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Pereira-Obilinovic, U.

Publications and source records attributed to Pereira-Obilinovic, U..

2 recordsLinked to original sources

Bifurcation in space: Emergence of function modularity in the neocortex

Recent reports of widespread neural representations challenge the notion that brain areas are specialized in distinct aspects of cognition. Here we tackle this challenge, using connectome-based neocortex models endowed with macro-scopic gradients of neurobiological properties. We show that a bifurcation (an abrupt change of behavior) occurring locally in the cortical space gives rise to specialization of a subset of areas, that defines a multi-regional functional module, for subjective decision making or working memory coding. We found that mnemonic activity exhibits an inverted-V shaped pattern of timescales across the cortical hierarchy, which represents an experimentally testable prediction. A plethora of bifurcations in space coexist, corresponding to a variety of functional modules not well predicted by network analysis of structural modules; their associated timescale profiles could be observed in animals performing tasks that require different mental processes. These findings suggest bifurcation in space as a fundamental principle for understanding brains modular organization. TeaserThe theory of bifurcation in space mechanistically accounts for the emergence of functional specialization of cortical areas dedicated to distinct cognitive functions that is compatible with distributed neural representations in a multiregional cortex.

neuroscience↗

Brain mechanism of foraging: reward-dependentsynaptic plasticity or neural integration of values?

During foraging behavior, action values are persistently encoded in neural activity and updated depending on the history of choice outcomes. What is the neural mechanism for action value maintenance and updating? Here we explore two contrasting network models: synaptic learning of action value versus neural integration. We show that both models can reproduce extant experimental data, but they yield distinct predictions about the underlying biological neural circuits. In particular, the neural integrator model but not the synaptic model requires that reward signals are mediated by neural pools selective for action alternatives and their projections are aligned with linear attractor axes in the valuation system. We demonstrate experimentally observable neural dynamical signatures and feasible perturbations to differentiate the two contrasting scenarios, suggesting that the synaptic model is a more robust candidate mechanism. Overall, this work provides a modeling framework to guide future experimental research on probabilistic foraging.

neuroscience↗