Feedforward Chemical Neural Network: An In Silico Chemical System That Learns xor.

Inspired by natural biochemicals that perform complex information processing within living cells, we design and simulate a chemically implemented feedforward neural network, which learns by a novel chemical-reaction-based analogue of backpropagation. Our network is implemented in a simulated chemical system, where individual neurons are separated from each other by semipermeable cell-like membranes. Our compartmentalized, modular design allows a variety of network topologies to be constructed from the same building blocks.

A General Statistical Method for Identifying Adaptations by Parameterizing Trait Space.

It is obviously useful to think of evolved individuals in terms of their adaptations, yet the task of empirically classifying traits as adaptations has been claimed by some to be impossible in principle. I reject that claim by construction, introducing a formal method to empirically test whether a trait is an adaptation. The method presented is general, intuitive, and effective at identifying adaptations while remaining agnostic about their adaptive function.