Wave-Processing of Long-Scale Information by Neuronal Chains

Abstract

Investigation of mechanisms of information handling in neural assemblies involved in computational and cognitive tasks is a challenging problem. Synergetic cooperation of neurons in time domain, through synchronization of firing of multiple spatially distant neurons, has been widely spread as the main paradigm. Complementary, the brain may also employ information coding and processing in spatial dimension. Then, the result of computation depends also on the spatial distribution of long-scale information. The latter bi-dimensional alternative is notably less explored in the literature. Here, we propose and theoretically illustrate a concept of spatiotemporal representation and processing of long-scale information in laminar neural structures. We argue that relevant information may be hidden in self-sustained traveling waves of neuronal activity and then their nonlinear interaction yields efficient wave-processing of spatiotemporal information. Using as a testbed a chain of FitzHugh-Nagumo neurons, we show that the wave-processing can be achieved by incorporating into the single-neuron dynamics an additional voltage-gated membrane current. This local mechanism provides a chain of such neurons with new emergent network properties. In particular, nonlinear waves as a carrier of long-scale information exhibit a variety of functionally different regimes of interaction: from complete or asymmetric annihilation to transparent crossing. Thus neuronal chains can work as computational units performing different operations over spatiotemporal information. Exploiting complexity resonance these composite units can discard stimuli of too high or too low frequencies, while selectively compress those in the natural frequency range. We also show how neuronal chains can contextually interpret raw wave information. The same stimulus can be processed differently or identically according to the context set by a periodic wave train injected at the opposite end of the chain.

Spatial Memory in a Spiking Neural Network with Robot Embodiment

“…In addition, each population burst of spikes topographically corresponds to a wave or a patch of activity traveling in a network monolayer. The results of experiments and simulations allow us to hypothesize that such a moving patch of excitation plays the role of the central functional unit in the brain’s information processes [ 42 , 43 ]. Model simulations have shown the possibility of the formation of associative connections caused by the interaction of traveling waves [ 44 ].…”

Single-neuron criticality optimizes analog dendritic computation

“…In our previous models 37 38 , other parameters have been studied, but the time-length of the spikes that we introduce here proves to be a crucial factor. Such variability in the duration of the spike has been shown to shape the network dynamics 43 , and to enhance the computational capability of neuronal networks 44 . In the present model, the variable duration of dendritic spikes plays an important role in dendritic computation because it is capable of controlling the emergence of a phase transition.…”

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

“…Based on this hypothesis, the concept of spatial computing was proposed, which can be defined as computations in neural networks mediated by the interaction of waves and patches of propagating excitation. This coding principle enables the detection of different signals and performing various stimulus transformations, for example, signal frequency reduction ( Villacorta-Atienza and Makarov, 2013 ). One of the implementations of this concept can be considered a learning model in a neural network based on the STDP association of interacting traveling waves ( Alexander et al, 2011 ; Palmer and Gong, 2014 ).…”