A case for chaos theory inclusion in neuropsychoanalytic modeling

ABSTRACT

This paper provides a review of the current evidence of chaoticity at various scales of the brain–mind as well as the application of nonlinear tools in clinical practice. Based on these data, a hypothesis is formulated that the brain–mind at various scales can operate in linear, nonlinear, or hybrid modes, such as chaotic functioning accompanied by noise. A thesis formulated by Mark Solms that living systems must minimize Shannon’s entropy of physical states (sensory entropy) is considered. Based on the data presented in this paper, minimization of entropy in that sense appears to be describing only a part of the complex brain–mind dynamics. Studies evaluating measures of entropy specifically developed for real living systems such as discrete timescale entropy (ApEn) suggest that a decrease in EEG entropy can be observed in some neuronal processes (e.g. progression from wakefulness to deep sleep); however, EEG entropy is observed to be increasing at other times and in other modes of brain–mind functioning (e.g. progression from deep sleep to REM to wakefulness; and from vegetative state to wakefulness). The clinical implications are discussed. This paper proposes that it would be theoretically and clinically beneficial for future revisions of neuropsychoanalytic models to consider including the chaos theory framework.

KEYWORDS:

• Chaos
• nonlinear
• brain–mind
• consciousness
• dynamical systems

Acknowledgments

I am grateful to the reviewers of the paper for their insightful suggestions and to Igor Novak, Nancy McWilliams, Michael Levin, Jeffrey Burgdorf, Loring Ingraham, Peter Smith, and Allison St. John for their valuable comments on the early version of this paper.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1 As we will see later, there is an intimate relationship between chaos and non-linear dynamics.

2 For illustration, the reader is invited to view Figure 12C in Faure and Korn (2001), where you can see areas of linearity, stochasticity, and chaos, as well hybrid areas, such as chaos+noise.

3 For illustrations, the reader is invited to view Figures 8 and 10 in Faure and Korn (2001).

4 Such as disassembling a watch or a car engine to find a faulty part.

5 Shannon wrote about a continuous version of entropy on page 35 of his paper (part III, Section 20) by replacing a summation with an integral; however, it is not identical in all respects to the discrete version. E.T. Jaynes further elaborated continuous version of entropy as limiting density of discrete points (LDDP) (Jaynes, 1957).

6 Two studies related to autism spectrum disorder and major depressive disorder diagnosis were excluded from this review due to issues with their methodology.

7 For further detail, the reader is invited to view Figure 10 in Solms (2021). Please note that the specific entropy that Mark Solms cites in the quoted passage [22 – Gosseries et al. (2011)] is SpEn entropy of EEG.

8 This may be valid if each level of the scale operates in the same functional space (M. Levin, personal communications, 2022). For example, if level two is biochemical and level three is morphological then homeostats at these two levels would likely not from a nested hierarchy to collectively upper bound Shannon’s entropy.

9 Such as the Great Red Spot on Jupiter (Gleick, 2008).

10 As one of the reviewers of this paper pointed out, this work has already started in a paper that provides a free energy principle formulation of dynamical systems that include chaos (Friston et al., 2021).