Bursting dynamics and network structural changes towards and away from a Pavlovian-conditioned neural network

Understanding the intricate relationship between neural network morphology, spiking dynamics, and function is a fundamental challenge in neuroscience. This study delves into this relationship in the renowned Pavlovian conditioning model proposed by Izhikevich to address the “distal reward problem.” The model consists of spiking neurons connected by synapses whose weights evolve based on dopamine-modulated spike-timing dependent plasticity and undergoes a specific Pavlovian conditioning protocol. Throughout conditioning, the network architecture undergoes a dramatic transformation, shifting from random recurrent connections to a predominant feed-forward structure, positioning a specific subpopulation of neurons targeted during conditioning at the network’s apex. Remarkably, this new structure, termed Pavlovian memory, can persist despite spontaneous population bursting activity driven by stochastic noise and residual recurrent connections. However, significantly elevated basal dopamine levels gradually induce morphological multi-stability, including a state of complete memory loss. These findings vividly illustrate the interplay of spiking dynamics, network morphology, and memory state within a dopamine-modulated Pavlovian conditioning system. We anticipate that employing a similar systematic approach could advance our understanding of more complex functions of spiking neural networks in both machine learning and the brain.