Where's the noise? : key features of spontaneous activity and neural variability arise through learning in a deterministic network

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experim
Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network's spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network's behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms.
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Metadaten
Author:Christoph Hartmann, Andreea Lazar, Bernhard Nessler, Jochen Triesch
URN:urn:nbn:de:hebis:30:3-392292
DOI:http://dx.doi.org/10.1371/journal.pcbi.1004640
ISSN:1553-734X
ISSN:1553-7358
Parent Title (English):Plos Computational Biology, volume 11, issue 12, e1004640 (2015)
Publisher:PLoS
Place of publication:Lawrence, Kan.
Document Type:Article
Language:English
Date of Publication (online):2015/12/29
Date of first Publication:2015/12/29
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2016/02/04
Volume:11
Issue:(12): e1004640
Pagenumber:35
First Page:1
Last Page:35
Note:
Copyright: © 2015 Hartmann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
HeBIS PPN:376968303
Institutes:Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0

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