Description: Deterministic networks for probabilistic computing

This title appears in the Scientific Report : 2019

Deterministic networks for probabilistic computing

Neuronal network models of high-level brain functions such as memory recall and reasoning often rely on the presence of some form of noise. The majority of these models assumes that each neuron in the functional network is equipped with its own private source of randomness, often in the form of unco...

Personal Name(s):	Jordan, Jakob (Corresponding author)
	Petrovici, Mihai A. / Breitwieser, Oliver / Schemmel, Johannes / Meier, Karlheinz / Diesmann, Markus / Tetzlaff, Tom
Contributing Institute:	Jara-Institut Brain structure-function relationships; INM-10 Theoretical Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6
Published in:	Scientific reports, 9 (2019) 1, S. 18303
Imprint:	[London] Macmillan Publishers Limited, part of Springer Nature 2019
PubMed ID:	31797943
DOI:	10.1038/s41598-019-54137-7
Document Type:	Journal Article
Research Program:	Human Brain Project Specific Grant Agreement 1 The Human Brain Project Advanced Computing Architectures Supercomputing and Modelling for the Human Brain Theory, modelling and simulation Brain-inspired multiscale computation in neuromorphic hybrid systems
Link:	Get full text Get full text OpenAccess OpenAccess
	Publikationsportal JuSER

Please use the identifier: http://dx.doi.org/10.1038/s41598-019-54137-7 in citations.
Please use the identifier: http://hdl.handle.net/2128/23586 in citations.

Neuronal network models of high-level brain functions such as memory recall and reasoning often rely on the presence of some form of noise. The majority of these models assumes that each neuron in the functional network is equipped with its own private source of randomness, often in the form of uncorrelated external noise. In vivo, synaptic background input has been suggested to serve as the main source of noise in biological neuronal networks. However, the finiteness of the number of such noise sources constitutes a challenge to this idea. Here, we show that shared-noise correlations resulting from a finite number of independent noise sources can substantially impair the performance of stochastic network models. We demonstrate that this problem is naturally overcome by replacing the ensemble of independent noise sources by a deterministic recurrent neuronal network. By virtue of inhibitory feedback, such networks can generate small residual spatial correlations in their activity which, counter to intuition, suppress the detrimental effect of shared input. We exploit this mechanism to show that a single recurrent network of a few hundred neurons can serve as a natural noise source for a large ensemble of functional networks performing probabilistic computations, each comprising thousands of units.