Computational significance of transient dynamics in cortical networks

Authors

  • Daniel Durstewitz,

    1. Centre for Theoretical and Computational Neuroscience, University of Plymouth, Portland Square, Drake Circus, Plymouth PL4 8AA, UK
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  • Gustavo Deco

    1. Institucio Catalana de Recerca i Estudis Avancats (ICREA), Universitat Pompeu Fabra, Department of Technology Computational Neuroscience, Passeig de Circumval.lacio, 8, 08003 Barcelona, Spain
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Errata

This article corrects:

  1. Computational significance of transient dynamics in cortical networks Volume 27, Issue 1, 217–227, Article first published online: 17 December 2007

Abstract

In the published paper of Durstewitz & Deco (2007), there was an error in the Abstract (in the first sentence, the words ‘are correlated’ should have been moved to the end). In the same paper, as Fig. 6 was not printed in colour, the words ‘black’ and ‘red’ should have been replaced by ‘middle’ and ‘top and bottom’, respectively. The publishers apologize for these errors, and reproduce the corrected Abstract and Fig. 6 legend below.

Abstract

Neural responses are most often characterized in terms of the sets of environmental or internal conditions or stimuli with which their firing rate increases or decreases are correlated. Their transient (nonstationary) temporal profiles of activity have received comparatively less attention. Similarly, the computational framework of attractor neural networks puts most emphasis on the representational or computational properties of the stable states of a neural system. Here we review a couple of neurophysiological observations and computational ideas that shift the focus to the transient dynamics of neural systems. We argue that there are many situations in which the transient neural behaviour, while hopping between different attractor states or moving along ‘attractor ruins’, carries most of the computational and/or behavioural significance, rather than the attractor states eventually reached. Such transients may be related to the computation of temporally precise predictions or the probabilistic transitions among choice options, accounting for Weber's law in decision-making tasks. Finally, we conclude with a more general perspective on the role of transient dynamics in the brain, promoting the view that brain activity is characterized by a high-dimensional chaotic ground state from which transient spatiotemporal patterns (metastable states) briefly emerge. Neural computation has to exploit the itinerant dynamics between these states.

Fig. 6. (Top) Attractor landscapes underlying the different dynamic regimes shown in the bifurcation diagram: stable spontaneous state, multistable, and bistable. (Bottom) Bifurcation diagram of the minimal decision-making neural network as a function of the input λ. Middle line, spontaneous state; top and bottom lines, decision states. Here we used A = 1/60 and B = −8/3.

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