Fluctuations and information filtering in coupled populations of spiking neurons with adaptation

Abstract

Finite-sized populations of spiking elements are fundamental to brain function, but also used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasi-renewal description of neurons with adaptation. We derive an integral equation with colored noise that governs the stochastic dynamics of the population activity in response to time-dependent stimulation and calculate the spectral density in the asynchronous state. We show that systems of coupled populations with adaptation can generate a frequency band in which sensory information is preferentially encoded. The theory is applicable to fully as well as randomly connected networks, and to leaky integrate-and-fire as well as to generalized spiking neurons with adaptation on multiple time scales.

References in corpus (4)

• Impact of network structure and cellular response on spike time correlations
• Recurrent Interactions in Spiking Networks with Arbitrary Topology
• Echoes in correlated neural systems
• Patterns of interval correlations in neural oscillators with adaptation

Cited by in corpus (8)

• Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size
• How single neuron properties shape chaotic dynamics and signal transmission in random neural networks
• Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity
• Fluctuation-dissipation relations for spiking neurons
• Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity
• Correlation transfer by layer 5 cortical neurons under recreated synaptic inputs in vitro
• How random connectivity shapes the fluctuating dynamics of finite-size neural populations
• Adjoint Method for Macroscopic Phase-Resetting Curves of Generic Spiking Neural Networks