Cognitive Phase Transitions in the Cerebral Cortex - by Robert Kozma, Walter J. Freeman

Cognitive Phase Transitions in the Cerebral Cortex - by Robert Kozma, Walter J. Freeman

By Robert Kozma, Walter J. Freeman

This interesting publication was once born out of the numerous discussions the authors had long ago 10 years in regards to the position of scale-free constitution and dynamics in generating clever habit in brains.
The microscopic dynamics of neural networks is easily defined by way of the existing paradigm established in a slender interpretation of the neuron doctrine. This ebook broadens the doctrine by means of incorporating the dynamics of neural fields, as first printed by way of modeling with differential equations (K-sets). The publication broadens that procedure by way of software of random graph conception (neuropercolation). The booklet concludes with various commentaries that exemplify the big variety of mathematical/conceptual ways to neural fields.
This e-book is meant for researchers, postdocs, and graduate scholars, who see the constraints of community thought and search a beachhead from which to embark on mesoscopic and macroscopic neurodynamics.

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Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields

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Extra info for Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields

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During each inhalation the activity converged to a narrow, steady frequency band carrying fixed AM/PM patterns. This change reflected the transition from a receiving state, which sustained a search trajectory through an unconstrained state space, to a transmitting state, which was tightly constrained by synaptic interactions. The constraints were represented by a low-dimensional attractor governing AM/PM pattern formation. A sharp discontinuity in the analytic frequency demarcated the transition just prior to the appearance of new AM/PM patterns.

In: Perlovsky L, Kozma R (eds) Neurodynamics of cognition and consciousness. Springer, Heidelberg, pp 11–38 56. Braitenberg V, Schuz A (1998) Cortex: statistics and geometry of neuronal connectivity, 2nd edn. Springer, Berlin References 33 57. Capolupo A, Freeman WJ, Vitiello G (2013) Dissipation of ‘dark energy’ by cortex in knowledge retrieval. Phys Life Rev, Online. 001 58. Freeman WJ (2015) Perspectives: mechanism and significance of global coherence in scalp EEG.

B The goodness of classification was reduced by removal of channels randomly selected, showing that every electrode contributed equally; from [40], Fig. 3, p. 115 patterns in 64 EEG signals from a standard 10–20 array on the scalp. Classification levels for pre- and post-stimulus periods in the 15–22 Hz pass band were evaluated by the binomial probability of the classification rate of EEG feature vectors extracted from frames determined by high synchrony in the analytic phase: 3 initial frames (I1, I2 and I3), 3 pre-stimulus control frames (C3, C2 and C1), and 6 post-stimulus test frames (T1, T2, T3, T4, T5 and T6) for each of six subjects, see Fig.

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