57-hopfield-1982

Foundational Papers in Complexity Science pp. 1663–1684
DOI: 10.37911/9781947864542.57

Attractor Neural Networks

Author: David Sherrington, University of Oxford

 

Excerpt

The brain is now well established as the control center for human life, but our understanding of its microscopic structure emerged only around 1900 due to the brilliant neuroanatomical work of Santiago Ramón y Cajal and Camillo Golgi identifying nerve cells (neurons) as key building blocks. The nature of interactions between neurons was unknown until the seminal work of Charles Sherrington, who proposed that synapses provided the connections and were of both excitatory and inhibitory character. In his own words (Sherrington 1934):

The nerve-nets are patterned networks of threads. The human brain is a vast example, offering immense numbers of determinate paths and immense numbers of junctional points. At these latter the travelling signal so to say hesitates and sets up a local gradable state which may have to accumulate before transmission further, or indeed may there subside and fail. These junctional points are often convergence points for lines from several directions. Arrived there, signals convergent from several lines may coalesce and may reinforce each other’s excitatory power.

At such points too appears a process which, instead of exciting, quells and precludes excitation . . . It is evoked by travelling signals not distinguishable from those which call forth excitement . . . These two opposed processes, excitation and inhibition, co-operate at nodal point after nodal point in the nerve-circuits. Their joint operation at any moment settles into what will be the conduction pattern.

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