Topic: asynchronous irregular activity

Network models are often designed to capture selective aspects of cortical circuits. On one end, mechanistic models such as balanced spiking networks resemble activity regimes observed in data, but are often limited to simple computations. On the other end, functional models like trained deep networks can show comparable performance and dynamical motifs, but are far removed from experimental physiology. Here, we put forth a new framework for excitatory-inhibitory spiking networks which retains key properties of both mechanistic and functional models. Based on previous studies of the geometry o

Memories may be encoded in the brain via assemblies, groups of neurons that coactivate upon memory recall. The concept of Hebbian plasticity suggests that these assemblies are generated through synaptic plasticity, strengthening the recurrent connections within select groups of neurons that receive correlated stimulation. To remain stable in absence of such stimulation, these assemblies need to be self-reinforcing under the plasticity rule. Previous models of such assembly generation and maintenance required mechanisms of heterosynaptic competition or homeostatic plasticity often with biologic

Synaptic plasticity is the cornerstone of learning and memory. However, the mechanistic link between individual synaptic changes and emerging network functions remains elusive. Here, we apply a numerical method for meta-learning plasticity rules---filter Simulation-Based Inference (fSBI)---to in vivo data. We identify thousands of co-active excitatory(E)-to-E, E-to-inhibitory(I), I-to-E and I-to-I rules that can drive learning and memory functions in large recurrent spiking networks. We begin with large search spaces of flexibly-parameterized co-active plasticity rules. We then use fSBI to fil