Population rate codes carried by mean, fluctuation and synchrony of neuronal firings

Abstract

A population of firing neurons is expected to carry information not only by mean firing rate but also by fluctuation and synchrony among neurons. In order to examine this possibility, we have studied responses of neuronal ensembles to three kinds of inputs: mean-, fluctuation- and synchrony-driven inputs. The generalized rate-code model including additive and multiplicative noise (H. Hasegawa, Phys. Rev. E {\bf 75} (2007) 051904) has been studied by direct simulations (DSs) and the augmented moment method (AMM) in which equations of motion for mean firing rate, fluctuation and synchrony are derived. Results calculated by the AMM are in good agreement with those by DSs. The independent component analysis (ICA) of our results has shown that mean firing rate, fluctuation (or variability) and synchrony may carry independent information in the population rate-code model. The input-output relation of mean firing rates is shown to have higher sensitivity for larger multiplicative noise, as recently observed in prefrontal cortex. A comparison is made between results obtained by the integrate-and-fire (IF) model and our rate-code model.

arXiv comment20 pages, 10 figures, accepted in Physica A (revised version of arXiv:0706.3489)

References in corpus (10)

• Response of Spiking Neurons to Correlated Inputs
• Dynamical mean-field theory of spiking neuron ensembles: response to a single spike with independent noises
• Responses of a Hodgkin-Huxley Neuron to Various Types of Spike-Train Inputs
• Switching Time Statistics for Driven Neuron Models: Analytic Expressions versus Numerics
• Dynamical mean-filed approximation to small-world networks of spiking neurons: From local to global, and/or from regular to random couplings
• Dynamical mean-field theory of noisy spiking neuron ensembles: Application to the Hodgkin-Huxley model
• Noise-Induced Phase Separation: Mean-Field Results
• Generalized Rate-Code Model for Neuron Ensembles with Finite Populations
• Stationary and dynamical properties of information entropies in nonextensive systems
• Effects of correlated variability on information entropies in nonextensive systems

Cited by in corpus (3)

• Mean-field dynamics of a random neural network with noise
• Generalized Fisher information matrix in nonextensive systems with spatial correlation
• Synchronization induced by periodic inputs in finite $N$-unit bistable Langevin models: The augmented moment method