Cortical Computation via Iterative Constructions
This work addresses neurally feasible computation for cognitive modeling, but it is incremental as it builds on Valiant's prior construction.
The paper tackles the problem of realizing Boolean functions via neurally feasible iterative constructions using constant-size primitives, showing that linear convergence is achievable for any uniform threshold function, but quadratic convergence requires larger primitives near extreme thresholds.
We study Boolean functions of an arbitrary number of input variables that can be realized by simple iterative constructions based on constant-size primitives. This restricted type of construction needs little global coordination or control and thus is a candidate for neurally feasible computation. Valiant's construction of a majority function can be realized in this manner and, as we show, can be generalized to any uniform threshold function. We study the rate of convergence, finding that while linear convergence to the correct function can be achieved for any threshold using a fixed set of primitives, for quadratic convergence, the size of the primitives must grow as the threshold approaches 0 or 1. We also study finite realizations of this process and the learnability of the functions realized. We show that the constructions realized are accurate outside a small interval near the target threshold, where the size of the construction grows as the inverse square of the interval width. This phenomenon, that errors are higher closer to thresholds (and thresholds closer to the boundary are harder to represent), is a well-known cognitive finding.