Where AI is good we are not so good and vice versa, which might seem to indicate current AI methods are not the most plausible mechanism for Brain operation (or is it Mind operation ?)

Von neumann architecture reaching a limit ? New types of algorithms are required that allow distribution of tasks over a great number of processors.

They should be error tolerant and capable of learning. This could be like the Brain with slow computational elements of limited reliability but which are present in huge numbers and process sensory, motor and perhaps other tasks concurrently.

1943 McCulloch and Pitts said nerve cells play the role of logical elements and circuits thereof could evaluate Boolean logical functions.

1958 RosenBlatts Perceptron

However advent of computers led to research direction which saw theorientation towards structure of biological nerve systems as little use in the investigation and simulation of intelligence.

Used elaborate programming based on "problem solving heuristics" it was hoped this way the goal of intelligent machines would be reached. Due to initial successes ANNs pushed to side.

Fast forward to Hopfield (1982) analogy between neural units with boolean properties and physical systems with consisting of elementary magnets or spins. Dynamic of such networks can be described by an Energy Function E and patterns in these networks can be regarded as attractors in a high dimensional phase space.

So what ?

Well if we are prepared to accept the analogy we can throw the mathematical methods of statistical physics at such networks (for analysis more so than operation of course).