Victor Pedrosa

This paper presents a framework where a spiking neural network learns a cognitive map using spike-timing dependent plasticity, showing connections to TD(λ) algorithm and implications for behavior and experience replays.

This paper introduces a voltage-dependent inhibitory synaptic plasticity model that maintains network homeostasis while allowing diverse neuronal dynamics across brain regions.

The paper shows that inhibitory synapses from parvalbumin and somatostatin interneurons undergo distinct forms of long-term plasticity that shape hippocampal network activity and spatial representations.

The paper proposes a computational model of hippocampal CA1 network dynamics to explain how somatic and dendritic inhibition interact to form and stabilize place fields during exploration.

Hippocampal pyramidal cells show branch-specific tuning for different place fields, and their coupling to their soma changes with experience of an environment.

The paper uses a computational model to show that input-dependent inhibitory plasticity (IDIP) maintains hippocampal spatial maps and network stability through network-centered homeostasis.