Generic origins of irregular spiking in neocortical networks

Abstract

We identify generic sources of complex and irregular spiking in biological neural networks. For the network description, we operate on a mathematically exact mesoscopic approach. Starting from experimental data, we determine exact properties of noise-driven, binary neuron interaction and extrapolate from there to properties of more complex types of interaction. Our approach fills a gap between approaches that start from detailed biophysically motivated simulations but fail to make mathematically exact global predictions, and approaches that are able to make exact statements but only on levels of description that are remote from biology. As a consequence of the approach, a novel coding scheme emerges, shedding new light on local information processing in biological neural networks.

Synchrony, stability, and firing patterns in pulse-coupled oscillators

“…Note that ∆ jk = 0 if oscillator k is not connected to oscillator j . Stoop et al [26] consider an alternative means of coupling between PRCs which is akin to "diffusive" coupling and coupled map lattices:…”

“…PRCs are popular among experimentalists as they provide a way to quantify the behavior of the system without knowing the underlying mechanisms responsible for the behavior. Indeed, PRCs have been computed for many biological oscillators [4,29] including neurons [23][24][25][26]. Stoop et al [26] have used experimental PRCs to devise coupled map lattices for arrays of nearest neighbor coupled neurons.…”

“…Indeed, PRCs have been computed for many biological oscillators [4,29] including neurons [23][24][25][26]. Stoop et al [26] have used experimental PRCs to devise coupled map lattices for arrays of nearest neighbor coupled neurons. Canavier and colleagues have taken a different approach and analyzed rings of uni-directionally coupled PRCs without forcing.…”

Natural computation measured as a reduction of complexity

“…The high computational efficiency of this two-step process could be fundamental for the efficacy of biological neural computation, 16 as it might explain the biological neuron's ease in performing efficient computation in noisy environments ͑or using noise even, to improve computation 17 ͒. In this context it is worthwhile noting that the information two ͑or more͒ stochastic neurons carry, can easily be converted into patterns by mutual phase-coupling.…”

In memory of Viktor Iosifovich Belinicher

“…This simplified analysis suggests that macroscopic chaos and synchronous behaviour have their origin in, and can be explained by, the corresponding mesoscopic properties. In a future contribution (Stoop et al 2000), we will concentrate on this issue by studying diffusively coupled map lattices of excitatory/inhibitory interaction, where simulations will be compared with analytical results from simplified models.…”