Chaos, nonlinear, and linear in human brain.pdf

Since 1960 at least, I have been sharing chaos thinking with my brother Fred, and reciprocally, learning about brain research from him. This is a brief reminiscence of those early years.

Journal of Mathematical Psychology, 2002

Commonwealth Scholar with Professor Stephen Link at McMaster University, Canada, in 1976. Heath's research has emphasized the role of new mathematical and computational techniques to the study and modeling of complex and nonlinear aspects of cognition. His experimental work has examined signal detection, categorization, memory, fatigue, handwriting, and the detection of behavior change. He has also developed nonlinear system identification models of attention and interference, nonstationary versions of the random walk model of choice response time, and adaptive, novelty sensitive models of human memory.

A plausible correlation is made between a period-doubling artificial neuron pair and experimentally found chaos in the squishy human brain. Preliminary statistical evaluations are done.

Nature Precedings, 2012

The brain is characterized by performing many different processing tasks ranging from elaborate processes as pattern recognition, memory or decision-making to more simple functionalities as linear filtering in image processing. Understanding the mechanisms by which the brain is able to produce such a different range of cortical operations remains a fundamental problem in neuroscience. Some recent empirical and theoretical results support the notion that the brain is naturally poised near critically between ordered and chaotic states. As the largest number of metastable states exists at a point near the transition, the brain can therefore access to a larger repertoire of behaviours.

We used fractal geometry and fractal dimension introductory argumentation as a framework to start understanding dynamical and complex biological systems to then introduce Hurst exponent estimation of chaos/no-chaos balance trend to explore the phenomenology and the information content of EEG data through time. We searched for measure proxy dynamical variables as potential biomarkers and/or endo-phenotypes that help us to figure out the multidimensionality and different timescale of simultaneous and crossed functional phenomena that manifests in the brain during executing any challenging task. We found consistencies in the way intra-and inter-individual differences express themselves through the EEG time series data analysis, and some degree of specificity and specialization in the frontal, temporal and occip-ital locations as well as brain interhemispheric cross-talk interaction modulating the chaos/no-chaos balance in the brain, during a projective process of imaging a dancing choreography. We recorded the brain activity of N = 9 professional dancers while executing the instruction of to imagine (by mean of a typical projective visualization) a future dancing performance as part of the requirement for to approve a specialization modern dance course and workshop (Kosmos In Movement, 2015).

Philosophy, Psychiatry, & Psychology, 2005

Acta Physica Polonica A, 2011

We describe several links between EEG data processing and quantum mechanics. Then we show examples of exploitation of methods commonly used in quantum chaos for EEG data analysis.

2003

This essay is a trial on establishing a multilayer model for human cognitive processes. Routine procedures are explained by a first level With linear dynamics. More sophisticated procedures are at a second self-adaptive level. Complicated behavior and self-organized patterns emerge from a nonlinear dynamics, belonging to a third level.

The Quarterly Journal of …, 2001

Response time (RT) is a commonly used measure of cognitive performance that is usually characterised as stochastic. However, useful information may be hidden in the apparently random fluctuations of RT. Dynamical systems analysis techniques allow an exploration of the alternative hypothesis that RT fluctuations are deterministic, albeit in a complex manner. We applied careful task construction and noise reduction and surrogate series tests to show that RT series from a forced -pace serial response time task have low dimensional chaotic characteristics. In Experiment 1, 80% of subjects' filtered RT series had low dimensionality, sensitive dependence on initial conditions, spectra close to 1/f, and stable attractor geometry across sessions. In Experiment 2, we showed that the size of the inter-stimulus interval (ISI) determined the number of subjects with low dimensional chaotic series. A small ISI caused 100% of subjects to respond in the chaotic regime, whereas only 25% had a low dimensional chaotic RT component when the ISI was large. We argue that demanding task requirements cause a reduction in the dimensionality of the dynamics, producing RT fluctuations that may reflect a response strategy for controlling RT.