ePoster · Computational Neuroscience
Plasticity-driven circuit self-organization on spiking stabilized supralinear networks
Raul Adell Segarra, Dylan Festa, Dimitra Maoutsa, Julijana Gjorgjieva · Bernstein Conference 2024
Nonlinear rate-based models, such as stabilized supralinear networks (SSNs), have been crucial in explaining key aspects of cortical dynamics, including the nonlinear integration of visual inputs and the modulation of response variability. Yet, the absence of spike-based signals in these frameworks has hindered the study of millisecond-scale interactions between pairs of neurons, which have been shown to drive synaptic plasticity. For instance, spike-timing dependent plasticity (STDP), a phenomenological description of long-term synaptic plasticity, can shape neural circuits based on spike-bas