Biology subjects

Clark, D. G.

Publications and source records attributed to Clark, D. G..

4 recordsLinked to original sources

bioRxiv2026

Structure, disorder, and dynamics in task-trained recurrent neural circuits

Across many brain areas, neurons produce heterogeneous, seemingly disordered responses. Yet such circuits cannot be purely random, since they must possess some structure to generate the representations and computations underlying behavior. How much structure is present in recurrent connectivity relative to disorder, and how the interaction between the two shapes population dynamics and single-neuron responses, remain incompletely understood. Recurrent neural networks trained to perform tasks have become a leading model of such circuits, but conventional training yields a single point in a vast space of task-compatible solutions, with no systematic way to explore this space and no theory of how internal representations vary within it. Without such a theory, the questions above cannot be addressed, and comparisons between trained networks and neural data are difficult to interpret. Here, we introduce a control parameter that governs the degree to which learning reshapes recurrent connectivity, interpolating between a reservoir regime and one in which recurrent weights are restructured by learning to produce task-relevant internal representations. Varying this parameter generates a family of task-compatible solutions whose internal dynamics differ in a controlled and interpretable way. We derive a dynamical mean-field theory showing that, while population-level dynamics converge to a deterministic limit, individual neurons are driven by independent samples from a single-neuron input-current distribution. When connectivity is random, this distribution is Gaussian. Recurrent restructuring drives it toward task-dependent, non-Gaussian forms. In linear networks, restructuring amplifies task-relevant frequencies. In nonlinear networks, it drives a phase transition from chaotic, high-dimensional activity to ordered, low-dimensional dynamics that generalize temporally beyond the training period. We apply the theory to a reaching task in which a recurrent network must reproduce macaque muscle activity, and find that optimally matching simultaneous motor-cortex recordings requires only a small degree of restructuring, with learned structure coexisting with random heterogeneity. These results suggest a broader picture in which large recurrent circuits are largely random but contain, to varying degrees, structured recurrent connectivity sufficient for generalizable, task-relevant representations.1

neuroscience↗

bioRxiv2025

A theory of multi-task computation and task selection

Neural activity during the performance of a stereotyped behavioral task is often described as low-dimensional, occupying only a limited region in the space of all firing-rate patterns. This region has been referred to as the "neural manifold" associated with a task. More recently, recordings of neural activity in animals challenged to perform multiple tasks have suggested that each task is associated with a different low-dimensional manifold. What connectivity structures underlie this flexibility in neural dynamics, and how is interference between the dynamics associated with different tasks avoided? We develop a theoretical model for multi-task computation in nonlinear recurrent neural networks whose connectivity is constructed as a weighted sum of many low-rank components, each encoding the dynamics associated with a different task. The model demonstrates that interference between different tasks dynamics limits flexible multi-tasking and can lead to chaotic fluctuations. However, small modulations of a networks effective connectivity overcome this interference. We derive the conditions that enable such task selection and characterize both single-neuron and population statistics in task-selected and unselected states. The model reveals the requirements for a single network to produce distinct dynamics confined to distinct neural manifolds and suggests circuit mechanisms that support this capability. Using the model, we propose different hypotheses for explaining the origin of high-dimensional neural activity in large-scale recordings.

neuroscience↗

bioRxiv2025

Associative synaptic plasticity creates dynamic persistent activity

In biological neural circuits, the dynamics of neurons and synapses are tightly coupled. We study the consequences of this coupling and show that it enables a novel form of working memory. In recurrent neural network models with ongoing Hebbian plasticity, we find that following oscillatory stimulation, neurons continue to oscillate long after the input is removed. This creates a dynamic form of memory that has no explicit storage or retrieval phases and that requires no prior knowledge of the input. We trace the mechanism of these "persistent oscillations" to an interaction between neurons and synapses that creates complex outlier eigenvalues of the connectivity matrix. This is shown both in simulation and analytically. We leverage this mechanistic understanding to generate persistent oscillations with prespecified dynamics, creating a dynamic analog of a classical Hopfield network. Our work demonstrates that coupling neuronal and synaptic dynamics enables novel forms of computation.

neuroscience↗

bioRxiv2025

Symmetries and continuous attractors in disordered neural circuits

A major challenge in neuroscience is reconciling idealized theoretical models with complex, heterogeneous experimental data. We address this challenge through continuous-attractor networks, which model how neural circuits represent continuous variables such as head direction or spatial location through collective dynamics. Classical continuous-attractor models rely on continuous symmetry in the recurrent weights to generate a manifold of stable states, predicting tuning curves that are identical up to shifts. However, mouse head-direction cells exhibit substantial heterogeneity in their responses, seemingly incompatible with this classical picture. We demonstrate that mammalian circuits could nevertheless rely on the same dynamical mechanisms as classical continuous-attractor models. We construct recurrent neural networks directly from experimental head-direction tuning curves that exhibit quasi-continuous-attractor dynamics, then develop a statistical generative process quantitatively capturing the structure of tuning heterogeneity. This enables large-N analysis, where we show through dynamical mean-field theory that these networks become equivalent to classical ring-attractor models, with Mexican-hat interactions and continuous symmetry that is spontaneously broken, leading to bump states. In the seemingly disordered weights, the continuous symmetry essential to classical models is reflected through eigenvalue degeneracies, positioning spectral structure as a target for detecting continuous-attractor circuits in connectome data. We extend this framework to two-dimensional symmetries, constructing grid-cell models that similarly reduce to classical toroidal attractors. Our work demonstrates that the dynamical mechanisms of classical continuous-attractor models may operate not only in small brains or idealized systems but also in complex mammalian circuits.

neuroscience↗