Neural Population Coding of Stimulus Features

In many cortical and subcortical areas, neurons are known to modulate their average firing rate in response to certain external stimulus features. It is widely believed that information about the stimulus features is coded by a weighted average of the neural responses. Recent theoretical studies have shown that the information capacity of such a coding scheme is very limited in the presence of the experimentally observed pairwise correlations. However, central to the analysis of these studies was the assumption of a homogeneous population of neurons. Experimental findings show a considerable measure of heterogeneity in the response properties of different neurons. In this study, we investigate the effect of neuronal heterogeneity on the information capacity of a correlated population of neurons. We show that information capacity of a heterogeneous network is not limited by the correlated noise, but scales linearly with the number of cells in the population. This information cannot be extracted by the population vector readout, whose accuracy is greatly suppressed by the correlated noise. On the other hand, we show that an optimal linear readout that takes into account the neuronal heterogeneity can extract most of this information. We study analytically the nature of the dependence of the optimal linear readout weights on the neuronal diversity. We show that simple online learning can generate readout weights with the appropriate dependence on the neuronal diversity, thereby yielding efficient readout.

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Difficulty of Singularity in Population Coding

Neural Computation ◽

10.1162/0899766053429426 ◽

2005 ◽

Vol 17 (4) ◽

pp. 839-858 ◽

Cited By ~ 25

Author(s):

Shun-ichi Amari ◽

Hiroyuki Nakahara

Keyword(s):

Fisher Information ◽

Regularity Condition ◽

Information Matrix ◽

Population Coding ◽

Neural Population ◽

Binding Problem ◽

Synchronous Firing ◽

Novel Method ◽

Pathological Behavior ◽

Pathological Case

Fisher information has been used to analyze the accuracy of neural population coding. This works well when the Fisher information does not degenerate, but when two stimuli are presented to a population of neurons, a singular structure emerges by their mutual interactions. In this case, the Fisher information matrix degenerates, and the regularity condition ensuring the Cramér-Rao paradigm of statistics is violated. An animal shows pathological behavior in such a situation. We present a novel method of statistical analysis to understand information in population coding in which algebraic singularity plays a major role. The method elucidates the nature of the pathological case by calculating the Fisher information. We then suggest that synchronous firing can resolve singularity and show a method of analyzing the binding problem in terms of the Fisher information. Our method integrates a variety of disciplines in population coding, such as nonregular statistics, Bayesian statistics, singularity in algebraic geometry, and synchronous firing, under the theme of Fisher information.

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Optimal Tuning Widths in Population Coding of Periodic Variables

Neural Computation ◽

10.1162/neco.2006.18.7.1555 ◽

2006 ◽

Vol 18 (7) ◽

pp. 1555-1576 ◽

Cited By ~ 13

Author(s):

Marcelo A. Montemurro ◽

Stefano Panzeri

Keyword(s):

Cortical Neurons ◽

Neuronal Population ◽

Population Coding ◽

Model Parameters ◽

Population Activity ◽

Sensory Stimuli ◽

Tuning Curves ◽

Periodic Stimulus ◽

Tuning Width ◽

Stimulus Features

We study the relationship between the accuracy of a large neuronal population in encoding periodic sensory stimuli and the width of the tuning curves of individual neurons in the population. By using general simple models of population activity, we show that when considering one or two periodic stimulus features, a narrow tuning width provides better population encoding accuracy. When encoding more than two periodic stimulus features, the information conveyed by the population is instead maximal for finite values of the tuning width. These optimal values are only weakly dependent on model parameters and are similar to the width of tuning to orientation ormotion direction of real visual cortical neurons. A very large tuning width leads to poor encoding accuracy, whatever the number of stimulus features encoded. Thus, optimal coding of periodic stimuli is different from that of nonperiodic stimuli, which, as shown in previous studies, would require infinitely large tuning widths when coding more than two stimulus features.

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Neural population coding of sound level adapts to stimulus statistics

Nature Neuroscience ◽

10.1038/nn1541 ◽

2005 ◽

Vol 8 (12) ◽

pp. 1684-1689 ◽

Cited By ~ 288

Author(s):

Isabel Dean ◽

Nicol S Harper ◽

David McAlpine

Keyword(s):

Sound Level ◽

Population Coding ◽

Neural Population

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When does recurrent connectivity improve neural population coding?

BMC Neuroscience ◽

10.1186/1471-2202-15-s1-p49 ◽

2014 ◽

Vol 15 (S1) ◽

Author(s):

Joel Zylberberg ◽

Eric Shea-Brown

Keyword(s):

Population Coding ◽

Neural Population

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Neuronal correlations in MT and MST impair population decoding of opposite directions of random dot motion

10.1101/267732 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tristan A. Chaplin ◽

Maureen A. Hagan ◽

Benjamin J. Allitt ◽

Leo L. Lui

Keyword(s):

Population Coding ◽

Correlation Structure ◽

Single Neurons ◽

Electrode Arrays ◽

Preferred Direction ◽

Dot Motion ◽

Random Dot Patterns ◽

Stimulus Features ◽

Random Dot Motion ◽

Spiking Activity

AbstractThe study of neuronal responses to random-dot motion patterns has provided some of the most valuable insights into how the activity of neurons is related to perception. In the opposite directions of motion paradigm, the motion signal strength is decreased by manipulating the coherence of random dot patterns to examine how well the activity of single neurons represents the direction of motion. To extend this paradigm to populations of neurons, studies have used modelling based on data from pairs of neurons, but several important questions require further investigation with larger neuronal datasets. We recorded neuronal populations in the middle temporal (MT) and medial superior temporal (MST) areas of anaesthetized marmosets with electrode arrays, while varying the coherence of random dot patterns in two opposite directions of motion (left and right). Using the spike rates of simultaneously recorded neurons, we decoded the direction of motion at each level of coherence with linear classifiers. We found that the presence of correlations had a detrimental effect to decoding performance, but that learning the correlation structure produced better decoding performance compared to decoders that ignored the correlation structure. We also found that reducing motion coherence increased neuronal correlations, but decoders did not need to be optimized for each coherence level. Finally, we showed that decoder weights depend of left-right selectivity at 100% coherence, rather than the preferred direction. These results have implications for understanding how the information encoded by populations of neurons is affected by correlations in spiking activity.Significance StatementMany studies have examined how the spiking activity of single neurons can encode stimulus features, such the direction of motion of visual stimuli. However, majority of such studies to date have only recorded from a small number of neurons at the same time, meaning that one cannot adequately account for the trial-to-trial correlations in spiking activity between neurons. Using multi-channel recordings, we were able to measure the neuronal correlations, and their effects on population coding of stimulus features. Our results have implications on the way which neural populations must be readout in order to maximize information.

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A signature of neural coding at human perceptual limits

10.1101/051714 ◽

2016 ◽

Author(s):

Paul M Bays

Keyword(s):

Neural Coding ◽

Empirical Relationship ◽

Human Perception ◽

Population Coding ◽

Neural Population ◽

Perceptual Awareness ◽

Neural Basis ◽

Visual Neurons ◽

Error Magnitude ◽

Subjective Confidence

AbstractSimple visual features, such as orientation, are thought to be represented in the spiking of visual neurons using population codes. I show that optimal decoding of such activity predicts characteristic deviations from the normal distribution of errors at low gains. Examining human perception of orientation stimuli, I show that these predicted deviations are present at near-threshold levels of contrast. The findings may provide a neural-level explanation for the appearance of a threshold in perceptual awareness, whereby stimuli are categorized as seen or unseen. As well as varying in error magnitude, perceptual judgments differ in certainty about what was observed. I demonstrate that variations in the total spiking activity of a neural population can account for the empirical relationship between subjective confidence and precision. These results establish population coding and decoding as the neural basis of perception and perceptual confidence.

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