Abstract
We show how hierarchical networks may be constructed of interconnected oscillatory network modules developed previously as models of olfactory cortex, or caricatures of “patches”of neocortex. The architecture is such that the larger system is itself a special case of the type of network of the submodules, and can be analysed with the same tools used to design the subnetwork modules. A particular subnetwork is formed by a set of neural populations whose interconnections also contain higher order synapses. These synapses determine attractors for that subnetwork independent of other subnetworks. Each subnetwork module assumes only minimal coupling justified by known anatomy. An N node network can be shown to function as an associative memory for up to N/2 oscillatory and N/3 chaotic memory attractors.
The modules can learn connection weights between themselves which will cause the system to evolve under a clocked “machine cycle” by a sequence of transitions of attractors within the modules, much as a digital computer evolves by transitions of its binary flip-flop states. Thus the architecture employs the principle of “computing with attractors” used by macroscopic systems for reliable computation in the presence of noise. Clocking is done by rhythmic variation of certain bifurcation parameters which hold sensory modules clamped at their attractors while motor states change, and then clamp motor states while sensory states are released to take new states based on input from external motor output and internal feedback.
Simulations show robust storage of oscillatory attractor transition sequences in a system with a sinusoidal clock and continuous oscillatory intermodule driving. The phase-locking or “binding” which occurs rapidly between coupled attractors of similar resonant frequency in different modules is important for reliable transitions. We show analytically how modular networks with more fault tolerant and biologically plausible distributed patterns can be built from “spreading activation” style networks which use single node representations.
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Baird, B. (1992). Information Processing by Dynamical Interaction of Oscillatory Modes in Coupled Cortical Networks. In: Taylor, J.G., Caianiello, E.R., Cotterill, R.M.J., Clark, J.W. (eds) Neural Network Dynamics. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-2001-8_14
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DOI: https://doi.org/10.1007/978-1-4471-2001-8_14
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