Cognitive network interactions through communication subspaces in large-scale models of the neocortex.

Neocortex-wide neural activity is organized into distinct networks of areas engaged in different cognitive processes. To elucidate the underlying mechanism of flexible network reconfiguration, we developed connectivity-constrained macaque and human whole-cortex models. In our model, within-area connectivity consists of a mixture of symmetric, asymmetric, and random motifs that give rise to stable (attractor) or transient (sequential) heterogeneous dynamics.

Brain mechanism of foraging: Reward-dependent synaptic plasticity versus 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.

Bifurcation in space: Emergence of functional modularity in the neocortex.

How does functional modularity emerge in a cortex composed of repeats of a canonical local circuit? Focusing on distributed working memory, we show that a rigorous description of bifurcation in space describes the emergence of modularity. A connectome-based model of monkey cortex displays bifurcation in space during decision-making and working memory, demonstrating this new concept's generality. In a generative model and multi-regional cortex models of both macaque monkey and mouse, we found an inverted-V-shaped profile of neuronal timescales across the cortical hierarchy during working memory, providing an experimentally testable prediction of modularity.

Metastable attractors explain the variable timing of stable behavioral action sequences.

The timing of self-initiated actions shows large variability even when they are executed in stable, well-learned sequences. Could this mix of reliability and stochasticity arise within the same neural circuit? We trained rats to perform a stereotyped sequence of self-initiated actions and recorded neural ensemble activity in secondary motor cortex (M2), which is known to reflect trial-by-trial action-timing fluctuations. Using hidden Markov models, we established a dictionary between activity patterns and actions.

From synapse to network: models of information storage and retrieval in neural circuits.

The mechanisms of information storage and retrieval in brain circuits are still the subject of debate. It is widely believed that information is stored at least in part through changes in synaptic connectivity in networks that encode this information and that these changes lead in turn to modifications of network dynamics, such that the stored information can be retrieved at a later time. Here, we review recent progress in deriving synaptic plasticity rules from experimental data and in understanding how plasticity rules affect the dynamics of recurrent networks.