Chapter 13 endnote 6, from Lisa Feldman Barrett.
Some context is:
You can find complex systems in neuroscience, physics, mathematics, economics, and other scholarly disciplines.
A complex system is made up of a large number of elements that interact with one another. In a system with N elements, there are N! (N factorial) possible patterns of those elements — each additional element multiplies the number of possible patterns. This mathematical observation is the scientific origin of the phrase, "the whole is more than the sum of the parts."
The brain is a large, complex system, and its elements are neurons. One neuron synapses on many other neurons (one-to many connectivity). A back of the envelope estimate is that each neuron has about 15,000 synapses, though neurons in primary sensory regions have many fewer synapses than neurons in the core networks. Consider also that a single neuron, in most cases, fires in different patterns, releases different neurotransmitters, and the function of the neurotransmitter release can change, depending on the other neurons that are firing. And many different neurons synapse onto one neuron, the consequence being that the information conveyed by the neuron changes depending on the other neurons firing in the moment (i.e., depending on its neural context). The upshot is that a single neuron (or group of neurons) represents different features from moment to moment: neurons are multipurpose.
Complexity means that variation is the norm in the brain, which should not be too surprising because variation is the norm in nature. Single-celled organisms that are genetically identical respond differently under exactly the same environmental conditions, depending on what occurred before, and because of the randomness built in to every biological process at the molecular level. Variability is also the norm in natural selection, so it should not be surprising to see variability in the kinds of minds that a human brain can create.
Notes on the Notes
- Simon, Herbert A. 1962. “The architecture of complexity.” Proceedings of the American Philosophical Society 106 (6): 467–482, p. 468.
- Barb Finlay, personal communication, November 26, 2014.
- Lennie, Peter. 2003. "The cost of cortical computation." Current Biology 13 (6): 493-497.
- McIntosh, Anthony Randal. 2004. Contexts and catalysts: a resolution of the localization and integration of function in the brain. Neuroinformatics, 2 (2): 175-82.
- Gjorgjieva, Juliajana, G. Drion and Eve Marder. 2016. "Computational implications of biophysical diversity and multiple timescales in neurons and synapses for circuit performance." Current Opinion in Neurobiology, 37: 44-52.
- Sillito, A. M. 1975. "The contribution of inhibitory mechanisms to the receptive field properties of neurones in the striate cortex of the cat." Journal of Neurophysiology 250 (2): 305-329.
- Edelman, Gerald M., and Joseph A. Gally. 2001. “Degeneracy and Complexity in Biological Systems.” Proceedings of the National Academy of Sciences 98 (24): 13763–13768.
- Demarque, Michaël, and Nicholas C. Spitzer. 2010. "Activity-dependent expression of Lmx1b regulates specification of serotonergic neurons modulating swimming behavior." Neuron 67 (2): 321-334.
- Korobkova, Ekaterina, Thierry Emonet, Jose MG Vilar, Thomas S. Shimizu, and Philippe Cluzel. 2004. "From molecular noise to behavioural variability in a single bacterium." Nature 428 (6982): 574-578.