Topic: Recurrent spiking networks

Synaptic plasticity is known to be a key player in the brain’s life-long learning abilities. However, due to experimental limitations, the nature of the local changes at individual synapses and their link with emerging network-level computations remain unclear. I will present a numerical, meta-learning approach to deduce plasticity rules from either neuronal activity data and/or prior knowledge about the network's computation. I will first show how to recover known rules, given a human-designed loss function in rate networks, or directly from data, using an adversarial approach. Then I will pr

Synaptic plasticity is thought to underlie learning and memory [1]. Plasticity rules can be active in different synaptic connection types, such as excitatory-excitatory (EE) and inhibitory-excitatory (IE) connections, with in silico and experimental evidence suggesting distinct mechanisms for each type [2,3,4]. While the interaction between connection types in learning and memory is an active research focus [5], studies typically probe only one type of plasticity at a time. Thus, at scale, the contributions of connection types and their co-active plasticity rules to memory processes remain unc

Recent theoretical models and experimental data have revealed that many neurons can exhibit homoclinic (HOM) spike-onset bifurcations by tuning variables like temperature, extracellular ion concentrations, or channel expression levels within the physiologically plausible range. In this HOM regime, spike trains have burst-like irregular firing induced by stochastic switches between attractors. While single-neuron bifurcations leading to HOM dynamics are well-studied (Hesse et al., 2022; Contreras et al., 2021; Hürkey et al., 2023; Schleimer et al., 2021; Niemeyer et al., 2021), their impact on