Everton J. Agnes

This paper develops a framework linking dendritic excitatory-inhibitory balance and branch-specific gating to selective associative memory recall in neural circuits.

This paper uses evolutionary strategies to meta-learn plasticity rules in spiking networks, finding stable rules for synapse types but facing challenges with complex co-active rules.

This paper introduces a context-modular memory network model that enhances memory capacity through context-dependent neuronal and synaptic gating, showing improved stability and accessibility of memory patterns.

The paper introduces a framework to model synaptic codependency between excitatory and inhibitory plasticity, explaining how they contribute to memory formation and stability in neural networks.

The paper explores transient amplification regimes in neuronal networks by manipulating eigenspectra and non-normality in upper triangular matrices, showing that imaginary distribution manipulation can lead to high amplification despite biological constraints.

The paper shows that inhibitory plasticity rules shape receptive fields by modulating the correlation between excitatory and inhibitory inputs, allowing context-dependent changes in neuronal responses.

This paper uses meta-learning to discover synaptic plasticity rules that can produce desired neural functions, re-discovering known rules and finding new ones for complex tasks.