In my talk I address the question: how might concepts from complexity theory be used to simplify the modeling of brain structure and dynamics for knowledge-based engineering? Engineers have often searched for novel approaches to machine intelligence by asking how brains work. This search has become surreal, because most engineers get their understanding of brain properties secondarily from reports by neuroscientists, while those neuroscientists whose work they can profitably read have adopted their hypotheses and experimental tools from engineers. This circularity has trapped both engineers and neuroscientists in a hall of mirrors. While it is well established that brains are not computers, it is equally clear that brains are dynamical systems of a different kind, but in order to construct really new kinds of devices, we must escape the trap. Yet we face continuing sources of confusion, because we have to use computers to solve our equations and build new devices. The problem is not merely that engineers mistakenly categorize a neuron as a transistor and an action potential as a binary digit; it is that the computational metaphor is so pervasive that to reject it may seem perverse and obfuscate.
My way out of this trap is to review the history and philosophy of how and why the computational model for brains has become so entrenched. I use that background to return to fundamentals and derive a biological model of brain functions in terms of nonequilibrium thermodynamics. I rely on three organizing principles:
1) Brains, like computers, are open systems, but brains use bodies to exchange energy and information by engaging their environments. Moreover, they are closed systems with respect to knowledge and meaning in cognition and experiential learning. An appropriate model is the intentional robot. www.scholarpedia.org/article/Intentionality.
2) The underlying topology of vertebrate brains is random and scale-free; structured connections such as those in local networks, topographic maps, hubs, etc. evolve as departures from randomness by genetic evolution and experiential learning through the action-perception cycle. A new starting point is random graph theory and neuropercolation. www.scholarpedia.org/article/Scale-Free_Neocortical_Dynamics.
3) State variables in brain models are determined by observers’ methods of measurement, not by the brains in their continuum of physical and chemical aspects. The multiple space-time scales that we require for measurement with chemical assays, microelectrodes, imaging devices, etc., give networks of patches that have to be stitched together using the concept from synergetics of circular causality: entities at each level order the lower-level elements that create them. www.scholarpedia.org/article/Hilbert_transform_for_brain_waves. A well-known acronym ‘GOFAI’ means ‘good old-fashioned artificial intelligence’.
I propose an alternate acronym ‘GOFISH’, which means ‘good old-fashioned innovative science and history’.