Skip to content

Advertisement

  • Poster presentation
  • Open Access

Key features of neural variability emerge from self-organized sequence learning in a deterministic neural network

BMC Neuroscience201516 (Suppl 1) :P266

https://doi.org/10.1186/1471-2202-16-S1-P266

  • Published:

Keywords

  • Stimulus Onset
  • Spontaneous Activity
  • Input Sequence
  • Internal Model
  • Sequence Learning

Cortical responses to identical stimuli show high trial-to-trial variability. This variability is commonly interpreted as resulting from internal noise. However, much of the variability can be explained by the pre-stimulus spontaneous activity [1]. In fact, the contribution of this spontaneous activity to the evoked response is sufficiently strong to bias perceptual decisions [2]. Importantly, spontaneous activity is structurally similar to evoked activity [3] and this similarity may be the result of learning an internal model of the environment during development [4]. Consistent with this idea, spontaneous activity seems to be a superset of possible evoked responses [5] and trial-to-trial variability drops at stimulus onset [6]. At present, it is unclear how these features of neural variability arise in cortical circuits.

Here, we show that all of these phenomena emerge in a completely deterministic self-organizing recurrent network (SORN) model [7]. The network consists of recurrently connected excitatory and inhibitory populations of McCulloch-Pitts units. The dynamics are shaped by spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms in response to structured input sequences. After a period of self-organization, during which the network learns an internal model of the input sequences, we observe all phenomena mentioned above: evoked responses and perceptual decisions can be predicted from prior spontaneous activity, spontaneous activity outlines the realm of evoked responses, Fano factors drop at stimulus onset, and spontaneous activity closely matches evoked activity patterns. In addition, the network produces the common signs of Poissonian variability in single units.

In sum, our model demonstrates that key features of neural variability emerge in a fully deterministic network from self-organized sequence learning via the interaction of STDP and homeostatic plasticity mechanisms. These results suggest that the high trial-to-trial variability of neural responses need not be taken as evidence for noisy neural processing elements.
Figure 1
Figure 1

Two example results after self-organization. a) The neural variability drops at stimulus onset. b) Spontaneous and evoked activity become more similar during learning

Authors’ Affiliations

(1)
Frankfurt Institute for Advanced Studies (FIAS), Frankfurt, Germany
(2)
Ernst-Strüngmann Institute (ESI), Frankfurt, Germany

References

  1. Arieli A, Sterkin A, Grinvald A, Aertsen A: Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science. 1996, 273: 1868-1871.PubMedView ArticleGoogle Scholar
  2. Hesselmann G, Kell C a, Eger E, Kleinschmidt A: Spontaneous local variations in ongoing neural activity bias perceptual decisions. Proc Natl Acad Sci U S A. 2008, 105: 10984-10989.PubMedPubMed CentralView ArticleGoogle Scholar
  3. Kenet T, Bibitchkov D, Tsodyks M, Grinvald A, Arieli A: Spontaneously emerging cortical representations of visual attributes. Nature. 2003, 425: 954-956.PubMedView ArticleGoogle Scholar
  4. Berkes P, Orbán G, Lengyel M, Fiser J: Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science (80- ). 2011, 331: 83-87.View ArticleGoogle Scholar
  5. Luczak A, Barthó P, Harris KD: Spontaneous events outline the realm of possible sensory responses in neocortical populations. Neuron. 2009, 62: 413-425.PubMedPubMed CentralView ArticleGoogle Scholar
  6. Churchland MM, et al: Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nat Neurosci. 2010, 13: 369-378.PubMedPubMed CentralView ArticleGoogle Scholar
  7. Lazar A, Pipa G, Triesch J: Emerging Bayesian priors in a self-organizing recurrent network. Artificial Neural Networks and Machine Learning - ICANN. 2011, 127-134.Google Scholar

Copyright

Advertisement