scholarly journals Contribution of behavioural variability to representational drift

2022 ◽  
Author(s):  
Sadra Sadeh ◽  
Claudia Clopath
Keyword(s):  
Sensory Processing ◽  
Neural Coding ◽  
Internal State ◽  
Neuronal Responses ◽  
Behavioural State ◽  
Cortical Areas ◽  
Allen Brain ◽  
Stable Substrate ◽  
Low Dimensional ◽  
External Stimuli

Neuronal responses to similar stimuli change dynamically over time, raising the question of how internal representations can provide a stable substrate for neural coding. While the drift of these representations is mostly characterized in relation to external stimuli or tasks, behavioural or internal state of the animal is also known to modulate the neural activity. We therefore asked how the variability of such modulatory mechanisms can contribute to representational drift. By analysing publicly available datasets from the Allen Brain Observatory, we found that behavioural variability significantly contributes to changes in stimulus-induced neuronal responses across various cortical areas in the mouse. This effect could not be explained by a gain model in which change in the behavioural state scaled the signal or the noise. A better explanation was provided by a model in which behaviour contributed independently to neuronal tuning. Our results are consistent with a view in which behaviour modulates the low-dimensional, slowly-changing setpoints of neurons, upon which faster operations like sensory processing are performed. Importantly, our analysis suggests that reliable but variable behavioural signals might be misinterpreted as representational drift, if neuronal representations are only characterized in the stimulus space and marginalised over behavioural parameters.

Dynamical Constraints on Using Precise Spike Timing to Compute in Recurrent Cortical Networks

2008 ◽  
Vol 20 (4) ◽  
pp. 974-993 ◽  
Author(s):  
Arunava Banerjee ◽  
Peggy Seriès ◽  
Alexandre Pouget
Keyword(s):  
Dynamical System ◽  
Initial Conditions ◽  
Internal State ◽  
Spike Timing ◽  
Spiking Neurons ◽  
Spatiotemporal Patterns ◽  
Cortical Networks ◽  
Temporal Precision ◽  
Cortical Areas ◽  
Dynamical Constraints

Several recent models have proposed the use of precise timing of spikes for cortical computation. Such models rely on growing experimental evidence that neurons in the thalamus as well as many primary sensory cortical areas respond to stimuli with remarkable temporal precision. Models of computation based on spike timing, where the output of the network is a function not only of the input but also of an independently initializable internal state of the network, must, however, satisfy a critical constraint: the dynamics of the network should not be sensitive to initial conditions. We have previously developed an abstract dynamical system for networks of spiking neurons that has allowed us to identify the criterion for the stationary dynamics of a network to be sensitive to initial conditions. Guided by this criterion, we analyzed the dynamics of several recurrent cortical architectures, including one from the orientation selectivity literature. Based on the results, we conclude that under conditions of sustained, Poisson-like, weakly correlated, low to moderate levels of internal activity as found in the cortex, it is unlikely that recurrent cortical networks can robustly generate precise spike trajectories, that is, spatiotemporal patterns of spikes precise to the millisecond timescale.

scholarly journals Anesthesia and brain sensory processing: impact on neuronal responses in a female songbird

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
G. Karino ◽  
I. George ◽  
L. Loison ◽  
C. Heyraud ◽  
G. De Groof ◽  
...  
Keyword(s):  
Sensory Processing ◽  
Neuronal Responses

scholarly journals Neural responses to action contingency error in different cortical areas are attributable to forward prediction or sensory processing

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Tatsuo Kikuchi ◽  
Motoaki Sugiura ◽  
Yuki Yamamoto ◽  
Yukako Sasaki ◽  
Sugiko Hanawa ◽  
...  
Keyword(s):  
Sensory Processing ◽  
Neural Responses ◽  
Cortical Areas ◽  
Forward Prediction

scholarly journals The effects of sensory-processing sensitivity and sense of coherence on depressive symptoms in university students

2019 ◽  
Vol 6 (2) ◽  
pp. 205510291987163 ◽  
Author(s):  
Kosuke Yano ◽  
Takayoshi Kase ◽  
Kazuo Oishi
Keyword(s):  
Depressive Symptoms ◽  
University Students ◽  
Sensory Processing ◽  
Sense Of Coherence ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Moderation Effect ◽  
Sensory Processing Sensitivity ◽  
External Stimuli ◽  
The Relationship

Sensory-processing sensitivity differentiates individuals according to responsivity to internal and external stimuli. It has been positively correlated with depressive symptoms. Meanwhile, sense of coherence, an individual’s perception that stressors are comprehensible, manageable, and meaningful for their life, could improve depression. This cross-sectional study investigated the moderation effect of sense of coherence on the relationship between sensory-processing sensitivity and depressive symptoms in university students. Japanese students ( N = 430) participated in a questionnaire survey that assessed levels of sensory-processing sensitivity, sense of coherence, and depressive symptoms. The results showed that a strong sense of coherence moderated the relationship between sensory-processing sensitivity and depressive symptoms in university students.

scholarly journals Evolving super stimuli for real neurons using deep generative networks

2019 ◽  
Author(s):  
Carlos R. Ponce ◽  
Will Xiao ◽  
Peter F. Schade ◽  
Till S. Hartmann ◽  
Gabriel Kreiman ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Visual Cortex ◽  
Neural Coding ◽  
Deep Neural Network ◽  
Natural Images ◽  
List Type ◽  
Neuronal Responses ◽  
Diagnostic Features ◽  
Neural Selectivity

AbstractFinding the best stimulus for a neuron is challenging because it is impossible to test all possible stimuli. Here we used a vast, unbiased, and diverse hypothesis space encoded by a generative deep neural network model to investigate neuronal selectivity in inferotemporal cortex without making any assumptions about natural features or categories. A genetic algorithm, guided by neuronal responses, searched this space for optimal stimuli. Evolved synthetic images evoked higher firing rates than even the best natural images and revealed diagnostic features, independently of category or feature selection. This approach provides a way to investigate neural selectivity in any modality that can be represented by a neural network and challenges our understanding of neural coding in visual cortex.HighlightsA generative deep neural network interacted with a genetic algorithm to evolve stimuli that maximized the firing of neurons in alert macaque inferotemporal and primary visual cortex.The evolved images activated neurons more strongly than did thousands of natural images.Distance in image space from the evolved images predicted responses of neurons to novel images.

scholarly journals RL mechanisms of short-term plasticity of auditory cortex

2016 ◽  
Author(s):  
Elena Krugliakova ◽  
Alexey Gorin ◽  
Anna Shestakova ◽  
Tommaso Fedele ◽  
Vasily Klucharev
Keyword(s):  
Decision Making ◽  
Auditory Cortex ◽  
Sensory Processing ◽  
Dynamic Environment ◽  
Influential Factors ◽  
Oddball Paradigm ◽  
Fine Grained ◽  
Before And After ◽  
Monetary Incentive Delay ◽  
External Stimuli

AbstractThe decision-making process is exposed to modulatory factors, and, according to the expected value (EV) concept the two most influential factors are magnitude of prospective behavioural outcome and probability of receiving this outcome. The discrepancy between received and predicted outcomes is reflected by the reward prediction error (RPE), which is believed to play a crucial role in learning in dynamic environment. Feedback related negativity (FRN), a frontocentral negative component registered in EEG during feedback presentation, has been suggested as a neural signature of RPE. In modern neurobiological models of decision-making the primary sensory input is assumed to be constant over the time and independent of the evaluation of the option associated to it. In this study we investigated whether the electrophysiological changes in auditory cues perception is modulated by the strengths of reinforcement signal, represented in the EEG as FRN.We quantified the changes in sensory processing through a classical passive oddball paradigm before and after performance a neuroeconomic monetary incentive delay (MID) task. Outcome magnitude and probability were encoded in the physical characteristics of auditory incentive cues. We evaluated the association between individual biomarkers of reinforcement signal (FRN) and the degree of perceptual learning, reflected by changes in auditory ERP components (mismatch negativity and P3a). We observed a significant correlation of MMN and valence - dFRN, reflecting differential processing of gains and omission of gains. Changes in P3a were correlated to probability - dFRN, including information on salience of the outcome, in addition to its valence.MID task performance evokes plastic changes associated with more fine-grained discrimination of auditory anticipatory cues and enhanced involuntary attention switch towards these cues. Observed signatures of neuro-plasticity of the auditory cortex may play an important role in learning and decision-making processes through facilitation of perceptual discrimination of valuable external stimuli. Thus, the sensory processing of options and the evaluation of options are not independent as implicitly assumed by the modern neuroeconomics models of decision-making.

scholarly journals Network dynamics underlying OFF responses in the auditory cortex

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Giulio Bondanelli ◽  
Thomas Deneux ◽  
Brice Bathellier ◽  
Srdjan Ostojic
Keyword(s):  
Auditory Cortex ◽  
Single Cell ◽  
Cell Model ◽  
Network Models ◽  
Population Responses ◽  
Two Photon ◽  
Cortical Areas ◽  
Single Cell Model ◽  
Spatio Temporal ◽  
Low Dimensional

Across sensory systems, complex spatio-temporal patterns of neural activity arise following the onset (ON) and offset (OFF) of stimuli. While ON responses have been widely studied, the mechanisms generating OFF responses in cortical areas have so far not been fully elucidated. We examine here the hypothesis that OFF responses are single-cell signatures of recurrent interactions at the network level. To test this hypothesis, we performed population analyses of two-photon calcium recordings in the auditory cortex of awake mice listening to auditory stimuli, and compared linear single-cell and network models. While the single-cell model explained some prominent features of the data, it could not capture the structure across stimuli and trials. In contrast, the network model accounted for the low-dimensional organisation of population responses and their global structure across stimuli, where distinct stimuli activated mostly orthogonal dimensions in the neural state-space.

scholarly journals Learning sparse codes from compressed representations with biologically plausible local wiring constraints

2020 ◽  
Author(s):  
Kion Fallah ◽  
Adam A. Willats ◽  
Ninghao Liu ◽  
Christopher J. Rozell
Keyword(s):  
Unsupervised Learning ◽  
Sparse Coding ◽  
Visual Information ◽  
Neural Coding ◽  
Receptive Fields ◽  
Sparse Representations ◽  
Linear Operators ◽  
Compression Process ◽  
Cortical Areas ◽  
Theoretical Neuroscience

AbstractSparse coding is an important method for unsupervised learning of task-independent features in theoretical neuroscience models of neural coding. While a number of algorithms exist to learn these representations from the statistics of a dataset, they largely ignore the information bottlenecks present in fiber pathways connecting cortical areas. For example, the visual pathway has many fewer neurons transmitting visual information to cortex than the number of photoreceptors. Both empirical and analytic results have recently shown that sparse representations can be learned effectively after performing dimensionality reduction with randomized linear operators, producing latent coefficients that preserve information. Unfortunately, current proposals for sparse coding in the compressed space require a centralized compression process (i.e., dense random matrix) that is biologically unrealistic due to local wiring constraints observed in neural circuits. The main contribution of this paper is to leverage recent results on structured random matrices to propose a theoretical neuroscience model of randomized projections for communication between cortical areas that is consistent with the local wiring constraints observed in neuroanatomy. We show analytically and empirically that unsupervised learning of sparse representations can be performed in the compressed space despite significant local wiring constraints in compression matrices of varying forms (corresponding to different local wiring patterns). Our analysis verifies that even with significant local wiring constraints, the learned representations remain qualitatively similar, have similar quantitative performance in both training and generalization error, and are consistent across many measures with measured macaque V1 receptive fields.

Evolution of Neocortex for Sensory Processing

2019 ◽  
Author(s):  
Jon H. Kaas
Keyword(s):  
Sensory Processing ◽  
Cortical Neurons ◽  
Pyramidal Neurons ◽  
Motor Behavior ◽  
Single Layer ◽  
Small Region ◽  
Inhibitory Neurons ◽  
Dorsal Cortex ◽  
Structural Differentiation ◽  
Cortical Areas

The neocortex is a part of the forebrain of mammals that is an innovation of mammal-like “reptilian” synapsid ancestors of early mammals. This neocortex emerged from a small region of dorsal cortex that was present in earlier ancestors and is still found in the forebrain of present-day reptiles. Instead of the thick structure of six layers of cells (five layers) and fibers (one layer) of neocortex of mammals, the dorsal cortex was characterized by a single layer of pyramidal neurons and a scattering of small, largely inhibitory neurons. In reptiles, the dorsal cortex is dominated by visual inputs, with outputs that relate to behavior and memory. The thicker neocortex of six layers in early mammals was already divided into a number of functionally specialized zones called cortical areas that were predominantly sensory in function, while relating to important aspects of motor behavior via subcortical projections. These early sensorimotor areas became modified in various ways as different branches of the mammalian radiation evolved, and neocortex often increased in size and the number of cortical areas, likely by the process of specializations within areas that subdivided areas. At least some areas, perhaps most, subdivided in another way by evolving two or more alternating types of small regions of different functional specializations, now referred to as cortical modules or columns. The specializations within and across cortical areas included those in the sizes of neurons and the extents of their processes, the dendrites and axons, and thus connections with other neurons. As a result, the neocortex of present-day mammals varies greatly within and across phylogenetically related groups (clades), while retaining basic features of organization from early ancestral mammals. In a number of present-day (extant) mammals, brains are relatively small and have little neocortex, with few areas and little structural differentiation, thus resembling early mammals. Other small mammals with little neocortex have specialized some part via selective enlargement and structural modifications to promote certain sensory abilities. Other mammals have a neocortex that is moderately to greatly expanded, with more cortical areas directly related to sensory processing and cognition and memory. The human brain is extreme in this way by having more neocortex in proportion to the rest of the brain, more cortical neurons, and likely more cortical areas.

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