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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 (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 compar

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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 (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

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Koyama, Shinsuke. "On the Relation Between Encoding and Decoding of Neuronal Spikes." Neural Computation 24, no. 6 (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

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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 (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 bou

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5

Curreli, Sebastiano, Jacopo Bonato, Sara Romanzi, Stefano Panzeri, and Tommaso Fellin. "Complementary encoding of spatial information in hippocampal astrocytes." PLOS Biology 20, no. 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 occurr

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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 (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 vel

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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 (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

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8

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 (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 clini

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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 (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 i

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10

Zeldenrust, Fleur, Boris Gutkin, and Sophie Denéve. "Efficient and robust coding in heterogeneous recurrent networks." PLOS Computational Biology 17, no. 4 (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 inves

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11

Zavitz, Elizabeth, and Nicholas S. C. Price. "Weighting neurons by selectivity produces near-optimal population codes." Journal of Neurophysiology 121, no. 5 (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 neuro

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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 a

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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 (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 (P

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Bouton, Sophie, Valérian Chambon, Rémi Tyrand, et al. "Focal versus distributed temporal cortex activity for speech sound category assignment." Proceedings of the National Academy of Sciences 115, no. 6 (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

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15

Rao, Naveen G., and John P. Donoghue. "Cue to action processing in motor cortex populations." Journal of Neurophysiology 111, no. 2 (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 spa

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16

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 (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

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17

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 (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 a

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18

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 (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

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19

Bi, Zedong, and Changsong Zhou. "Understanding the computation of time using neural network models." Proceedings of the National Academy of Sciences 117, no. 19 (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

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20

Dechery, Joseph B., and Jason N. MacLean. "Emergent cortical circuit dynamics contain dense, interwoven ensembles of spike sequences." Journal of Neurophysiology 118, no. 3 (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 pre

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21

Habeeb, Rawnaq. "Coding-Decoding Ternary Logic." Iraqi Journal for Electrical and Electronic Engineering 10, no. 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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22

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

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23

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 (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 r

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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 (1996): 857–64. http://dx.doi.org/10.2307/1312971.

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26

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 (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 (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 (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 (1994): 620. http://dx.doi.org/10.1121/1.410422.

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31

Borkiewicz, Lidia, Joanna Kalafut, Karolina Dudziak, Alicja Przybyszewska-Podstawka, and Ilona Telejko. "Decoding LncRNAs." Cancers 13, no. 11 (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 cha

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32

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

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33

He, S., C. Liu, G. Skogerbo, et al. "NONCODE v2.0: decoding the non-coding." Nucleic Acids Research 36, Database (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 (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 (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 (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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Jiang, Hong Rui, and Kyung Sup Kwak. "Space–Time Block Coding Iterative Multiuser Receiver." Journal of Circuits, Systems and Computers 12, no. 01 (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 e

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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) (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 o

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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 (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

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Petrova, Yulia, and Irina Kisel. "Communication process: coding and decoding of communication." Humanities and Social Sciences 85, no. 2 (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 (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 (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 (2011): 6444–54. http://dx.doi.org/10.1109/tit.2011.2165826.

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

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

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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 (2013): 673–89. http://dx.doi.org/10.1016/j.jmathb.2013.08.004.

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