Neural circuit mechanisms for multi-tasking
- 1Columbia University
Abstract
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.
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BibTeX
@misc{beiran2026neural,
title = {{Neural circuit mechanisms for multi-tasking}},
author = {M. Beiran and O. Marschall and K. Zemlianova and A. Di Bernardo and S. Ostojic and A. Litwin-Kumar},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.01.755960},
url = {https://www.biorxiv.org/content/10.64898/2026.10.01.755960v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Neural circuit mechanisms for multi-tasking
AU - M. Beiran
AU - O. Marschall
AU - K. Zemlianova
AU - A. Di Bernardo
AU - S. Ostojic
AU - A. Litwin-Kumar
PY - 2026
DA - 2026/10/01
PB - bioRxiv
DO - 10.64898/2026.10.01.755960
UR - https://www.biorxiv.org/content/10.64898/2026.10.01.755960v1
AB - 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.
ER -