scholarly journals Biases in multivariate neural population codes

2017 ◽  
Author(s):  
Sander W. Keemink ◽  
Mark C. W. van Rossum
Keyword(s):  
Neural Coding ◽  
Large Population ◽  
Neural Population ◽  
Strong Correlations ◽  
Behavioral Experiments ◽  
Homogeneous Population ◽  
Tuning Curves ◽  
Neural Noise ◽  
Process Framework ◽  
System Information

AbstractThroughout the nervous system information is typically coded in activity distributed over large population of neurons with broad tuning curves. In idealized situations where a single, continuous stimulus is encoded in a homogeneous population code, the value of an encoded stimulus can be read out without bias. Here we find that when multiple stimuli are simultaneously coded in the population, biases in the estimates of the stimuli and strong correlations between estimates can emerge. Although bias produced via this novel mechanism can be reduced by competitive coding and disappears in the complete absence of noise, the bias diminishes only slowly as a function of neural noise level. A Gaussian Process framework allows for accurate calculation of the bias and shows that a bimodal estimate distribution underlies the bias. The results have implications for neural coding and behavioral experiments.

scholarly journals Unconscious Biases in Neural Populations Coding Multiple Stimuli

2018 ◽  
Vol 30 (12) ◽  
pp. 3168-3188 ◽  
Author(s):  
Sander W. Keemink ◽  
Dharmesh V. Tailor ◽  
Mark C. W. van Rossum
Keyword(s):  
Neural Coding ◽  
Low Noise ◽  
Neural Representation ◽  
Noise Levels ◽  
Population Code ◽  
Behavioral Experiments ◽  
Homogeneous Population ◽  
Motion Patterns ◽  
Neural Populations ◽  
System Information

Throughout the nervous system, information is commonly coded in activity distributed over populations of neurons. In idealized situations where a single, continuous stimulus is encoded in a homogeneous population code, the value of the encoded stimulus can be read out without bias. However, in many situations, multiple stimuli are simultaneously present; for example, multiple motion patterns might overlap. Here we find that when multiple stimuli that overlap in their neural representation are simultaneously encoded in the population, biases in the read-out emerge. Although the bias disappears in the absence of noise, the bias is remarkably persistent at low noise levels. The bias can be reduced by competitive encoding schemes or by employing complex decoders. To study the origin of the bias, we develop a novel general framework based on gaussian processes that allows an accurate calculation of the estimate distributions of maximum likelihood decoders, and reveals that the distribution of estimates is bimodal for overlapping stimuli. The results have implications for neural coding and behavioral experiments on, for instance, overlapping motion patterns.

scholarly journals Theory of neural coding predicts an upper bound on estimates of memory variability

2019 ◽  
Author(s):  
Robert Taylor ◽  
Paul M Bays
Keyword(s):  
Neural Coding ◽  
Normal Component ◽  
Population Coding ◽  
Neural Population ◽  
Long Term Memory ◽  
Large Set ◽  
Behavioral Experiments ◽  
Short Delay ◽  
Neuronal Populations ◽  
Tuning Width

AbstractObservers reproducing elementary visual features from memory after a short delay produce errors consistent with the encoding-decoding properties of neural populations. While inspired by electrophysiological observations of sensory neurons in cortex, the population coding account of these errors is based on a mathematical idealization of neural response functions that abstracts away most of the heterogeneity and complexity of real neuronal populations. Here we examine a more physiologically grounded model based on the tuning of a large set of neurons recorded in macaque V1, and show that key predictions of the idealized model are preserved. Both models predict long-tailed distributions of error when memory resources are taxed, as observed empirically in behavioral experiments and commonly approximated with a mixture of normal and uniform error components. Specifically, for an idealized homogeneous neural population, the width of the fitted normal distribution cannot exceed the average tuning width of the component neurons, and this also holds to a good approximation for more biologically realistic populations. Examining eight published studies of orientation recall, we find a consistent pattern of results suggestive of a median tuning width of approximately 20 degrees, which compares well with neurophysiological observations. The finding that estimates of variability obtained by the normal-plus-uniform mixture method are bounded from above leads us to reevaluate previous studies that interpreted a saturation in width of the normal component as evidence for fundamental limits on the precision of perception, working memory and long-term memory.

scholarly journals The Geometry of Information Coding in Correlated Neural Populations

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Rava Azeredo da Silveira ◽  
Fred Rieke
Keyword(s):  
Neural Coding ◽  
Neural Population ◽  
Annual Review ◽  
Publication Date ◽  
Information Coding ◽  
Population Code ◽  
New Approach ◽  
Neural Populations ◽  
The Brain ◽  
Theoretical Developments

Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Neural population encoding and decoding of sound source location across sound level in the rabbit inferior colliculus

2016 ◽  
Vol 115 (1) ◽  
pp. 193-207 ◽  
Author(s):  
Mitchell L. Day ◽  
Bertrand Delgutte
Keyword(s):  
Inferior Colliculus ◽  
Sound Source ◽  
Auditory Pathway ◽  
Relevant Information ◽  
Sound Level ◽  
Neural Population ◽  
Linear Transformations ◽  
Tuning Curves ◽  
Azimuthal Position ◽  
Sound Levels

At lower levels of sensory processing, the representation of a stimulus feature in the response of a neural population can vary in complex ways across different stimulus intensities, potentially changing the amount of feature-relevant information in the response. How higher-level neural circuits could implement feature decoding computations that compensate for these intensity-dependent variations remains unclear. Here we focused on neurons in the inferior colliculus (IC) of unanesthetized rabbits, whose firing rates are sensitive to both the azimuthal position of a sound source and its sound level. We found that the azimuth tuning curves of an IC neuron at different sound levels tend to be linear transformations of each other. These transformations could either increase or decrease the mutual information between source azimuth and spike count with increasing level for individual neurons, yet population azimuthal information remained constant across the absolute sound levels tested (35, 50, and 65 dB SPL), as inferred from the performance of a maximum-likelihood neural population decoder. We harnessed evidence of level-dependent linear transformations to reduce the number of free parameters in the creation of an accurate cross-level population decoder of azimuth. Interestingly, this decoder predicts monotonic azimuth tuning curves, broadly sensitive to contralateral azimuths, in neurons at higher levels in the auditory pathway.

Quantitative Analysis of the Responses of V1 Neurons to Horizontal Disparity in Dynamic Random-Dot Stereograms

2002 ◽  
Vol 87 (1) ◽  
pp. 191-208 ◽  
Author(s):  
S.J.D. Prince ◽  
A. D. Pointon ◽  
B. G. Cumming ◽  
A. J. Parker
Keyword(s):  
Spatial Frequency ◽  
Tuning Curve ◽  
Ocular Dominance ◽  
Large Population ◽  
Gaussian Function ◽  
Energy Model ◽  
Horizontal Disparity ◽  
Orientation Preference ◽  
Tuning Curves ◽  
Random Dot Stereograms

Horizontal disparity tuning for dynamic random-dot stereograms was investigated for a large population of neurons ( n = 787) in V1 of the awake macaque. Disparity sensitivity was quantified using a measure of the discriminability of the maximum and minimum points on the disparity tuning curve. This measure and others revealed a continuum of selectivity rather than separate populations of disparity- and nondisparity-sensitive neurons. Although disparity sensitivity was correlated with the degree of direction tuning, it was not correlated with other significant neuronal properties, including preferred orientation and ocular dominance. In accordance with the Gabor energy model, tuning curves for horizontal disparity were adequately described by Gabor functions when the neuron's orientation preference was near vertical. For neurons with orientation preferences near to horizontal, a Gaussian function was more frequently sufficient. The spatial frequency of the Gabor function that described the disparity tuning was weakly correlated with measurements of the spatial frequency and orientation preference of the neuron for drifting sinusoidal gratings. Energy models make several predictions about the relationship between the response rates to monocular and binocular dot patterns. Few of the predictions were fulfilled exactly, although the observations can be reconciled with the energy model by simple modifications. These same modifications also provide an account of the observed continuum in strength of disparity selectivity. A weak correlation between the disparity sensitivity of simultaneously recorded single- and multiunit data were revealed as well as a weak tendency to show similar disparity preferences. This is compatible with a degree of local clustering for disparity sensitivity in V1, although this is much weaker than that reported in area MT.

scholarly journals Modelling the neural code in large populations of correlated neurons

2020 ◽  
Author(s):  
Sacha Sokoloski ◽  
Amir Aschner ◽  
Ruben Coen-Cagli
Keyword(s):  
Neural Coding ◽  
Large Scale ◽  
Neural Code ◽  
Neural Population ◽  
Population Activity ◽  
Fundamental Properties ◽  
Wide Range ◽  
Large Populations ◽  
Scale Population ◽  
Quantitative Understanding

AbstractThe activity of a neural population encodes information about the stimulus that caused it, and decoding population activity reveals how neural circuits process that information. Correlations between neurons strongly impact both encoding and decoding, yet we still lack models that simultaneously capture stimulus encoding by large populations of correlated neurons and allow for accurate decoding of stimulus information, thus limiting our quantitative understanding of the neural code. To address this, we propose a class of models of large-scale population activity based on the theory of exponential family distributions. We apply our models to macaque primary visual cortex (V1) recordings, and show they capture a wide range of response statistics, facilitate accurate Bayesian decoding, and provide interpretable representations of fundamental properties of the neural code. Ultimately, our framework could allow researchers to quantitatively validate predictions of theories of neural coding against both large-scale response recordings and cognitive performance.

scholarly journals A signature of neural coding at human perceptual limits

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.

scholarly journals Learning shapes cortical dynamics to enhance integration of relevant sensory input

2021 ◽  
Author(s):  
Angus Chadwick ◽  
Adil Khan ◽  
Jasper Poort ◽  
Antonin Blot ◽  
Sonja Hofer ◽  
...  
Keyword(s):  
Neural Coding ◽  
Sensory Input ◽  
Temporal Dynamics ◽  
Network Dynamics ◽  
Sensory Information ◽  
Neural Population ◽  
Cortical Circuits ◽  
Population Activity ◽  
Cortical Dynamics ◽  
Network Output

Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity amongst neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input.

Neural coding of quality of complex olfactory stimuli in lobsters

1988 ◽  
Vol 60 (1) ◽  
pp. 303-324 ◽  
Author(s):  
M. N. Girardot ◽  
C. D. Derby
Keyword(s):  
Neural Coding ◽  
Spatial Configuration ◽  
Dimensional Space ◽  
Absolute Magnitude ◽  
Neural Population ◽  
Population Responses ◽  
Natural Extracts ◽  
Stimulus Concentration ◽  
Standardization Procedure ◽  
Artificial Mixtures

1. Extracellular responses to complex biologically relevant stimuli were recorded from 30 primary olfactory cells from excised antennules of spiny lobsters. The stimulus types were natural extracts of crab, mullet, oyster, and shrimp and artificial mixtures of crab, mullet, oyster and shrimp based on the chemical composition of the related extracts. All stimulus types were presented at the following three concentrations: 0.005, 0.05, and 0.5 mM. 2. The responses were expressed as number of spikes per 5 s. Response magnitude increased significantly as a function of concentration. It was significantly greater for the natural extracts than for the related artificial mixtures but was not significantly different among stimulus types within either natural extracts or artificial mixtures. 3. The cells were broadly tuned to all stimuli. Tuning slightly, but significantly, broadened as a function of stimulus concentration. 4. Multidimensional scaling (MDS) was used to evaluate similarities and dissimilarities among stimuli based on population responses. The artificial mixtures and the natural extracts were analyzed separately. Dimensionality of spatial configuration was based on the following three criteria: stress values, squared correlation values, and relevance to quality coding. 5. When applied to the original data, MDS distributed the stimuli in a two-dimensional space where the location of each stimulus was based mainly on stimulus concentration. The results of a simple standardization procedure showed that this distribution resulted mostly from the significant effect of concentration on one of the two features of population responses, which is the absolute magnitude. This standardization procedure equalized the three concentrations in terms of absolute magnitude of evoked response. Consequently, the neural population responses of the 12 stimuli (4 types X 3 concentrations) could be compared based only on their across-neuron patterns (ANPs) (relative amount of activity across neurons). 6. When stress and squared correlation values were used as criteria for dimensionality, the configuration of the artificial mixtures space was best derived from dimensions 1, 2, and 3 of the three-dimensional resolution. When relevance to quality coding was used, the configuration of the artificial mixtures space was best derived from dimensions 1, 3, and 4 of the four-dimensional resolution. Whether stress and squared correlation values or relevance to quality coding were used, the four types of stimuli occupied nonoverlapping spaces.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals Electrosensory processing in Apteronotus albifrons: implications for general and specific neural coding strategies across wave-type weakly electric fish species

2016 ◽  
Vol 116 (6) ◽  
pp. 2909-2921 ◽  
Author(s):  
Diana Martinez ◽  
Michael G. Metzen ◽  
Maurice J. Chacron
Keyword(s):  
Fish Species ◽  
Related Species ◽  
Neural Coding ◽  
Electric Fish ◽  
Weakly Electric Fish ◽  
Closely Related Species ◽  
Tuning Curves ◽  
Baseline Activity ◽  
Coding Strategies ◽  
Weakly Electric

Understanding how the brain processes sensory input to generate behavior remains an important problem in neuroscience. Towards this end, it is useful to compare results obtained across multiple species to gain understanding as to the general principles of neural coding. Here we investigated hindbrain pyramidal cell activity in the weakly electric fish Apteronotus albifrons. We found strong heterogeneities when looking at baseline activity. Additionally, ON- and OFF-type cells responded to increases and decreases of sinusoidal and noise stimuli, respectively. While both cell types displayed band-pass tuning, OFF-type cells were more broadly tuned than their ON-type counterparts. The observed heterogeneities in baseline activity as well as the greater broadband tuning of OFF-type cells were both similar to those previously reported in other weakly electric fish species, suggesting that they constitute general features of sensory processing. However, we found that peak tuning occurred at frequencies ∼15 Hz in A. albifrons, which is much lower than values reported in the closely related species Apteronotus leptorhynchus and the more distantly related species Eigenmannia virescens. In response to stimuli with time-varying amplitude (i.e., envelope), ON- and OFF-type cells displayed similar high-pass tuning curves characteristic of fractional differentiation and possibly indicate optimized coding. These tuning curves were qualitatively similar to those of pyramidal cells in the closely related species A. leptorhynchus. In conclusion, comparison between our and previous results reveals general and species-specific neural coding strategies. We hypothesize that differences in coding strategies, when observed, result from different stimulus distributions in the natural/social environment.

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