Literature DB >> 19003492

Definitions of state variables and state space for brain-computer interface : Part 1. Multiple hierarchical levels of brain function.

Walter J Freeman1.   

Abstract

Neocortical state variables are defined and evaluated at three levels: microscopic using multiple spike activity (MSA), mesoscopic using local field potentials (LFP) and electrocorticograms (ECoG), and macroscopic using electroencephalograms (EEG) and brain imaging. Transactions between levels occur in all areas of cortex, upwardly by integration (abstraction, generalization) and downwardly by differentiation (speciation). The levels are joined by circular causality: microscopic activity upwardly creates mesoscopic order parameters, which downwardly constrain the microscopic activity that creates them. Integration dominates in sensory cortices. Microscopic activity evoked by receptor input in sensation induces emergence of mesoscopic activity in perception, followed by integration of perceptual activity into macroscopic activity in concept formation. The reverse process dominates in motor cortices, where the macroscopic activity embodying the concepts supports predictions of future states as goals. These macroscopic states are conceived to order mesoscopic activity in patterns that constitute plans for actions to achieve the goals. These planning patterns are conceived to provide frames in which the microscopic activity evolves in trajectories that adapted to the immediate environmental conditions detected by new stimuli. This circular sequence forms the action-perception cycle. Its upward limb is understood through correlation of sensory cortical activity with behavior. Now brain-machine interfaces (BMI) offer a means to understand the downward sequence through correlation of behavior with motor cortical activity, beginning with macroscopic goal states and concluding with recording of microscopic MSA trajectories that operate neuroprostheses. Part 1 develops a hypothesis that describes qualitatively the neurodynamics that supports the action-perception cycle and derivative reflex arc. Part 2 describes episodic, "cinematographic" spatial pattern formation and predicts some properties of the macroscopic and mesoscopic frames by which the embedded trajectories of the microscopic activity of cortical sensorimotor neurons might be organized and controlled.

Entities:  

Year:  2006        PMID: 19003492      PMCID: PMC2288954          DOI: 10.1007/s11571-006-9001-x

Source DB:  PubMed          Journal:  Cogn Neurodyn        ISSN: 1871-4080            Impact factor:   5.082


  59 in total

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Authors:  Justin C Sanchez; Jose M Carmena; Mikhail A Lebedev; Miguel A L Nicolelis; John G Harris; Jose C Principe
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3.  Reduction of single-neuron firing uncertainty by cortical ensembles during motor skill learning.

Authors:  Dana Cohen; Miguel A L Nicolelis
Journal:  J Neurosci       Date:  2004-04-07       Impact factor: 6.167

4.  Analysis of the correlation between local field potentials and neuronal firing rate in the motor cortex.

Authors:  Yiwen Wang; Justin C Sanchez; Jose C Principe; Jeremiah D Mitzelfelt; Aysegul Gunduz
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2006

5.  Coherence of gamma-band EEG activity as a basis for associative learning.

Authors:  W H Miltner; C Braun; M Arnold; H Witte; E Taub
Journal:  Nature       Date:  1999-02-04       Impact factor: 49.962

Review 6.  Distributed modular architectures linking basal ganglia, cerebellum, and cerebral cortex: their role in planning and controlling action.

Authors:  J C Houk; S P Wise
Journal:  Cereb Cortex       Date:  1995 Mar-Apr       Impact factor: 5.357

Review 7.  Visual feature integration and the temporal correlation hypothesis.

Authors:  W Singer; C M Gray
Journal:  Annu Rev Neurosci       Date:  1995       Impact factor: 12.449

8.  Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human.

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9.  Instant neural control of a movement signal.

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Journal:  Nature       Date:  2002-03-14       Impact factor: 49.962

10.  Spatiotemporal analysis of prepyriform, visual, auditory, and somesthetic surface EEGs in trained rabbits.

Authors:  J M Barrie; W J Freeman; M D Lenhart
Journal:  J Neurophysiol       Date:  1996-07       Impact factor: 2.714

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  23 in total

1.  Simulated power spectral density (PSD) of background electrocorticogram (ECoG).

Authors:  Walter J Freeman; Jian Zhai
Journal:  Cogn Neurodyn       Date:  2008-10-02       Impact factor: 5.082

2.  Towards dynamical system models of language-related brain potentials.

Authors:  Peter Beim Graben; Sabrina Gerth; Shravan Vasishth
Journal:  Cogn Neurodyn       Date:  2008-04-29       Impact factor: 5.082

3.  Parallel reinforcement learning for weighted multi-criteria model with adaptive margin.

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Authors:  Sabrina Gerth; Peter Beim Graben
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6.  Computational models of reinforcement learning: the role of dopamine as a reward signal.

Authors:  R D Samson; M J Frank; Jean-Marc Fellous
Journal:  Cogn Neurodyn       Date:  2010-03-21       Impact factor: 5.082

Review 7.  Spatiotemporal scales and links between electrical neuroimaging modalities.

Authors:  Sara L Gonzalez Andino; Stephen Perrig; Rolando Grave de Peralta Menendez
Journal:  Med Biol Eng Comput       Date:  2011-04-12       Impact factor: 2.602

8.  Model based generalization analysis of common spatial pattern in brain computer interfaces.

Authors:  Gan Huang; Guangquan Liu; Jianjun Meng; Dingguo Zhang; Xiangyang Zhu
Journal:  Cogn Neurodyn       Date:  2010-06-06       Impact factor: 5.082

9.  A semi-supervised support vector machine approach for parameter setting in motor imagery-based brain computer interfaces.

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Journal:  Cogn Neurodyn       Date:  2010-06-08       Impact factor: 5.082

10.  Active dendrites mediate stratified gamma-range coincidence detection in hippocampal model neurons.

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Journal:  J Physiol       Date:  2015-06-25       Impact factor: 5.182

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