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.

scholarly journals Internal models for interpreting neural population activity during sensorimotor control

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Matthew D Golub ◽  
Byron M Yu ◽  
Steven M Chase
Keyword(s):  
Internal Model ◽  
Sensory Information ◽  
Internal Models ◽  
Neural Population ◽  
Prior Beliefs ◽  
Population Activity ◽  
Motor Commands ◽  
Machine Interface ◽  
The Brain ◽  
Neural Population Activity

To successfully guide limb movements, the brain takes in sensory information about the limb, internally tracks the state of the limb, and produces appropriate motor commands. It is widely believed that this process uses an internal model, which describes our prior beliefs about how the limb responds to motor commands. Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys and novel statistical analyses of neural population activity to gain insight into moment-by-moment internal model computations. We discovered that a mismatch between subjects’ internal models and the actual BMI explains roughly 65% of movement errors, as well as long-standing deficiencies in BMI speed control. We then used the internal models to characterize how the neural population activity changes during BMI learning. More broadly, this work provides an approach for interpreting neural population activity in the context of how prior beliefs guide the transformation of sensory input to motor output.

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 Probabilistic representation in human visual cortex reflects uncertainty in serial decisions

2019 ◽  
Author(s):  
R.S. van Bergen ◽  
J.F.M. Jehee
Keyword(s):  
Visual Cortex ◽  
Neural Coding ◽  
Probability Distributions ◽  
Sensory Information ◽  
Population Level ◽  
Neural Representation ◽  
Serial Dependence ◽  
Population Activity ◽  
Human Visual Cortex ◽  
Sensory Uncertainty

AbstractHow does the brain represent the reliability of its sensory evidence? Here, we test whether sensory uncertainty is encoded in cortical population activity as the width of a probability distribution – a hypothesis that lies at the heart of Bayesian models of neural coding. We probe the neural representation of uncertainty by capitalizing on a well-known behavioral bias called serial dependence. Human observers of either sex reported the orientation of stimuli presented in sequence, while activity in visual cortex was measured with fMRI. We decoded probability distributions from population-level activity and found that serial dependence effects in behavior are consistent with a statistically advantageous sensory integration strategy, in which uncertain sensory information is given less weight. More fundamentally, our results suggest that probability distributions decoded from human visual cortex reflect the sensory uncertainty that observers rely on in their decisions, providing critical evidence for Bayesian theories of perception.

scholarly journals Gradual progression from sensory to task-related processing in cerebral cortex

2018 ◽  
Vol 115 (30) ◽  
pp. E7202-E7211 ◽  
Author(s):  
Scott L. Brincat ◽  
Markus Siegel ◽  
Constantin von Nicolai ◽  
Earl K. Miller
Keyword(s):  
Sensory Information ◽  
Relevant Information ◽  
Neural Population ◽  
Motion Direction ◽  
Population Activity ◽  
Shape Information ◽  
Sensory Inputs ◽  
Neural Representations ◽  
Current Task ◽  
Cortical Regions

Somewhere along the cortical hierarchy, behaviorally relevant information is distilled from raw sensory inputs. We examined how this transformation progresses along multiple levels of the hierarchy by comparing neural representations in visual, temporal, parietal, and frontal cortices in monkeys categorizing across three visual domains (shape, motion direction, and color). Representations in visual areas middle temporal (MT) and V4 were tightly linked to external sensory inputs. In contrast, lateral prefrontal cortex (PFC) largely represented the abstracted behavioral relevance of stimuli (task rule, motion category, and color category). Intermediate-level areas, including posterior inferotemporal (PIT), lateral intraparietal (LIP), and frontal eye fields (FEF), exhibited mixed representations. While the distribution of sensory information across areas aligned well with classical functional divisions (MT carried stronger motion information, and V4 and PIT carried stronger color and shape information), categorical abstraction did not, suggesting these areas may participate in different networks for stimulus-driven and cognitive functions. Paralleling these representational differences, the dimensionality of neural population activity decreased progressively from sensory to intermediate to frontal cortex. This shows how raw sensory representations are transformed into behaviorally relevant abstractions and suggests that the dimensionality of neural activity in higher cortical regions may be specific to their current task.

scholarly journals Interneuron subtypes enable independent modulation of excitatory and inhibitory firing rates after sensory deprivation

2021 ◽  
Author(s):  
Leonidas M. A. Richter ◽  
Julijana Gjorgjieva
Keyword(s):  
Sensory Deprivation ◽  
Sensory Information ◽  
Mechanistic Explanation ◽  
Monocular Deprivation ◽  
Recurrent Networks ◽  
Cortical Circuits ◽  
Cortical Dynamics ◽  
Sensory Cortices ◽  
Synaptic Changes

Diverse interneuron subtypes determine how cortical circuits process sensory information depending on their connectivity. Sensory deprivation experiments are ideally suited to unravel the plasticity mechanisms which shape circuit connectivity, but have yet to consider the role of different inhibitory subtypes. We investigate how synaptic changes due to monocular deprivation affect the firing rate dynamics in a microcircuit network model of the visual cortex. We demonstrate that, in highly recurrent networks, deprivation-induced plasticity generates fundamentally different activity changes depending on interneuron composition. Considering parvalbumin-positive (PV+) and somatostatin-positive (SST+) interneuron subtypes can capture the experimentally observed independent modulation of excitatory and inhibitory activity during sensory deprivation when SST+ feedback is sufficiently strong. Our model also applies to whisker deprivation in the somatosensory cortex revealing that these mechanisms are general across sensory cortices. Therefore, we provide a mechanistic explanation for the differential role of interneuron subtypes in regulating cortical dynamics during deprivation-induced plasticity.

scholarly journals Correlations enhance the behavioral readout of neural population activity in association cortex

2020 ◽  
Author(s):  
Martina Valente ◽  
Giuseppe Pica ◽  
Caroline A. Runyan ◽  
Ari S. Morcos ◽  
Christopher D. Harvey ◽  
...  
Keyword(s):  
Neural Activity ◽  
Posterior Parietal Cortex ◽  
Sensory Information ◽  
Neural Population ◽  
Association Cortex ◽  
Perceptual Discrimination ◽  
Imaging Data ◽  
Stimulus Information ◽  
Population Activity ◽  
Behavioral Choice

The spatiotemporal structure of activity in populations of neurons is critical for accurate perception and behavior. Experimental and theoretical studies have focused on “noise” correlations – trial-to-trial covariations in neural activity for a given stimulus – as a key feature of population activity structure. Much work has shown that these correlations limit the stimulus information encoded by a population of neurons, leading to the widely-held prediction that correlations are detrimental for perceptual discrimination behaviors. However, this prediction relies on an untested assumption: that the neural mechanisms that read out sensory information to inform behavior depend only on a population’s total stimulus information independently of how correlations constrain this information across neurons or time. Here we make the critical advance of simultaneously studying how correlations affect both the encoding and the readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations limited the ability to encode stimulus information, but (seemingly paradoxically) correlations were higher when mice made correct choices than when they made errors. On a single-trial basis, a mouse’s behavioral choice depended not only on the stimulus information in the activity of the population as a whole, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, sensory information was more efficiently converted into a behavioral choice in the presence of correlations. Given this enhanced-by-consistency readout, we estimated that correlations produced a behavioral benefit that compensated or overcame their detrimental information-limiting effects. These results call for a re-evaluation of the role of correlated neural activity, and suggest that correlations in association cortex can benefit task performance even if they decrease sensory information.

scholarly journals A deep learning framework for inference of single-trial neural population activity from calcium imaging with sub-frame temporal resolution

2021 ◽  
Author(s):  
Feng Zhu ◽  
Harrison A Grier ◽  
Raghav Tandon ◽  
Changjia Cai ◽  
Andrea Giovannucci ◽  
...  
Keyword(s):  
Deep Learning ◽  
Temporal Resolution ◽  
Calcium Imaging ◽  
Temporal Dynamics ◽  
Population Level ◽  
Neural Population ◽  
Single Trial ◽  
Population Activity ◽  
Temporal Precision ◽  
Network States

In many brain areas, neural populations act as a coordinated network whose state is tied to behavior on a moment-by-moment basis and millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe network-scale computation, as it can measure the activity of many individual neurons, monitor multiple layers simultaneously, and sample from identified cell types. However, estimating network states and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities, and limitations on temporal resolution. Here we describe RADICaL, a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically-recorded spikes. It incorporates a novel network training strategy that exploits the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers network states more accurately than previous methods, particularly for high-frequency components. In real 2p recordings from sensorimotor areas in mice performing a "water grab" task, RADICaL infers network states with close correspondence to single-trial variations in behavior, and maintains high-quality inference even when neuronal populations are substantially reduced.

Temporal dynamics and communication of winner-effects in the crayfish, orconectes rusticus

Behaviour ◽  
2003 ◽  
Vol 140 (6) ◽  
pp. 805-825 ◽  
Author(s):  
Alisdair Daws ◽  
Robert Huber ◽  
Daniel Bergman ◽  
Jeremy McIntyre ◽  
Paul Moore ◽  
...  
Keyword(s):  
Social Status ◽  
Sensory Input ◽  
Temporal Dynamics ◽  
Sensory Information ◽  
Hierarchical Structures ◽  
Intensity Level ◽  
Orconectes Rusticus ◽  
Agonistic Interactions ◽  
Minute Interval

AbstractA variety of factors influences the formation of hierarchical structures, and can include an altered aggressive state, an ability to physically dominate, and previous agonistic experience. Using male Orconectes rusticus, we tested the duration of the winner effect by varying the time between a winning encounter and a subsequent encounter by a 20, 40 or 60-minute interval. Varying the time between the two fights significantly altered the probabilities of initiating fight behaviour and of winning a fight. A crayfish with a 20-minute delay between its winning experience and its subsequent fight was significantly less likely to initiate fight behaviour and significantly more likely to win its next fight than was an animal whose next fight was delayed for 40 or 60 minutes. We then investigated whether the dynamics of this winner effect were influenced by perception of odour signals during agonistic interactions by blocking the chemo- and mechanoreceptors on the antennae and antennules to prevent reception of relevant cues communicating social status. Individuals fighting an opponent with this loss of sensory information were significantly more likely to initiate a fight, but then escalated at a slower rate to a higher fight intensity level. In addition, individuals had a decreased chance of winning an agonistic bout against an opponent deprived of sensory input from the antennae and antennules.

scholarly journals Coupling of pupil- and neuronal population dynamics reveals diverse influences of arousal on cortical processing

2021 ◽  
Author(s):  
Thomas Pfeffer ◽  
Christian Keitel ◽  
Daniel S. Kluger ◽  
Anne Keitel ◽  
Alena Russmann ◽  
...  
Keyword(s):  
Low Frequency ◽  
Intermediate Frequency ◽  
Brain Regions ◽  
Neuronal Population ◽  
Neural Population ◽  
Cortical Circuits ◽  
Population Activity ◽  
Linear Relationships ◽  
Cortical Regions ◽  
Aperiodic Component

Fluctuations in arousal, controlled by subcortical neuromodulatory systems, continuously shape cortical state, with profound consequences for information processing. Yet, how arousal signals influence cortical population activity in detail has only been characterized for a few selected brain regions so far. Traditional accounts conceptualize arousal as a homogeneous modulator of neural population activity across the cerebral cortex. Recent insights, however, point to a higher specificity of arousal effects on different components of neural activity and across cortical regions. Here, we provide a comprehensive account of the relationships between fluctuations in arousal and neuronal population activity across the human brain. Exploiting the established link between pupil size and central arousal systems, we performed concurrent magnetoencephalographic (MEG) and pupillographic recordings in a large number of participants, pooled across three laboratories. We found a cascade of effects relative to the peak timing of spontaneous pupil dilations: Decreases in low-frequency (2-8 Hz) activity in temporal and lateral frontal cortex, followed by increased high-frequency (>64 Hz) activity in mid-frontal regions, followed by linear and non-linear relationships with intermediate frequency-range activity (8-32 Hz) in occipito-parietal regions. The non-linearity resembled an inverted U-shape whereby intermediate pupil sizes coincided with maximum 8-32 Hz activity. Pupil-linked arousal also coincided with widespread changes in the structure of the aperiodic component of cortical population activity, indicative of changes in the excitation-inhibition balance in underlying microcircuits. Our results provide a novel basis for studying the arousal modulation of cognitive computations in cortical circuits.

scholarly journals Equations governing dynamics of excitation and inhibition in the mouse corticothalamic network

2020 ◽  
Author(s):  
I-Chun Lin ◽  
Michael Okun ◽  
Matteo Carandini ◽  
Kenneth D. Harris
Keyword(s):  
Cortical Neurons ◽  
Mean Field ◽  
Inhibitory Neurons ◽  
Cortical Circuits ◽  
Mean Field Model ◽  
Population Activity ◽  
Cortical Dynamics ◽  
Mean Field Models ◽  
Cortical Responses ◽  
Mouse Visual Cortex

Although cortical circuits are complex and interconnected with the rest of the brain, their macroscopic dynamics are often approximated by modeling the averaged activities of excitatory and inhibitory cortical neurons, without interactions with other brain circuits. To verify the validity of such mean-field models, we optogenetically stimulated populations of excitatory and parvalbumin-expressing inhibitory neurons in awake mouse visual cortex, while recording population activity in cortex and in its thalamic correspondent, the lateral geniculate nucleus. The cortical responses to brief test pulses could not be explained by a mean-field model including only cortical excitatory and inhibitory populations. However, these responses could be predicted by extending the model to include thalamic interactions that cause net cortical suppression following activation of cortical excitatory neurons. We conclude that mean-field models can accurately summarize cortical dynamics, but only when the cortex is considered as part of a dynamic corticothalamic network.

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