Abstract
Primates display a remarkable ability to adapt to novel situations. Determining what is most pertinent in these situations is not always possible based only on the current sensory inputs, and often also depends on recent inputs and behavioral outputs that contribute to internal states. Thus, one can ask how cortical dynamics generate representations of these complex situations. It has been observed that mixed selectivity in cortical neurons contributes to represent diverse situations defined by a combination of the current stimuli, and that mixed selectivity is readily obtained in randomly connected recurrent networks. In this context, these reservoir networks reproduce the highly recurrent nature of local cortical connectivity. Recombining present and past inputs, random recurrent networks from the reservoir computing framework generate mixed selectivity which provides pre-coded representations of an essentially universal set of contexts. These representations can then be selectively amplified through learning to solve the task at hand. We thus explored their representational power and dynamical properties after training a reservoir to perform a complex cognitive task initially developed for monkeys. The reservoir model inherently displayed a dynamic form of mixed selectivity, key to the representation of the behavioral context over time. The pre-coded representation of context was amplified by training a feedback neuron to explicitly represent this context, thereby reproducing the effect of learning and allowing the model to perform more robustly. This second version of the model demonstrates how a hybrid dynamical regime combining spatio-temporal processing of reservoirs, and input driven attracting dynamics generated by the feedback neuron, can be used to solve a complex cognitive task. We compared reservoir activity to neural activity of dorsal anterior cingulate cortex of monkeys which revealed similar network dynamics. We argue that reservoir computing is a pertinent framework to model local cortical dynamics and their contribution to higher cognitive function.
Highlights
One of the properties that sets primates apart in the animal kingdom is their extraordinary adaptation skills which are supported by efficient context-dependent learning mechanisms
We previously demonstrated that the dorsal anterior cingulate cortex (dACC) [21] and dorsolateral prefrontal cortex [22] play complimentary roles in this task [23]. dACC plays a greater role than DLPFC in integration of positive and negative feedback and tracking exploration vs. exploitation phases of the task
The present study demonstrates the presence of dynamic mixed selectivity in a complex cognitive task, both in a reservoir and in a prefrontal cortical area
Summary
One of the properties that sets primates apart in the animal kingdom is their extraordinary adaptation skills which are supported by efficient context-dependent learning mechanisms. Observed in early PFC studies while animals performed tasks involving multiple variables [3,4,5,6], mixed selectivity in the PFC has only recently become a specific research focus [7]. The authors demonstrated that these non-linear combinations of task variables were absent in PFC activity when monkeys made errors, emphasizing the importance of mixed selectivity in encoding behavioral context. This context can be defined with the current set of stimuli directly available from the environment, and with previous stimuli and actions that define the internal state of the agent
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