Neural computation in excitable media

Theoretical Neuroscience seeks to delineate conceptualizations which would generate explanatory and predictive accounts of empirical observations in the Neurosciences. At its inception in the late part of the 19th Century, it dealt creatively with what one could call 'virtual objects'. For instance Sherrington's Synapse and 'central excitatory/inhibitory states' were ideas whose actualization in real mechanisms required many years of experimental work. Similarly, ideas of 'wiring diagrams' of neural connectivity (24,11) and the role of neurons acting in assemblies rather than in isolation (30) were formulated as directives for empirical investigation. In the late 1940s, Theoretical Neuroscience took a decisive turn to become, essentially, the current Computational Neuroscience. Several signal events occurred at that time, in large measure associated with or triggered by the "Cybernetic Revolution'. For a short list of these influences I single out Turing machine computation and Shannon's information theory, merging to the idea of the brain as an information processing machine in which binary neural impulses would function as a code for external physical events : both ideas being sustained by the fertile ground of the then prevailing logical atomism in Epistemology, and of Cartesian representationalism. These ideas are epitomized in the influential work of McCulloch and Pitts (42) on 'the logical ideas immanent in nervous activity ' that established that computation on these principles can prove all theorems of the Principia Mathematica. The single neuron methodology of recording neural activity enabled experimental neurophysiologists to supply the data that the theory required, thus sustaining a circularity of seemingly mutual validation. Concurrently, computational models have sought to determine principles by which networks of neurons can process and represent information in digital form. Thus consolidated the ideology of the 'Digital Brain'.