Modeling Loop Formation in Cortical Circuits Using Spike Timing Dependent Plasticity
Date:
I delivered an oral presentation in the Kreiman Laboratory at Harvard University, summarizing the results of my summer research internship under the supervision of Professor Gabriel Kreiman. The computational neuroscience project focused on understanding how spike timing dependent plasticity (STDP) shapes the architecture of recurrent cortical circuits and the conditions under which specific connectivity patterns emerge.
The aim of the internship was to use computational simulations to investigate how local learning rules influence the formation and elimination of loops in neuronal networks. I implemented simulations of spiking neural circuits, analyzed how synaptic connectivity evolves under STDP, and examined how subtle differences in temporal firing relationships can give rise to large scale effects on network topology. This work required developing mathematical tools, writing custom simulation code, exploring different STDP formulations, and performing extensive parameter sweeps to probe the stability and dynamics of the resulting networks.
In the presentation, I described preliminary results showing how combinations of learning rules and temporal asymmetries affect the emergence of feed forward versus recurrent structure in cortical models. I also discussed how these mechanisms relate to hypotheses about circuit development, synaptic organization, and the computational principles that support visual intelligence.
Photos from the internship are available at Harvard Internship and KLAB Camping at Acadia National Park.