Everton J. Agnes

To capture more complex plasticity mechanisms we constructed two higher dimensional and more expressive plasticity rule parameterizations, i.e., (1) a polynomial with additional dependencies and (2) a neural network parameterization (“MLP”, a feedforward network that determines the synaptic changes of the recurrent spiking network, see Methods). We benchmarked these two new parameterizations on the same stability task as for the small search space (Fig 2).

Biological memory is known to be flexible—memory formation and recall depend on factors such as the behavioral context of the organism. However, this property is often ignored in associative memory models, leaving it unclear how memories can be organized and recalled when subject to contextual control. Because of the lack of a rigorous analytical framework, it is also unknown how contextual control affects memory stability, storage capacity, and information content.

Abstract Synaptic plasticity is a key player in the brain′s life-long learning abilities. However, due to experimental limitations, the mechanistic link between synaptic plasticity rules and the network-level computations they enable remain opaque. Here we use evolutionary strategies (ES) to meta-learn local co-active plasticity rules in large recurrent spiking networks, using parameterizations of increasing complexity.