Chaos in the mind
From CasGroup
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at the interface between neuroscience and psychology. | at the interface between neuroscience and psychology. | ||
One major role may be to guarantee creativity and | One major role may be to guarantee creativity and | ||
| - | diversity of behavior. | + | diversity of behavior. The unconscious mind takes the flood |
| + | of information and simplifies, canalizes and categorizes it into ever-changing | ||
| + | streams of patterns and packages. Chaos arising from combinatorial | ||
| + | search maybe important as a mechanism to control the steam of consciousness | ||
| + | (at least the unconscious part), to arrive at novel | ||
| + | solutions for cognitive problems, to find a way or trajectory | ||
| + | to the best idea which matches the current situation, | ||
| + | and to find new analogies, metaphors and insights. | ||
| + | A number of scientists have coined names for this process, | ||
| + | Peter Ashwin's has proposed "Cycling Chaos", | ||
| + | Mikhail Rabinovich a "Winnerless Competition" (WLC), | ||
| + | and Ichiro Tsuda "Chaotic Itinerancy". They are all similar, | ||
| + | the general idea is to connect different saddle points with | ||
| + | "cycling" trajectories. | ||
== Chaos and the point of view == | == Chaos and the point of view == | ||
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assemblies, show inherently complex and chaotic behavior. | assemblies, show inherently complex and chaotic behavior. | ||
The more detailed you model them, the more | The more detailed you model them, the more | ||
| - | + | complex the situation becomes: if you simulate a single | |
neuron exactly with differential equations, | neuron exactly with differential equations, | ||
| - | even a single neuron | + | even a single neuron can show complex (for |
| + | instance spiking and bursting) or even chaotic behavior. | ||
| + | The differential equations for single neurons can show | ||
| + | chaotic behavior (Hodgkin-Huxley model, | ||
| + | Fitz-Hugh-Nagumo model,..). Bursting of neurons | ||
| + | can be described by bifurcation theory. | ||
Neural networks are inherently complex because | Neural networks are inherently complex because | ||
every connection involves a merging and splitting | every connection involves a merging and splitting | ||
operation. When the brain processes information, | operation. When the brain processes information, | ||
in every step the previous state is merged and splitted | in every step the previous state is merged and splitted | ||
| - | in complex ways. | + | in complex ways. It we consider the overall network, |
| + | for example with an EEG, then we see that EEG data | ||
| + | apparently is chaotic. | ||
Yet on the psychological level, chaos seems to | Yet on the psychological level, chaos seems to | ||
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If you go from the bottom (neuroscience) to the | If you go from the bottom (neuroscience) to the | ||
top, everything seems to be complex, chaotic and involved | top, everything seems to be complex, chaotic and involved | ||
| - | in chaos. If you look from top (psychology) to | + | in chaos, from the attempt to describe even a single neuron |
| + | to a typical EEG. If you look from top (psychology) to | ||
the bottom, there seems to be little chaos. | the bottom, there seems to be little chaos. | ||
Somewhere in between chaos seems to vanish, | Somewhere in between chaos seems to vanish, | ||
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"The Neurosciences Institute" in La Jolla, California. | "The Neurosciences Institute" in La Jolla, California. | ||
He is probably not completely on the wrong track. | He is probably not completely on the wrong track. | ||
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== Role and Function of Chaos == | == Role and Function of Chaos == | ||
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| + | : "Chaos underlies the ability of the brain to respond flexibly to the outside world and to generate novel activity patterns, including those that are experienced as fresh ideas." ''(Walter J. Freeman, [http://sulcus.berkeley.edu/FLM/MS/Physio.Percept.html The Physiology of Perception])'' | ||
The mechanism described above uses chaotic search | The mechanism described above uses chaotic search | ||
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irregular and unpredictable like random search. | irregular and unpredictable like random search. | ||
When a system is chaotic, you can only predict | When a system is chaotic, you can only predict | ||
| - | it's future in a | + | it's future in a probabilistic manner. A system |
undergoing chaotic interactions samples all possible | undergoing chaotic interactions samples all possible | ||
states available to it, it is running through all | states available to it, it is running through all | ||
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because it seems to be involved in exploration, | because it seems to be involved in exploration, | ||
creation of new ideas and insights. | creation of new ideas and insights. | ||
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| + | == Links == | ||
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| + | * Lewis Dartnell, [http://plus.maths.org/issue35/features/dartnell/index.html Chaos in the Brain] | ||
