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      Robust Transient Dynamics and Brain Functions

      review-article

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          Abstract

          In the last few decades several concepts of dynamical systems theory (DST) have guided psychologists, cognitive scientists, and neuroscientists to rethink about sensory motor behavior and embodied cognition. A critical step in the progress of DST application to the brain (supported by modern methods of brain imaging and multi-electrode recording techniques) has been the transfer of its initial success in motor behavior to mental function, i.e., perception, emotion, and cognition. Open questions from research in genetics, ecology, brain sciences, etc., have changed DST itself and lead to the discovery of a new dynamical phenomenon, i.e., reproducible and robust transients that are at the same time sensitive to informational signals. The goal of this review is to describe a new mathematical framework – heteroclinic sequential dynamics – to understand self-organized activity in the brain that can explain certain aspects of robust itinerant behavior. Specifically, we discuss a hierarchy of coarse-grain models of mental dynamics in the form of kinetic equations of modes. These modes compete for resources at three levels: (i) within the same modality, (ii) among different modalities from the same family (like perception), and (iii) among modalities from different families (like emotion and cognition). The analysis of the conditions for robustness, i.e., the structural stability of transient (sequential) dynamics, give us the possibility to explain phenomena like the finite capacity of our sequential working memory – a vital cognitive function –, and to find specific dynamical signatures – different kinds of instabilities – of several brain functions and mental diseases.

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          Most cited references82

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          Neural networks and physical systems with emergent collective computational abilities.

          J Hopfield (1982)
          Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
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            Real-time computing without stable states: a new framework for neural computation based on perturbations.

            A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.
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              Synaptic theory of working memory.

              It is usually assumed that enhanced spiking activity in the form of persistent reverberation for several seconds is the neural correlate of working memory. Here, we propose that working memory is sustained by calcium-mediated synaptic facilitation in the recurrent connections of neocortical networks. In this account, the presynaptic residual calcium is used as a buffer that is loaded, refreshed, and read out by spiking activity. Because of the long time constants of calcium kinetics, the refresh rate can be low, resulting in a mechanism that is metabolically efficient and robust. The duration and stability of working memory can be regulated by modulating the spontaneous activity in the network.
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                Author and article information

                Journal
                Front Comput Neurosci
                Front. Comput. Neurosci.
                Frontiers in Computational Neuroscience
                Frontiers Research Foundation
                1662-5188
                31 January 2011
                13 June 2011
                2011
                : 5
                : 24
                Affiliations
                [1] 1simpleBioCircuits Institute, University of California San Diego La Jolla, CA, USA
                [2] 2simpleGrupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politénica Superior, Universidad Autónoma de Madrid Spain
                Author notes

                Edited by: Stefano Cardanobile, Albert-Ludwig University Freiburg, Germany

                Reviewed by: Stefano Fusi, Columbia University, USA; Tomoki Fukai, RIKEN Brain Science Institute, Japan

                *Correspondence: Mikhail I. Rabinovich, BioCircuits Institute, University of California San Diego, 9500 Gilman Drive 0328, La Jolla, CA 92093-0328, USA. e-mail: mrabinovich@ 123456ucsd.edu
                Article
                10.3389/fncom.2011.00024
                3116137
                21716642
                a6e1dd45-7e04-4c2f-93ee-28f91e144ddb
                Copyright © 2011 Rabinovich and Varona.

                This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.

                History
                : 11 September 2010
                : 09 May 2011
                Page count
                Figures: 5, Tables: 0, Equations: 7, References: 96, Pages: 10, Words: 8546
                Categories
                Neuroscience
                Review Article

                Neurosciences
                binding,stable heteroclinic channel,mental modes,low frequency oscillations,winnerless competition,transient neural dynamics,working memory,mental disorders

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