Topic: Cortical neuronal networks

Cortical neuronal networks exhibit a rich dynamical repertoire and are capable of performing many different computations, for instance the generation of multiple distinct dynamical patterns in motor cortex. However, we do not know what connectivity structures support this capability, nor the mechanism for selecting among the dynamics required for different tasks. We develop an analytical theory describing a network model capable of multi-task computation, where the connectivity is generated as a sum over a large number of low-rank matrices, each of which is specialized to perform one low-dimen

The modern world is increasingly dependent upon artificial intelligence and machine learning. Applications range from decision-making in areas such as health and finance to autonomous vehicle control. However, current deep-learning machines and neural network algorithms have important limitations, namely ineffective learning rules, long training, and high power consumption (1). In the present project, we aim to address these limitations by using the human nervous system itself as a model, which can process external information in a power-efficient way. To accomplish this objective, we utilize