Abstract
Stimulus-Specific Adaptation (SSA) to repetitive stimulation is a phenomenon that has been observed across many different species and in several brain sensory areas. It has been proposed as a computational mechanism, responsible for separating behaviorally relevant information from the continuous stream of sensory information. Although SSA can be induced and measured reliably in a wide variety of conditions, the network details and intracellular mechanisms giving rise to SSA still remain unclear. Recent computational studies proposed that SSA could be associated with a fast and synchronous neuronal firing phenomenon called Population Spikes (PS). Here, we test this hypothesis using a mean-field rate model and corroborate it using a neuromorphic hardware. As the neuromorphic circuits used in this study operate in real-time with biologically realistic time constants, they can reproduce the same dynamics observed in biological systems, together with the exploration of different connectivity schemes, with complete control of the system parameter settings. Besides, the hardware permits the iteration of multiple experiments over many trials, for extended amounts of time and without losing the networks and individual neural processes being studied. Following this “neuromorphic engineering” approach, we therefore study the PS hypothesis in a biophysically inspired recurrent networks of spiking neurons and evaluate the role of different linear and non-linear dynamic computational primitives such as spike-frequency adaptation or short-term depression (STD). We compare both the theoretical mean-field model of SSA and PS to previously obtained experimental results in the area of novelty detection and observe its behavior on its neuromorphic physical equivalent model. We show how the approach proposed can be extended to other computational neuroscience modelling efforts for understanding high-level phenomena in mechanistic models.
Highlights
The auditory environment is composed of a significant amount of complex sounds from different sources that need to be organized by the nervous system to efficiently achieve stimulus processing
Similar to earlier studies that combine mean-field models and microscopic s imulations[17], we explored the properties of the neural network with a minimal mean-field rate model and implemented it on a mixed-signal analog-digital neuromorphic hardware circuit
The present work recreated a simplified version of the network presented in[15] while using a mean-field rate model and an implementation on a neuromorphic hardware. The latter represents a more realistic platform which aims at reproducing the dynamics and computational functions present in biological brains
Summary
The auditory environment is composed of a significant amount of complex sounds from different sources that need to be organized by the nervous system to efficiently achieve stimulus processing. Supported by computational modelling studies, the hypothesis of a depressed feed-forward thalamocortical input to the primary auditory cortex (A1) could explain SSA12–14 This hypothesis requires further investigation as the prediction suggested by these models does not fully concur with the experimental d ata[15]. The neuromorphic model used a biologically plausible substrate and allowed us to explore the computational role of population spikes (PS), i.e., a large group of neurons firing briefly and synchronously, whose existence has been proposed by numerous studies conducted in the auditory system and in A1. Our model supports the hypothesis proposed to the Yarden et al.’s hypothesis[15] that PS are essential in the development of SSA This intrinsic phenomenon, associated with the properties of the model, propagates laterally upon a rare (i.e. deviant) stimulus presentation.
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