Biology subjects

Mrsic-Flogel, T.

Publications and source records attributed to Mrsic-Flogel, T..

5 recordsLinked to original sources

bioRxiv2021

Learning shapes cortical dynamics to enhance integration of relevant sensory input

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. HighlightsO_LIA new theoretical principle links recurrent circuit dynamics to optimal sensory coding C_LIO_LIPredicts that high-SNR input dimensions activate slowly decaying modes of dynamics C_LIO_LIPopulation dynamics in primary visual cortex realign during learning as predicted C_LIO_LIStimulus-specific changes in E-I connectivity in recurrent circuits explain realignment C_LI

neuroscience↗

bioRxiv2020

A standardized and reproducible method to measure decision-making in mice

Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We designed a task for head-fixed mice that combines established assays of perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be successfully reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path towards achieving reproducibility in neuroscience through collaborative open-science approaches.

neuroscience↗

bioRxiv2019

The sensory representation of causally controlled objects

Intentional control over external objects is informed by our sensory experience of them. To study how causal relationships are learned and effected, we devised a brain machine interface (BMI) task utilising wide-field calcium signals. Mice learned to entrain activity patterns in arbitrary pairs of cortical regions to guide a visual cursor to a target location for reward. Brain areas that were normally correlated could be rapidly reconfigured to exert control over the cursor in a sensory feedback-dependent manner. Higher visual cortex was more engaged when expert but not naive animals controlled the cursor. Individual neurons in higher visual cortex responded more strongly to the cursor when mice controlled it than when they passively viewed it, with the greatest response boosting as the cursor approached target location. Thus, representations of causally-controlled objects are sensitive to intention and proximity to the subjects goal, potentially strengthening sensory feedback to allow more fluent control.

neuroscience↗

bioRxiv2018

Locomotion-dependent remapping of distributed cortical networks

The interactions between areas of the neocortex are fluid and state-dependent, but how individual neurons couple to cortex-wide network dynamics remains poorly understood. We correlated the spiking of individual neurons in primary visual (V1) and retrosplenial (RSP) cortex to activity across dorsal cortex, recorded simultaneously by calcium imaging. Individual neurons were correlated with distinct and reproducible patterns of activity across the cortical surface; while some fired predominantly with their local area, others coupled to activity in subsets of distal areas. The extent of distal coupling was predicted by how strongly neurons correlated with the local network. Changes in brain state triggered by locomotion re-structured how neurons couple to cortical activity patterns: running strengthened affiliations of V1 neurons with visual areas, while strengthening distal affiliations of RSP neurons with sensory cortices. Thus, individual neurons within a cortical area can independently engage in different cortical networks depending on the animal's behavioral state.

neuroscience↗

bioRxiv2017

Assessing the role of inhibition in stabilizing neocortical networks requires large-scale perturbation of the inhibitory population

Neurons within cortical microcircuits are interconnected with recurrent excitatory synaptic connections that are thought to amplify signals (Douglas and Martin, 2007), form selective subnetworks (Ko et al., 2011) and aid feature discrimination. Strong inhibition (Haider et al., 2013) counterbalances excitation, enabling sensory features to be sharpened and represented by sparse codes (Willmore et al., 2011). This \"balance\" between excitation and inhibition makes it difficult to assess the strength, or gain, of recurrent excitatory connections within cortical networks, which is key to understanding their operational regime and the computations they perform. Networks of neurons combining an unstable high-gain excitatory population with stabilizing inhibitory feedback are known as inhibition-stabilized networks (ISNs; Tsodyks et al. 1997). Theoretical studies using reduced network models predict that ISNs produce paradoxical responses to perturbation, but experimental perturbations failed to find evidence for ISNs in cortex (Atallah et al., 2012). We re-examined this question by investigating how cortical network models consisting of many neurons behave following perturbations, and found that results obtained from reduced network models fail to predict responses to perturbations in more realistic networks. Our models predict that a large proportion of the inhibitory network must be perturbed in order to robustly detect an ISN regime in cortex. We propose that wide-field optogenetic suppression of inhibition under a promoter targeting all inhibitory neurons may provide a perturbation of sufficient strength to reveal the operating regime of cortex. Our results suggest that detailed computational models of optogenetic perturbations are necessary to interpret the results of experimental paradigms.\n\nSignificance statementMany useful computational mechanisms proposed for cortex require local excitatory recurrence to be very strong, such that local inhibitory feedback is necessary to avoid epileptiform runaway activity (an \"inhibition-stabilized network\" or \"ISN\" regime). However, recent experimental results suggest this regime may not exist in cortex. We simulated activity perturbations in cortical networks of increasing realism, and found that in order to detect ISN-like properties in cortex, large proportions of the inhibitory population must be perturbed. Current experimental methods for inhibitory perturbation are unlikely to satisfy this requirement, implying that existing experimental observations are inconclusive about the computational regime of cortex. Our results suggest that new experimental designs, targetting a majority of inhibitory neurons, may be able to resolve this question.

neuroscience↗