Curto, C. | bioRxiv Science

bioRxiv2025

Topological analysis of neuronal assemblies reveals low-rank structure modulated by cholinergic activity

1Neuronal assemblies are fundamental building blocks of brain function (e.g. memory, spatial navigation, eye fixation, etc.). However, the principles underlying the connectivity patterns that support their function have remained elusive. The optic tectum of the zebrafish larva is organized into distinct functional neuronal assemblies. These assemblies display all-or-none preferred activation states and inhibitory competition, mechanisms that improve the decoding of visual information. Here, we combined light-sheet microscopy to capture the dynamics of large neuronal networks ([~]2,000 neurons) in the optic tectum; genetic cell-type markers for studying the physiological and functional properties of tectal assemblies; and techniques from topological data analysis to study the dynamic connectivity patterns that enable the emergence and functional role of the neuronal assemblies. We found that during spontaneous activations, tectal assemblies maintain a tight and stable ratio of E-I activity despite the large increase in activity. Topological analysis of the spontaneous activations indicated a low-rank organization of the assemblies and a discrete number of temporal activation patterns. Finally, we observed that the cholinergic system can modulate the topological features of the assemblies to alter their functional role.

neuroscience↗

bioRxiv2025

Attractor-based models for sequences and pattern generation in neural circuits

Neural circuits in the brain perform a variety of essential functions, including input classification, pattern completion, and the generation of rhythms and oscillations that support functions such as breathing and locomotion. There is also substantial evidence that the brain encodes memories and processes information via sequences of neural activity. Traditionally, rhythmic activity and pattern generation have been modeled using coupled oscillators, whereas input classification and pattern completion have been modeled using attractor neural networks. Here, we present a theoretical framework that demonstrates how attractor-based networks can also generate diverse rhythmic patterns, such as those of central pattern generator circuits (CPGs). Additionally, we propose a mechanism for transitioning between patterns. Specifically, we construct a network that can step through a sequence of five different quadruped gaits. It is composed of two dynamically distinct modules: a "counter" network, that can count the number of external inputs it receives via a sequence of fixed points; and a locomotion network, that encodes five different quadruped gaits as limit cycles. A sequence of locomotive gaits is obtained by connecting the counter network with the locomotion network. Specifically, we introduce a new architecture for layering networks that produces "fusion" attractors, binding pairs of attractors from individual layers. All of this is accomplished within a unified framework of attractor-based models using threshold-linear networks.

neuroscience↗