Biology subjects

Dubreuil, A.

Publications and source records attributed to Dubreuil, A..

3 recordsLinked to original sources

bioRxiv2026

Assembling Neural Latching Switch Circuits for temporally structured behavior

Elaborated temporal structures in behavior have been recognized as a hall-mark of high-level cognitive processes, such as planning or natural language. Systems neuroscience experiments in behaving animals have validated cell assemblies as a fundamental piece of neural circuitry underlying lower-level cognitive processes. Although recent research has identified neural bases for certain temporally structured behaviors, it is still an open question whether a general-purpose circuit - akin to cell assemblies - supports such behaviors. Here we show how Neural Latching Switch Circuits can be assembled as lego-bricks to create neural architectures suited for the implementation of any procedural behavior. By focusing on various forms of temporal structures and leveraging a combination of theoretical tools, we demonstrate how these circuits can be mapped onto interacting brain areas and propose predictions for identifying analogous structures in mammalian brains, for instance in cortical columns. By interpreting neural architectures as computing automata we reveal surprising relationships between behavior and computation.

neuroscience↗

bioRxiv2025

Neural Latching Switch Circuits for temporally structured behavior

Cognitive processes rely on switches between mental states. Mental states are thought to be supported by cell assembly activations as introduced by Hebb. This idea has been formalized using attractor neural networks, leading to detailed mechanistic and quantitative characterizations, allowing for confrontations with neurobiology experiments. However such a mechanistic understanding is lacking for switches between mental states. Here we introduce Neural Latching Switch Circuits (NLSC) which are composed of an attractor neural network, augmented with gate neurons allowing to program transitions between network states. We explore conditions under which such circuits emerge in artificial neural networks, and provide a quantitative description by embodying NLSC into models of binary neurons. We show how NLSC can be mapped onto the flys head-direction system and put forward signatures of NLSC for identifying such a structure in other brain circuits. Throughout examples, we show that NLSC are suited to implement computations relevant for sensory, motor, or more abstract types of processing. To propose a meaningful characterization of NLSC in these various contexts, we interpret them as finite-state automata, identifying attractor-states and automata-states, a first step in establishing a connection between neural and symbolic computations.

neuroscience↗

bioRxiv2019

Can grid cell ensembles represent multiple spaces?

The way grid cells represent space in the rodent brain has been a striking discovery, with theoret-ical implications still unclear. Differently from hippocampal place cells, which are known to encode multiple, environment-dependent spatial maps, grid cells have been widely believed to encode space through a single low dimensional manifold, in which coactivity relations between different neurons are preserved when the environment is changed. Does it have to be so? Here, we compute - using two alternative mathematical models - the storage capacity of a population of grid-like units, em-bedded in a continuous attractor neural network, for multiple spatial maps. We show that distinct representations of multiple environments can coexist, as existing models for grid cells have the po-tential to express several sets of hexagonal grid patterns, challenging the view of a universal grid map. This suggests that a population of grid cells can encode multiple non-congruent metric rela-tionships, a feature that could in principle allow a grid-like code to represent environments with a variety of different geometries and possibly conceptual and cognitive spaces, which may be expected to entail such context-dependent metric relationships.

neuroscience↗