ePoster · Computational Neuroscience
Excitatory and inhibitory neurons exhibit distinct roles for task learning, temporal scaling, and working memory in recurrent spiking neural network models of neocortex.
Ulaş Ayyılmaz, Antara Krishnan, Yuqing Zhu · Bernstein Conference 2024
The brain encodes information in the spiking dynamics of recurrently connected inhibitory (I) and excitatory (E) neurons. Recurrent Spiking Neural Networks (RSNNs) have the potential to illuminate mechanisms of neurobiological computation. While inhibition and excitation underlie cortical dynamics, the roles of individual E and I neurons, as well as distinct I neuron subclasses, remain unclear. To elucidate these roles, we created biologically plausible RSNNs with leaky integrate and fire neurons and trained the networks on variations of a temporal task. We find that distinct interneuron subcl