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Relevant bibliographies by topics / Neuronal coding and decoding

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Journal articles

Academic literature on the topic 'Neuronal coding and decoding'

Author: Grafiati

Published: 14 March 2023

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Journal articles on the topic "Neuronal coding and decoding"

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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3

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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4

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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6

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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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 (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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9

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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