Biology subjects

Chau, H. Y.

Publications and source records attributed to Chau, H. Y..

5 recordsLinked to original sources

bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

bioRxiv2026

Characteristics and dynamical signatures of recurrent cortical circuits during context-dependent processing

Context profoundly shapes neural responses and behavior. During context-dependent sensory processing, recurrent connections shape the integration of feedforward sensory input and feed-back input from downstream brain regions. How do different cell types, interacting through spatially structured recurrent lateral connections, give rise to context-dependent processing and circuit stability, and what dynamical signatures reveal their individual roles? To answer these questions, we employ data-driven approaches to construct spatially extended stabilized supralinear network models that capture the responses of diverse cell types in the mouse primary visual cortex during context-dependent processing. Analysis of well-fitting models reveals that the dominant inhibitory cell type affecting excitatory neurons is not fixed but dynamically varies with stimulus and space. While PV-mediated stabilization is indispensable across all models and stimulus conditions, SST-mediated stabilization is also required, and likely in a stimulus-dependent manner. Interestingly, even when a specific inhibitory cell type is required for circuit stabilization, a uniform perturbation of it does not necessarily produce a paradoxical change in its mean activity. Instead, assessing cell-type-specific circuit stabilization requires patterned perturbations, where paradoxical effects manifest along specific activity modes. Finally, we show that recurrent connections and input-output nonlinearities are essential for integrating feedforward and feedback inputs to reproduce the observed spatial response profiles. Recurrent excitatory connections, in particular, are required to account for responses to small stimuli, where external inputs are relatively weak. Taken together, our work reveals the crucial role of ubiquitous biological components in context-dependent processing and delineates the characteristics and dynamical signatures of these circuits.

neuroscience↗

bioRxiv2024

Exact linear theory of perturbation response in a space- and feature-dependent cortical circuitmodel

What are the principles that govern the responses of cortical networks to their inputs and the emergence of these responses from recurrent connectivity? Recent experiments have probed these questions by measuring cortical responses to two-photon optogenetic perturbations of single cells in the mouse primary visual cortex. A robust theoretical framework is needed to determine the implications of these responses for cortical recurrence. Here we propose a novel analytical approach: a formulation of the dependence of cell-type-specific connectivity on spatial distance that yields an exact solution for the linear perturbation response of a model with multiple cell types and space- and feature-dependent connectivity. Importantly and unlike previous approaches, the solution is valid in regimes of strong as well as weak intra-cortical coupling. Analysis reveals the structure of connectivity implied by various features of single-cell perturbation responses, such as the surprisingly narrow spatial radius of nearby excitation beyond which inhibition dominates, the number of transitions between mean excitation and inhibition thereafter, and the dependence of these responses on feature preferences. Comparison of these results to existing optogenetic perturbation data yields constraints on cell-type-specific connection strengths and their tuning dependence. Finally, we provide experimental predictions regarding the response of inhibitory neurons to single-cell perturbations and the modulation of perturbation response by neuronal gain. Significance StatementThe cerebral cortex is strongly recurrently connected with complex wiring rules. This circuitry can now be probed by studying responses to optogenetic perturbations of one or small numbers of cells. However, we currently lack a general theory connecting these responses to underlying circuitry. Here we develop a novel, exactly solvable theory to determine responses to small perturbations from the underlying connectivity. Analysis of these equations reveals simple rules that govern perturbation response patterns. Comparison with experimental data yields new constraints on the connectivity parameters. The theory yields predictions for the responses of unmeasured cell types and in new experimental conditions.

neuroscience↗

bioRxiv2019

Emergence of opposite neurons in a decentralized firing-rate model of multisensory integration

Multisensory integration areas such as dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas in macaques combine visual and vestibular cues to produce better estimates of self-motion. Congruent and opposite neurons, two types of neurons found in these areas, prefer congruent inputs and opposite inputs from the two modalities, respectively. A recently proposed computational model of congruent and opposite neurons reproduces their tuning properties and shows that congruent neurons optimally integrate information while opposite neurons compute disparity information. However, the connections in the network are fixed rather than learned, and in fact the connections of opposite neurons, as we will show, cannot arise from Hebbian learning rules. We therefore propose a new model of multisensory integration in which congruent neurons and opposite neurons emerge through Hebbian and anti-Hebbian learning rules, and show that these neurons exhibit experimentally observed tuning properties.

neuroscience↗

bioRxiv2019

Emergence of opposite neurons in a firing-rate model of multisensory integration

Opposite neurons, found in macaque dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas, combine visual and vestibular cues of self-motion in opposite ways. A neural circuit recently proposed utilizes opposite neurons to perform causal inference and decide whether the visual and vestibular cues in MSTd and VIP should be integrated or segregated. However, it is unclear how these opposite connections can be formed with biologically realistic learning rules. We propose a network model capable of learning these opposite neurons, using Hebbian and Anti-Hebbian learning rules. The learned neurons are topographically organized and have von Mises-shaped feedforward connections, with tuning properties characteristic of opposite neurons. Our purpose is two-fold: on the one hand, we provide a circuit-level mechanism that explains the properties and formation of opposite neurons; on the other hand, we present a way to extend current theories of multisensory integration to account for appropriate segregation of sensory cues.

neuroscience↗