Memory formation and recall in recurrent spiking neural networks

Our brain has the capacity to analyze a visual scene in a split second, to learn how to play an instrument, and to remember events, faces and concepts. Neurons underlie all of these diverse functions. Neurons, cells within the brain that generate and transmit electrical activity, communicate with each other through chemical synapses. These synaptic connections dynamically change with experience, a process referred to as synaptic plasticity. These synaptic changes are thought to be at the core of the brain’s ability to learn and process the world in sophisticated ways. Our understanding of the rules of synaptic plasticity remain quite limited. To enable efficient computations among neurons or to serve as a trace of memory, synapses must create stable connectivity patterns between neurons. However there remains an insufficient theoretical explanation as to how stable connectivity patterns can exist in the presence of synaptic plasticity. What complicates and limits our understanding is that the dynamics of recurrently connected neurons depend upon their connections, which themselves change in response to the network dynamics. The recursive nature of the problem necessitates that the network connectivity and the synaptic plasticity be treated as a single compound system. Due to the nonlinear nature of the problem this quickly becomes analytically challenging. Utilizing network simulations that model the interplay between the network connectivity and synaptic plasticity can provide insight into this problem. However, most existing network models that implement biologically relevant forms of plasticity become unstable, developing seizure like activity. This suggests that these models do not accurately describe the biological networks, which have no difficulty functioning without succumbing to exploding network activity. The instability in these network simulations could originate from the fact that theoretical studies have, almost exclusively, focused on Hebbian plasticity at excitatory synapses. Hebbian plasticity causes connected neurons that are active together to increase the connection strength between them. Biological networks, however, display a large variety of different forms of synaptic plasticity and homeostatic mechanisms, beyond Hebbian plasticity. Furthermore, inhibitory cells can undergo synaptic plasticity as well. These diverse forms of plasticity are active at the same time, and our un-