Reflections on a giant of brain science

Abstract

Walter J. Freeman was a giant of the field of neuroscience whose visionary work contributed various experimental and theoretical breakthroughs to brain research in the past 60 years. He has pioneered a number of Electroencephalogram and Electrocorticogram tools and approaches that shaped the field, while ''Freeman Neurodynamics'' is a theoretical concept that is widely known, used, and respected among neuroscientists all over the world. His recent death is a profound loss to neuroscience and biomedical engineering. Many of his revolutionary ideas on brain dynamics have been ahead of their time by decades. We summarize his following groundbreaking achievements: (1) Mass Action in the Nervous System, from microscopic (single cell) recordings, through mesoscopic populations, to large-scale collective brain patterns underlying cognition; (2) Freeman-Kachalsky model of multi-scale, modular brain dynamics; (3) cinematic theory of cognitive dynamics; (4) phase transitions in cortical dynamics modeled with random graphs and quantum field theory; (5) philosophical aspects of intentionality, consciousness, and the unity of brain-mind-body. His work has been admired by many of his neuroscientist colleagues and followers. At the same time, his multidisciplinary approach combining advanced concepts of control theory and the mathematics of nonlinear systems and chaos, poses significant challenges to those who wish to thoroughly understand his message. The goal of this commemorative paper is to review key aspects of Freeman's neurodynamics and to provide some handles to gain better understanding about Freeman's extraordinary intellectual achievement.

The Energy Coding of a Structural Neural Network Based on the Hodgkin–Huxley Model

“…Thus, the neural information can be expressed as energy at the level of molecule, neuron, network, cognition, behavior, and their combination such that it can unify the neural models among various levels. (2) The neural energy can be combined with spiking pattern of membrane potentials to resolve the neural information (Wang et al, 2006 , 2008 , 2016 , 2017 ; Wang and Wang, 2014 ; Wang R. et al, 2014 ; Wang Z. et al, 2014 ; Kozma, 2016 ; Yan et al, 2016 ; Zheng et al, 2016 ). (3) Neural energy can describe the interaction of large-scale neurons referring to the interaction of multiple brain regions that cannot be achieved by any conventional coding theory (Wang et al, 2009 ; Vuksanović and Hövel, 2016 ; Zhang et al, 2016 ; Déli et al, 2017 ; Peters et al, 2017 ).…”

The essence of neuronal activity from the consistency of two different neuron models

“…But when the brain is activated, it is difficult to obtain an accurate understanding of the neural activities in the brain in various states because of the averaging distribution of blood flow in the brain as well as nonlinear coupling relationship between blood flow and oxygen consumption. At the same time, we cannot understand the interaction among neurons in the brain area with such a method [1,2].…”

Personality development in psychotherapy: a synergetic model of state-trait dynamics

“…Finally, the phenomenological (psychological) model could be more closely linked to the neural mechanisms of human change processes. Emergent psychological mechanisms could be related to more basic (meso- and micro-level) neural network dynamics (Bonzon 2017 ; Freeman 2000 ; Haken 2004 ; Haken and Schiepek 2010 ; Kozma 2016 ) and by this, the promising approaches of computational systems neuroscience and computational systems psychology could be integrated.…”

“…Both levels are related to each other, but given our actual knowledge, there exist emergent qualities at the macro level (e.g., phenomenological consciousness) which cannot be fully reduced to the micro level. Anyway, the dynamics at two or more levels may be correlated (see the K model of Freeman 2000 , 2004 ; Kozma 2016 ). In one of our own studies we were able to show that order transitions in the dynamics of cognitions and emotions during psychotherapy (assessed by daily self-ratings) were timely related to pattern transitions of brain activity (assessed by repeated fMRI scans; Schiepek et al 2013 ).…”