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.

Mutual Information, Fisher Information, and Efficient Coding

2016 ◽  
Vol 28 (2) ◽  
pp. 305-326 ◽  
Author(s):  
Xue-Xin Wei ◽  
Alan A. Stocker
Keyword(s):  
Mutual Information ◽  
Fisher Information ◽  
Neural Coding ◽  
Population Coding ◽  
Neural Population ◽  
Small Noise ◽  
Efficient Coding ◽  
Coding Efficiency ◽  
Stimulus Variable ◽  
Neural Populations

Fisher information is generally believed to represent a lower bound on mutual information (Brunel & Nadal, 1998 ), a result that is frequently used in the assessment of neural coding efficiency. However, we demonstrate that the relation between these two quantities is more nuanced than previously thought. For example, we find that in the small noise regime, Fisher information actually provides an upper bound on mutual information. Generally our results show that it is more appropriate to consider Fisher information as an approximation rather than a bound on mutual information. We analytically derive the correspondence between the two quantities and the conditions under which the approximation is good. Our results have implications for neural coding theories and the link between neural population coding and psychophysically measurable behavior. Specifically, they allow us to formulate the efficient coding problem of maximizing mutual information between a stimulus variable and the response of a neural population in terms of Fisher information. We derive a signature of efficient coding expressed as the correspondence between the population Fisher information and the distribution of the stimulus variable. The signature is more general than previously proposed solutions that rely on specific assumptions about the neural tuning characteristics. We demonstrate that it can explain measured tuning characteristics of cortical neural populations that do not agree with previous models of efficient coding.

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 A visual circuit uses complementary mechanisms to support transient and sustained pupil constriction

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
William Thomas Keenan ◽  
Alan C Rupp ◽  
Rachel A Ross ◽  
Preethi Somasundaram ◽  
Suja Hiriyanna ◽  
...  
Keyword(s):  
Neural Coding ◽  
Pupil Size ◽  
Neural Circuit ◽  
Ganglion Cells ◽  
Retinal Ganglion ◽  
Neural Basis ◽  
Behavioral Dynamics ◽  
Pupil Constriction ◽  
Sustained Responses ◽  
Stable Control

Rapid and stable control of pupil size in response to light is critical for vision, but the neural coding mechanisms remain unclear. Here, we investigated the neural basis of pupil control by monitoring pupil size across time while manipulating each photoreceptor input or neurotransmitter output of intrinsically photosensitive retinal ganglion cells (ipRGCs), a critical relay in the control of pupil size. We show that transient and sustained pupil responses are mediated by distinct photoreceptors and neurotransmitters. Transient responses utilize input from rod photoreceptors and output by the classical neurotransmitter glutamate, but adapt within minutes. In contrast, sustained responses are dominated by non-conventional signaling mechanisms: melanopsin phototransduction in ipRGCs and output by the neuropeptide PACAP, which provide stable pupil maintenance across the day. These results highlight a temporal switch in the coding mechanisms of a neural circuit to support proper behavioral dynamics.

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 Dynamics of the Auditory Continuity Illusion

2021 ◽  
Vol 15 ◽  
Author(s):  
Qianyi Cao ◽  
Noah Parks ◽  
Joshua H. Goldwyn
Keyword(s):  
Firing Rate ◽  
Noise Burst ◽  
Auditory Scene Analysis ◽  
Quantitative Model ◽  
Neural Dynamics ◽  
Sufficient Evidence ◽  
Neural Population ◽  
Neural Basis ◽  
Continuous Responses ◽  
Conceptual Explanations

Illusions give intriguing insights into perceptual and neural dynamics. In the auditory continuity illusion, two brief tones separated by a silent gap may be heard as one continuous tone if a noise burst with appropriate characteristics fills the gap. This illusion probes the conditions under which listeners link related sounds across time and maintain perceptual continuity in the face of sudden changes in sound mixtures. Conceptual explanations of this illusion have been proposed, but its neural basis is still being investigated. In this work we provide a dynamical systems framework, grounded in principles of neural dynamics, to explain the continuity illusion. We construct an idealized firing rate model of a neural population and analyze the conditions under which firing rate responses persist during the interruption between the two tones. First, we show that sustained inputs and hysteresis dynamics (a mismatch between tone levels needed to activate and inactivate the population) can produce continuous responses. Second, we show that transient inputs and bistable dynamics (coexistence of two stable firing rate levels) can also produce continuous responses. Finally, we combine these input types together to obtain neural dynamics consistent with two requirements for the continuity illusion as articulated in a well-known theory of auditory scene analysis: responses persist through the noise-filled gap if noise provides sufficient evidence that the tone continues and if there is no evidence of discontinuities between the tones and noise. By grounding these notions in a quantitative model that incorporates elements of neural circuits (recurrent excitation, and mutual inhibition, specifically), we identify plausible mechanisms for the continuity illusion. Our findings can help guide future studies of neural correlates of this illusion and inform development of more biophysically-based models of the auditory continuity illusion.

scholarly journals Effects of spectral and temporal disruption on cortical encoding of gerbil vocalizations

2013 ◽  
Vol 110 (5) ◽  
pp. 1190-1204 ◽  
Author(s):  
Maria Ter-Mikaelian ◽  
Malcolm N. Semple ◽  
Dan H. Sanes
Keyword(s):  
Cortical Neurons ◽  
Neural Coding ◽  
Human Performance ◽  
Animal Communication ◽  
Human Perception ◽  
Primary Auditory Cortex ◽  
Sufficient Information ◽  
Spectral Bands ◽  
Stable Representation ◽  
Discharge Patterns

Animal communication sounds contain spectrotemporal fluctuations that provide powerful cues for detection and discrimination. Human perception of speech is influenced both by spectral and temporal acoustic features but is most critically dependent on envelope information. To investigate the neural coding principles underlying the perception of communication sounds, we explored the effect of disrupting the spectral or temporal content of five different gerbil call types on neural responses in the awake gerbil's primary auditory cortex (AI). The vocalizations were impoverished spectrally by reduction to 4 or 16 channels of band-passed noise. For this acoustic manipulation, an average firing rate of the neuron did not carry sufficient information to distinguish between call types. In contrast, the discharge patterns of individual AI neurons reliably categorized vocalizations composed of only four spectral bands with the appropriate natural token. The pooled responses of small populations of AI cells classified spectrally disrupted and natural calls with an accuracy that paralleled human performance on an analogous speech task. To assess whether discharge pattern was robust to temporal perturbations of an individual call, vocalizations were disrupted by time-reversing segments of variable duration. For this acoustic manipulation, cortical neurons were relatively insensitive to short reversal lengths. Consistent with human perception of speech, these results indicate that the stable representation of communication sounds in AI is more dependent on sensitivity to slow temporal envelopes than on spectral detail.

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