A population code with added grandmothers?

2000 ◽  
Vol 23 (4) ◽  
pp. 495-496 ◽  
Author(s):  
Malcolm P. Young ◽  
Stefano Panzeri ◽  
Robert Robertson
Keyword(s):  
Population Coding ◽  
Neuronal Responses ◽  
Population Code ◽  
Natural Stimuli ◽  
Sparse Population

Page's “localist” code, a population code with occasional, maximally firing elements, does not seem to us usefully or testably different from sparse population coding. Some of the evidence adduced by Page for his proposal is not actually evidence for it, and coding by maximal firing is challenged by lower firing observed in neuronal responses to natural stimuli.

scholarly journals Laminar dependence of neuronal correlations in visual cortex

2013 ◽  
Vol 109 (4) ◽  
pp. 940-947 ◽  
Author(s):  
Matthew A. Smith ◽  
Xiaoxuan Jia ◽  
Amin Zandvakili ◽  
Adam Kohn
Keyword(s):  
Visual Cortex ◽  
Field Potential ◽  
Low Frequency ◽  
Population Coding ◽  
Neuronal Responses ◽  
Cortical Function ◽  
Area V2 ◽  
Complementary Pattern ◽  
Deep Layers ◽  
Slow Timescale

Neuronal responses are correlated on a range of timescales. Correlations can affect population coding and may play an important role in cortical function. Correlations are known to depend on stimulus drive, behavioral context, and experience, but the mechanisms that determine their properties are poorly understood. Here we make use of the laminar organization of cortex, with its variations in sources of input, local circuit architecture, and neuronal properties, to test whether networks engaged in similar functions but with distinct properties generate different patterns of correlation. We find that slow timescale correlations are prominent in the superficial and deep layers of primary visual cortex (V1) of macaque monkeys, but near zero in the middle layers. Brief timescale correlation (synchrony), on the other hand, was slightly stronger in the middle layers of V1, although evident at most cortical depths. Laminar variations were also apparent in the power of the local field potential, with a complementary pattern for low frequency (<10 Hz) and gamma (30–50 Hz) power. Recordings in area V2 revealed a laminar dependence similar to V1 for synchrony, but slow timescale correlations were not different between the input layers and nearby locations. Our results reveal that cortical circuits in different laminae can generate remarkably different patterns of correlations, despite being tightly interconnected.

scholarly journals Population coding of valence in the basolateral amygdala

2018 ◽  
Author(s):  
Xian Zhang ◽  
Bo Li
Keyword(s):  
Associative Learning ◽  
Reversal Learning ◽  
Basolateral Amygdala ◽  
Population Coding ◽  
Neuronal Responses ◽  
Conditioned Stimuli ◽  
Reward And Punishment

AbstractThe basolateral amygdala (BLA) plays an important role in associative learning, by representing both conditioned stimuli (CSs) and unconditioned stimuli (USs) of positive and negative valences, and by forming associations between CSs and USs. However, how such associations are formed and updated during learning remains unclear. Here we show that associative learning driven by reward and punishment profoundly alters BLA neuronal responses at population levels, reducing noise correlations and transforming the representations of CSs to resemble the distinctive valence-specific representations of USs. This transformation is accompanied by the emergence of prevalent inhibitory CS and US responses, and by the plasticity of CS responses in individual BLA neurons. During reversal learning wherein the expected valences are reversed, BLA population CS representations are remapped onto ensembles representing the opposite valences and track the switching in valence-specific behavioral actions. Our results reveal how signals predictive of opposing valences in the BLA evolve during reward and punishment learning, and how these signals might be updated and used to guide flexible behaviors.

scholarly journals Statistically Efficient Estimation Using Population Coding

1998 ◽  
Vol 10 (2) ◽  
pp. 373-401 ◽  
Author(s):  
Alexandre Pouget ◽  
Kechen Zhang ◽  
Sophie Deneve ◽  
Peter E. Latham
Keyword(s):  
Maximum Likelihood ◽  
Recurrent Network ◽  
Population Coding ◽  
Efficient Estimation ◽  
Population Code ◽  
Population Vector ◽  
Uncorrelated Noise ◽  
Vector Estimate ◽  
The Brain ◽  
Code Format

Coarse codes are widely used throughout the brain to encode sensory and motor variables. Methods designed to interpret these codes, such as population vector analysis, are either inefficient (the variance of the estimate is much larger than the smallest possible variance) or biologically implausible, like maximum likelihood. Moreover, these methods attempt to compute a scalar or vector estimate of the encoded variable. Neurons are faced with a similar estimation problem. They must read out the responses of the presynaptic neurons, but, by contrast, they typically encode the variable with a further population code rather than as a scalar. We show how a nonlinear recurrent network can be used to perform estimation in a near-optimal way while keeping the estimate in a coarse code format. This work suggests that lateral connections in the cortex may be involved in cleaning up uncorrelated noise among neurons representing similar variables.

scholarly journals Homogenous Chaotic Network Serving as a Rate/Population Code to Temporal Code Converter

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mikhail V. Kiselev
Keyword(s):  
Spike Trains ◽  
Population Coding ◽  
Temporal Coding ◽  
Temporal Code ◽  
Information Coding ◽  
Population Code ◽  
Chaotic Neural Network ◽  
Coding Schemes ◽  
Network Training ◽  
Rate Coding

At present, it is obvious that different sections of nervous system utilize different methods for information coding. Primary afferent signals in most cases are represented in form of spike trains using a combination of rate coding and population coding while there are clear evidences that temporal coding is used in various regions of cortex. In the present paper, it is shown that conversion between these two coding schemes can be performed under certain conditions by a homogenous chaotic neural network. Interestingly, this effect can be achieved without network training and synaptic plasticity.

scholarly journals Functional and Anatomical Specificity in a Higher Olfactory Centre

2018 ◽  
Author(s):  
Shahar Frechter ◽  
Alexander S. Bates ◽  
Sina Tootoonian ◽  
Michael-John Dolan ◽  
James D. Manton ◽  
...  
Keyword(s):  
Sensory Processing ◽  
Mushroom Body ◽  
Cell Types ◽  
Projection Neurons ◽  
Olfactory Behavior ◽  
Population Code ◽  
Odor Coding ◽  
Sensory Stimuli ◽  
Anatomical Analysis ◽  
Sparse Population

AbstractMost sensory systems are organized into parallel neuronal pathways that process distinct aspects of incoming stimuli. For example, in insects, second order olfactory projection neurons target both the mushroom body, which is required for learning, and the lateral horn (LH), which has been proposed to mediate innate olfactory behavior. Mushroom body neurons encode odors in a sparse population code, which does not appear stereotyped across animals. In contrast the functional principles of odor coding in the LH remain poorly understood. We have carried out a comprehensive anatomical analysis of the Drosophila LH, counting ~1400 neurons; combining genetic driver lines, anatomical and functional criteria, we identify 165 LHN cell types. We then show that genetically labeled LHNs have stereotyped odor responses across animals for 33 of these cell types. LHN tuning can be ultra-sparse (1/40 odors tested), but on average single LHNs respond to three times more odors than single projection neurons. This difference can be rationalized by our observation that LHNs are better odor categorizers, likely due to pooling of input projection neurons responding to different odors of the same category. Our results reveal some of the principles by which a higher sensory processing area can extract innate behavioral significance from sensory stimuli.

scholarly journals Synthesis of a comprehensive population code for contextual features in the awake sensory cortex

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Evan H Lyall ◽  
Daniel P Mossing ◽  
Scott R Pluta ◽  
Yun Wen Chu ◽  
Amir Dudai ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
Spatial Summation ◽  
Higher Order ◽  
Population Coding ◽  
Population Code ◽  
Complex Objects ◽  
Global Features ◽  
Sensory Stimuli ◽  
Stimulus Features ◽  
Complex Features

How cortical circuits build representations of complex objects is poorly understood. Individual neurons must integrate broadly over space, yet simultaneously obtain sharp tuning to specific global stimulus features. Groups of neurons identifying different global features must then assemble into a population that forms a comprehensive code for these global stimulus properties. Although the logic for how single neurons summate over their spatial inputs has been well-explored in anesthetized animals, how large groups of neurons compose a flexible population code of higher order features in awake animals is not known. To address this question, we probed the integration and population coding of higher order stimuli in the somatosensory and visual cortices of awake mice using two-photon calcium imaging across cortical layers. We developed a novel tactile stimulator that allowed the precise measurement of spatial summation even in actively whisking mice. Using this system, we found a sparse but comprehensive population code for higher order tactile features that depends on a heterogeneous and neuron-specific logic of spatial summation beyond the receptive field. Different somatosensory cortical neurons summed specific combinations of sensory inputs supra-linearly, but integrated other inputs sub-linearly, leading to selective responses to higher order features. Visual cortical populations employed a nearly identical scheme to generate a comprehensive population code for contextual stimuli. These results suggest that a heterogeneous logic of input-specific supra-linear summation may represent a widespread cortical mechanism for the synthesis of sparse higher order feature codes in neural populations. This may explain how the brain exploits the thalamocortical expansion of dimensionality to encode arbitrary complex features of sensory stimuli.

scholarly journals Asynchrony adaptation reveals neural population code for audio-visual timing

2010 ◽  
Vol 278 (1710) ◽  
pp. 1314-1322 ◽  
Author(s):  
Neil W. Roach ◽  
James Heron ◽  
David Whitaker ◽  
Paul V. McGraw
Keyword(s):  
Neuronal Response ◽  
Population Coding ◽  
Neural Population ◽  
Common Source ◽  
Population Code ◽  
After Effects ◽  
Temporal Relationships ◽  
Wide Range ◽  
Under Sampling ◽  
Flexible Adaptation

The relative timing of auditory and visual stimuli is a critical cue for determining whether sensory signals relate to a common source and for making inferences about causality. However, the way in which the brain represents temporal relationships remains poorly understood. Recent studies indicate that our perception of multisensory timing is flexible—adaptation to a regular inter-modal delay alters the point at which subsequent stimuli are judged to be simultaneous. Here, we measure the effect of audio-visual asynchrony adaptation on the perception of a wide range of sub-second temporal relationships. We find distinctive patterns of induced biases that are inconsistent with the previous explanations based on changes in perceptual latency. Instead, our results can be well accounted for by a neural population coding model in which: (i) relative audio-visual timing is represented by the distributed activity across a relatively small number of neurons tuned to different delays; (ii) the algorithm for reading out this population code is efficient, but subject to biases owing to under-sampling; and (iii) the effect of adaptation is to modify neuronal response gain. These results suggest that multisensory timing information is represented by a dedicated population code and that shifts in perceived simultaneity following asynchrony adaptation arise from analogous neural processes to well-known perceptual after-effects.

scholarly journals A computational toolbox and step-by-step tutorial for the analysis of neuronal population dynamics in calcium imaging data

2017 ◽  
Author(s):  
Sebastián A. Romano ◽  
Verónica Pérez-Schuster ◽  
Adrien Jouary ◽  
Alessia Candeo ◽  
Jonathan Boulanger-Weill ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Calcium Imaging ◽  
Single Neuron ◽  
Neuronal Population ◽  
Population Coding ◽  
Imaging Data ◽  
Neuronal Responses ◽  
Population Activity ◽  
The Brain

The development of new imaging and optogenetics techniques to study the dynamics of large neuronal circuits is generating datasets of unprecedented volume and complexity, demanding the development of appropriate analysis tools. We present a tutorial for the use of a comprehensive computational toolbox for the analysis of neuronal population activity imaging. It consists of tools for image pre-processing and segmentation, estimation of significant single-neuron single-trial signals, mapping event-related neuronal responses, detection of activity-correlated neuronal clusters, exploration of population dynamics, and analysis of clusters’ features against surrogate control datasets. They are integrated in a modular and versatile processing pipeline, adaptable to different needs. The clustering module is capable of detecting flexible, dynamically activated neuronal assemblies, consistent with the distributed population coding of the brain. We demonstrate the suitability of the toolbox for a variety of calcium imaging datasets, and provide a case study to explain its implementation.

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