Alex D. Reyes

The paper derives probabilistic rules for combining excitatory and inhibitory inputs in neural circuits to explain how neurons respond to stimuli and modulate their input-output properties.

This study develops models of cortical networks that incorporate short-term synaptic depression to explain both asynchronous and synchronous activity observed in vitro.

The paper defines topological conditions for reliable neural activity propagation across brain layers using simplicial complexes to assess input convergence and active neuron contributions.

The paper develops a mathematical framework to define the conditions for a place code in the auditory system, showing how frequency and intensity can be encoded through spatial and cluster size representations in neural networks.

The paper investigates how spike timing and firing rate propagate through in vitro feedforward neural networks, showing rate information is preserved across layers via NMDA-mediated synapses.