October 20, 2020

Stability by gating plasticity in recurrent neural networks

With Hebbian learning ‘who fires together wires together’, well-known problems arise. On the one hand, plasticity can lead to unstable network dynamics, manifesting as run-away activity or silence. On the other hand, plasticity can erase or overwrite stored memories. Unstable dynamics can partly be addressed with homeostatic plasticity mechanisms. Unfortunately, the time constants of homeostatic mechanisms required in network models are much shorter than what has been measured experimentally. Here, we propose that homeostatic time constants can be slow if plasticity is gated. We investigate how the gating of plasticity influences the stability of network activity and stored memories. We use plastic balanced spiking neural networks consisting of excitatory neurons with a somatic and a dendritic compartment (which resemble cortical pyramidal cells in their firing properties), and inhibitory neurons targeting those compartments. We compare how different factors such as excitability, learning rate, and inhibition can lift the requirements for the critical time constant of homeostatic plasticity. We specifically investigate how gating of dendritic versus somatic plasticity allows for different amounts of weight changes in networks with the same critical homeostatic time constant. We suggest that the striking compartmentalisation of pyramidal cells and their inhibitory inputs enable large synaptic changes at the dendrite while maintaining network stability. We additionally show that spatially restricted plasticity in a subpopulation of the network improves stability. Finally, we compare how the different gates affect the stability of memories in the network.

 bioRxiv Subject Collection: Neuroscience

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