History’s Top Insights Into Brain Computation

This post is the culmination of a month-long chronicling of the major brain computation insights of all time.

Some important insights were certainly left out, so feel free to add comments with your favorites.

Below you will find all 26 insights listed with links to their entries. At the end is the summary of the insights in two (lengthy) sentences.

1) The brain computes the mind (Hippocrates- 460-379 B.C.)

2)  Brain signals are electrical (Galvani – 1791, Rolando – 1809)

3)  Functions are distributed in the brain (Flourens – 1824, Lashley – 1929)

4) Functions can be localized in the brain (Bouillaud – 1825, Broca – 1861, Fritsch & Hitzig – 1870)

5) Neurons are fundamental units of brain computation (Ramon y Cajal – 1889)

6) Neural networks consist of excitatory and inhibitory neurons connected by synapses (Sherrington – 1906)

7) Brain signals are chemical (Dale – 1914, Loewi – 1921)

8) Reward-based reinforcement learning can explain much of behavior (Skinner – 1938, Thorndike – 1911, Pavlov – 1905)

9) Convergence and divergence between layers of neural units can perform abstract computations (Pitts & McCulloch – 1947)

10) The Hebbian learning rule: 'Neurons that fire together wire together' [plus corollaries] (Hebb, 1949)

11) Action potentials, the electrical events underlying brain communication, are governed by ion concentrations and voltage differences mediated by ion channels (Hodgkin & Huxley – 1952)

12) Hippocampus is necessary for episodic memory formation (Milner – 1953)

13) Larger cortical space is correlated with greater representational resolution; memories are stored in cortex (Penfield – 1957)

14) Neocortex is composed of columnar functional units (Mountcastle – 1957, Hubel & Wiesel – 1962)

15) Consciousness depends on cortical communication; the cortical hemispheres are functionally specialized (Sperry & Gazzaniga – 1969)

16) Critical periods of cortical development via competition (Hubel & Wiesel – 1970)

17) Reverbatory activity in lateral prefrontal cortex maintains memories and attention over short periods (Fuster – 1971, Jacobsen – 1936, Goldman-Rakic – 2000)

18) Behavior exists on a continuum between controlled and automatic processing (Schneider & Shiffrin – 1977)

19) Neural networks can self-organize via competition (Grossberg – 1978, Kohonen – 1981)

20) Spike-timing dependent plasticity: Getting the brain from correlation to causation (Levy – 1983, Sakmann – 1994, Bi & Poo – 1998, Dan – 2002)

21) Parallel and distributed processing across many neuron-like units can lead to complex behaviors (Rumelhart & McClelland – 1986, O'Reilly – 1996)

22) Recurrent connectivity in neural networks can elicit learning and reproduction of temporal sequences (Jordan – 1986, Elman – 1990, Schneider – 1991)

23) Motor cortex is organized by movement direction (Schwartz  & Georgopoulos – 1986, Schwartz – 2001)

24) Cognitive control processes are distributed within a network of distinct regions (Goldman-Rakic – 1988, Posner – 1990, Wager & Smith – 2004, Dosenbach et al. – 2006, Cole & Schneider – 2007)

25) The dopamine system implements a reward prediction error algorithm (Schultz – 1996, Sutton – 1988)

26) Some complex object categories, such as faces, have dedicated areas of cortex for processing them, but are also represented in a distributed fashion (Kanwisher – 1997, Haxby – 2001)

Implication: The mind, largely governed by reward-seeking behavior on a continuum between controlled and automatic processing, is implemented in an electro-chemical organ with distributed and modular function consisting of excitatory and inhibitory neurons communicating via ion-induced action potentials over convergent and divergent synaptic connections altered by timing-dependent correlated activity often driven by expectation errors. The cortex, a part of that organ organized via local competition and composed of functional column units whose spatial dedication determines representational resolution, is composed of many specialized regions forming specialized and/or overlapping distributed networks involved in perception (e.g., touch: parietal, vision: occipital), action (e.g., frontal), and memory (e.g., short-term: prefrontal, long-term: temporal), which depend on inter-regional connectivity for functional integration, population vector summation for representational specificity, dopamine signals for reinforcement learning, and recurrent connectivity for sequential learning.

-MC 

5 Responses to “History’s Top Insights Into Brain Computation”

  1. Anthony Rasmussen Says:

    Absolutely amazing! Great work!

  2. Rhys Evans Says:

    Fascinating read

  3. Mike Thorne Says:

    In #4, it should be Fritsch & Hitzig, not Fitsch & Hitzig.

  4. M.C. Says:

    Thanks Mike. The typo has been corrected.

  5. PL2 Says:

    According to the SMOG readability calculator, your “Implication” is written on the 30.93th grade level.

    17 – 18 post-graduate studies Harvard Business Review
    19+ post-graduate degree IRS Code

    Basic Data
    Sentences 2
    Total Words 142
    Letters 911
    Digits 0
    Characters 1082

    Derived Data
    Words/Sentence 71.0
    Syllables/Word 2.27
    Syllables/Sentence 161.5
    Letters/Syllable 2.82
    Letters/Word 6.42
    Letters/Sentence 455.5

    I guess there is no easy way to explain away 100 billion neurons and a quadrillion synapses. Still, it is nice to see it all in one place as you’ve done. Bravissimo.

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