Biology subjects

Marschall, O.

Publications and source records attributed to Marschall, O..

3 recordsLinked to original sources

bioRxiv2026

Neural circuit mechanisms for multi-tasking

Neural activity during performance of a cognitive or motor task is often described as lying on a low-dimensional manifold in the space of all possible activity patterns. When multiple tasks are performed, an individual brain region's activity may generate multiple such manifolds. Several models have been introduced to explain this capability, each with different assumptions concerning architecture and inputs. Here, we develop a theory of multi-tasking according to which models and experimental paradigms can be organized. We introduce a distinction between sequential and simultaneous multi-tasking, the performance of one task at a time or multiple in parallel. We show that interference between tasks often prevents multi-tasking and introduce a model of ``task barcodes" that circumvents this problem and parameterizes a neuronal population's task selectivity. Our theory reveals categorical distinctions between neural circuit mechanisms supporting multi-tasking that can be probed using physiological recordings.

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↗

bioRxiv2023

Probing learning through the lens of changes in circuit dynamics

Despite the success of dynamical systems as accounts of circuit computation and observed behavior, our understanding of how dynamical systems evolve over learning is very limited. Here we develop a computational framework for extracting core dynamical systems features of recurrent circuits across learning and analyze the properties of these meta-dynamics in model analogues of several brain-relevant tasks. Across learning algorithms and tasks we find a stereotyped path to task mastery, which involves the creation of dynamical systems features and their refinement to a stable solution. This learning universality reveals common principles in the organization of recurrent neural networks in service to function and highlights some of the challenges in reverse engineering learning principles from chronic population recordings of neural activity.

neuroscience↗