathematical models and practical tools. The main prerequisite for the complication of computations in deterministic linear error-correcting codes is the use of the algebraic representation as the main mathematical apparatus for these types of codes. Despite the universalism of the algebraic approach, its main drawback is the impossibility of taking into account the characteristic features of all subclasses of linear codes. In particular, the cyclic property is not taken into account at all for cyclic codes. Taking this property into account, one can go to a fundamentally different mathematical representation of cyclic codes – the theory of linear automata in Galois fields (linear finite-state machine). For the automaton representation of cyclic codes, it is proved that the problem of syndromic decoding of these codes in the general case is an NP-complete problem. However, if to use the proposed hierarchical approach to problems of complexity, then on its basis it is possible to carry out a more accurate analysis of the growth of computational complexity. Correction of single errors during one time interval (one iteration) of decoding has a linear decoding complexity on the length of the codeword, and error correction during m iterations of permutations of codeword bits has a polynomial complexity. According to three subclasses of cyclic codes, depending on the complexity of their decoding: easy decoding (linear complexity), iteratively decoded (polynomial complexity), complicate decoding (exponential complexity). Practical ways to reduce the complexity of computations are considered: alternate use of probabilistic and deterministic linear codes, simplification of software and hardware implementation by increasing the decoding time, use of interleaving. A method of interleaving is proposed, which makes it possible to simultaneously generate the burst errors and replace them with single errors. The mathematical apparatus of linear automata allows solving together the indicated problems of error correction coding.

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41

Hu, Lehua. "Coding and Decoding Optimization of Remote Video Surveillance Systems." International Journal of Grid and High Performance Computing 15, no. 2 (February 16, 2023): 1–15. http://dx.doi.org/10.4018/ijghpc.318405.

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In order to solve the problems of high distortion rate and low decoding efficiency of the decoded video when the current coding and decoding methods are used to encode and decode the remote video monitoring system, considering the local area network, research on the optimization method of the coding and decoding of the remote video monitoring system is proposed. The local area network is used to collect image information, to process, and to output the image information. By preprocessing the remote video monitoring system, the low frame rate remote video monitoring system is decoded in parallel. The motion information of the lost frame is estimated to realize the fast coding and decoding of the remote video monitoring system. The experimental results show that the proposed method has low distortion rate and high decoding efficiency and has high practical value.

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42

Petrova, Yulia, and Irina Kisel. "Communication process: coding and decoding of communication." Humanities and Social Sciences 85, no. 2 (April 1, 2021): 40–47. http://dx.doi.org/10.18522/2070-1403-2021-85-2-40-47.

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Nguyen, Trung Thanh, and Lutz Lampe. "Channel Coding Diversity with Mismatched Decoding Metrics." IEEE Communications Letters 15, no. 9 (September 2011): 916–18. http://dx.doi.org/10.1109/lcomm.2011.071311.111110.

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Scarlett, Jonathan, Alfonso Martinez, and Albert Guillen i Fabregas. "Multiuser Random Coding Techniques for Mismatched Decoding." IEEE Transactions on Information Theory 62, no. 7 (July 2016): 3950–70. http://dx.doi.org/10.1109/tit.2016.2555317.

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Somekh-Baruch, Anelia, and Neri Merhav. "Exact Random Coding Exponents for Erasure Decoding." IEEE Transactions on Information Theory 57, no. 10 (October 2011): 6444–54. http://dx.doi.org/10.1109/tit.2011.2165826.

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46

Wacquier, Benjamin, Valérie Voorsluijs, Laurent Combettes, and Geneviève Dupont. "Coding and decoding of oscillatory Ca2+ signals." Seminars in Cell & Developmental Biology 94 (October 2019): 11–19. http://dx.doi.org/10.1016/j.semcdb.2019.01.008.

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47

Shokurov, A. V. "Image coding with optimal decoding possible afterwards." Journal of Mathematical Sciences 156, no. 2 (January 2009): 359–80. http://dx.doi.org/10.1007/s10958-008-9273-2.

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48

Iyer, Namrata. "Decoding non-coding DNA: Trash or treasure?" Resonance 16, no. 4 (April 2011): 333–40. http://dx.doi.org/10.1007/s12045-011-0039-7.

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49

Pijlman, Gorben P. "Flavivirus RNAi suppression: decoding non-coding RNA." Current Opinion in Virology 7 (August 2014): 55–60. http://dx.doi.org/10.1016/j.coviro.2014.04.002.

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Pittalis, Marios, and Constantinos Christou. "Coding and decoding representations of 3D shapes." Journal of Mathematical Behavior 32, no. 3 (September 2013): 673–89. http://dx.doi.org/10.1016/j.jmathb.2013.08.004.

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