Reservoir Computing Properties of Neural Dynamics in Prefrontal Cortex

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.
Author Summary One of the most noteworthy properties of primate behavior is its diversity and adaptability. Human and non-human primates can learn an astonishing variety of novel behaviors that could not have been directly anticipated by evolution. How then can the nervous system be prewired to anticipate the ability to represent such an open class of behaviors? Recent developments in a branch of recurrent neural networks, referred to as reservoir computing, begins to shed light on this question. The novelty of reservoir computing is that the recurrent connections in the network are fixed, and only the connections from these neurons to the output neurons change with learning. The fixed recurrent connections provide the network with an inherent high dimensional dynamics that creates essentially all possible spatial and temporal combinations of the inputs which can then be selected, by learning, to perform the desired task. This high dimensional mixture of activity inherent to reservoirs has begun to be found in the primate cortex. Here we make direct comparisons between dynamic coding in the cortex and in reservoirs performing the same task, and contribute to the emerging evidence that cortex has significant reservoir properties.