Skip to main content
  • Poster presentation
  • Open access
  • Published:

Recurrent networks expect

How the brain anticipates future events is one of the most intriguing questions in neural information processing. The brain does not simply respond to the external world but in predictably structured environments like natural language, birdsong and somatosensory flow in fluids predicts it [1]. Reactions are improved by predictive encoding of what sensory stimuli may occur as well as when they may occur. Quantitative studies of implicit timing in streaming perceptual discrimination tasks show that performance is enhanced if stimuli occur at expected times according to an established rhythm. The neural basis for implicit timing is not fully understood but oscillatory entrainment mechanisms have been suggested [2]. Recent studies confirm that low-frequency brain oscillations do become phase entrained in such tasks. Phase entrainment increases with the temporal regularity of the sensory stream and correlates with enhanced discrimination performance [3]. It is not limited to sensory cortices; larger scale cortical networks, as well as subcortical networks like the basal ganglia [4], may also be coherently modulated by the predictability of stimuli streams. The presence of large scale oscillatory entrainment suggests that network mediated neuronal ensemble dynamics may be involved. Recurrent neural networks generate complex but reproducible temporally extended dynamical activity patterns in response to input stimuli [5, 6]. Such transient activity patterns have been suggested to provide a natural substrate for working memory and a representation of elapsed time.

Here we add to the understanding of how random recurrent neural networks support neural information processing by demonstrating that temporal expectation also naturally emerges from their dynamics. We show that the weakly chaotic oscillations generated by recurrent networks can be phase synchronized [7] by temporally regular stimulus sequences. We show network responses are maximally discriminative when stimuli fall at their preferred phase in phase entrained networks (Figure 1). Discriminability increases continuously with both temporal regularity and stimulus type predictability which also interact. Our results do not depend on specific network characteristics, are resilient to the presence of network noise and random distractor stimuli and are consistent with multiple streaming perceptual discrimination studies.

Figure 1
figure 1

Excess correct classification probability of on-time target stimuli compared to mistimed target stimuli (100 ms early, black ; 100 ms late, red) in a temporally regular streaming task with inter-trial-interval 800 ms. Results are plotted versus the Lyapunov exponent of the autonomous network dynamics.


  1. Nobre AC, Correa A, Coull JT: The hazards of time. Curr. Op. Neurobio. 2007, 17 (4): 465-470.

    Article  CAS  Google Scholar 

  2. Large EW, Jones MR: The dynamics of attending: How people track time-varying events. Psych. Rev. 1999, 106 (1): 119-

    Article  Google Scholar 

  3. Cravo AM, Rohenkohl G, Wyart V, Nobre AC: Temporal expectation enhances contrast sensitivity by phase entrainment of low-frequency oscillations in visual cortex. J. Neurosci. 2013, 33 (9): 4002-4010.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  4. Geiser E, Notter M, Gabrieli JD: A corticostriatal neural system enhances auditory perception through temporal context processing. J. Neurosci. 2012, 32 (18): , 6177-

    Article  PubMed  CAS  Google Scholar 

  5. Buonomano DV, Merzenich MM: Temporal information transformed into a spatial code by a neural network with realistic properties. Science. 1995, 267: 1028-1030.

    Article  PubMed  CAS  Google Scholar 

  6. Ponzi A, Wickens JR: Optimal Balance of the Striatal Medium Spiny Neuron Network. PLoS comp bio. 2013, 9 (4): e1002954-

    Article  CAS  Google Scholar 

  7. Pikovsky A, Zaks M, Rosenblum M, Osipov G, Kurths J: Phase synchronization of chaotic oscillations in terms of periodic orbits. Chaos. 1997, 7 (4): 680-687.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Adam Ponzi.

Rights and permissions

This article is published under license to BioMed Central Ltd. 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 work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ponzi, A., Wickens, J.R. Recurrent networks expect. BMC Neurosci 16 (Suppl 1), P285 (2015).

Download citation

  • Published:

  • DOI: