Clark, D. G. | Search bioRxiv

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↗