Volume 14 Supplement 1

Abstracts from the Twenty Second Annual Computational Neuroscience Meeting: CNS*2013

Open Access

Identifying sources of non-stationary neural ensemble dynamics

  • Emili Balaguer-Ballester1, 2Email author,
  • Hamid Bouchachia1 and
  • Christopher C Lapish3
BMC Neuroscience201314(Suppl 1):P15

DOI: 10.1186/1471-2202-14-S1-P15

Published: 8 July 2013

In the traditional view on brain activity dynamics, the cognitive flow of information wanders through multiple stable states driven by task-dependent inputs [13]. This focus has been recently challenged both empirically and from the modeling perspective. For instance, experimental studies suggest that olfactory [4] and gustatory representations [5] can be understood as a sequence of temporally stable, attractor-like states; but such transient states are essentially transient and driven by stochastic fluctuations. Likewise, in several contemporary models, intrinsic activity fluctuations can drive default transitions between states [6, 7].

It has been recently proposed that such transient states are basically shaped by anatomical connectivity and transitions between them occur even in the absence of external stimuli [8]: Noise enriches the dynamical repertoire of deterministic states; creating flexible 'ghost' attractors which enable the effective processing of task-related cognitive entities [7].

A different metaphor of transient brain dynamics was proposed by Rabinovich and colleagues [9]. In such model, transitions between states mapping cognitive entities is purely deterministic: The dynamical portrait of the model consists of successions of temporally stable states i.e. metastable saddle points linked by heteroclinic channels. Such dynamical objects are particularly reliable, but neural activity eventually switches between them even without the intervention of noise or external inputs.

In this work we develop an empirical criterion to discern whether observable neural ensemble activity can be originated by non-autonomous yet deterministic dynamical systems or rather by stochastic fluctuations between temporally attracting states. Towards this goal, we used in vivo multiple single-unit recording in rodent frontal cortex during a decision making task. Effective dynamics of neural activity is first empirically reconstructed in nonlinear state spaces [10]. Then, trajectory analyses enable us to differentiate systems driven by non-automatous dynamics from those driven by stochastic transitions.


Underlying dynamics of recorded ensemble activity is probably driven by a slowly drifting, non-autonomous dynamical system containing low-order nonlinear interactions.

Authors’ Affiliations

School of Engineering and Computing, Bournemouth University
Bernstein Center for Computational Neuroscience, ZI Mannheim-University of Heidelberg
Department of Psychology, Indiana University-Purdue University


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© Balaguer-Ballester et al; licensee BioMed Central Ltd. 2013

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 (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.