Abstract
High-level brain function such as memory, classification or reasoning can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware constitutes a fast and energy efficient substrate for the implementation of such neural computing architectures in technical applications and neuroscientific research. The functional performance of neural networks is often critically dependent on the level of correlations in the neural activity. In finite networks, correlations are typically inevitable due to shared presynaptic input. Recent theoretical studies have shown that inhibitory feedback, abundant in biological neural networks, can actively suppress these shared-input correlations and thereby enable neurons to fire nearly independently. For networks of spiking neurons, the decorrelating effect of inhibitory feedback has so far been explicitly demonstrated only for homogeneous networks of neurons with linear sub-threshold dynamics. Theory, however, suggests that the effect is a general phenomenon, present in any system with sufficient inhibitory feedback, irrespective of the details of the network structure or the neuronal and synaptic properties. Here, we investigate the effect of network heterogeneity on correlations in sparse, random networks of inhibitory neurons with non-linear, conductance-based synapses. Emulations of these networks on the analog neuromorphic hardware system Spikey allow us to test the efficiency of decorrelation by inhibitory feedback in the presence of hardware-specific heterogeneities. The configurability of the hardware substrate enables us to modulate the extent of heterogeneity in a systematic manner. We selectively study the effects of shared input and recurrent connections on correlations in membrane potentials and spike trains. Our results confirm ...
Highlights
Dynamical systems in nature often exhibit a remarkable degree of diversity, specialization, or anticorrelation across their components, despite equalizing factors such as common input or homogeneity in component and interaction parameters
We show that decorrelation by inhibitory feedback is effective even in highly heterogeneous networks with broad distributions of firing rates
We investigate the roles of shared input, feedback, and heterogeneity on input and output correlations in random, sparse networks of inhibitory leaky integrate-and-fire (LIF) neurons with conductance-based synapses (Table I), implemented on the analog neuromorphic-hardware chip Spikey (Fig. 1)
Summary
Dynamical systems in nature often exhibit a remarkable degree of diversity, specialization, or anticorrelation across their components, despite equalizing factors such as common input or homogeneity in component and interaction parameters. Decorrelation by negative feedback implements an efficient form of redundancy reduction. In biological systems, it may serve similar purposes as decorrelation in technical applications, where it is used in data compression (e.g., principal-component analysis [7]), cross-talk reduction (e.g., in digital signal processing [8]), echo suppression
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