Dynamic transitions in the effective connectivity of interacting cortical areas
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1
Bernstein Center for Computational Neuroscience, Germany
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2
Max Planck Institute for Dynamics and Self-Organization , Germany
Long-range anatomic connections between distinct cortical local areas define a substrate network constraining the spatio-temporal complexity of neural responses and, particularly of brain rhythmic activity [1]. Such structural connectivity does not however coincide with effective connectivity, related to the more elusive question “Which areas cause the activity of which others?” [2]. Effective connectivity is directed and is often task-dependent, evolving even across different stages of a single task [3, 4]. These fast changes are incompatible with the slow variation of anatomical connections in a mature brain and might be explained as dynamical transitions in the collective organization of neural activity. We consider here small network motifs of interacting cortical areas (N = 2 ÷ 4), modeled first as mean-field rate units and then as large populations of spiking neurons. Intra-areal local couplings are mainly inhibitory while inter-areal longer-range couplings are purely excitatory. All the interactions are delayed. Sufficiently strong local delayed inhibition induces synchronous fast oscillations and for weak long-range excitation phase-locked multi-areal polyrhythms are obtained [5, 6]. Even when the structural networks are fully symmetric, varying the strength of local inhibition and the delays of local and long-range interactions generates dynamical configurations which spontaneously break the symmetry under permutation of the areas. The simplest example is provided by the N = 2 network in which transitions from in-phase or anti-phase to out-of-phase lockings with intermediate equilibrium phase-shifts are identified [6]. Areas leading in phase over laggard areas can therefore be unambiguously pinpointed. The natural emergence of directionality in inter-areal communication is probed analysing the time-series obtained from simulations with tools like cross wavelet transform [7] and spectral-based estimation of Granger causality [8]. Remarkably, for stronger inter-areal couplings, chaotic states emerge which amplify the asymmetries of the polyrhythms from which they originate. In such configurations, the firing rate of laggard areas undergoes significantly stronger and more irregular amplitude fluctuations than leading areas. Asymmetric chaotic states can be described as conditions of effective entrainment in which laggard areas are driven into chaos by the more periodic firing of leader areas. Fully symmetric structural networks can thus give thus rise to multiple alternative effective networks with reduced symmetry. Transitions between different effective connectivities are achieved via transient perturbations of the dynamics without need for costly rearrangements of the structural connections.
References
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Conference:
Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009.
Presentation Type:
Poster Presentation
Topic:
Dynamical systems and recurrent networks
Citation:
Battaglia
D and
Witt
A
(2009). Dynamic transitions in the effective connectivity of interacting cortical areas.
Front. Comput. Neurosci.
Conference Abstract:
Bernstein Conference on Computational Neuroscience.
doi: 10.3389/conf.neuro.10.2009.14.031
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Received:
26 Aug 2009;
Published Online:
26 Aug 2009.
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Correspondence:
Demian Battaglia, Bernstein Center for Computational Neuroscience, Göttingen, Germany, demian@nld.ds.mpg.de