I was also working in AI in the late 80s and 90s, but in a lab that loved the neural network side of things. I decided pretty quickly that the biological inspiration for NNs was more marketing than useful. The more interesting developments were being done with Bayesian stuff (at least there was a mathematical theory behind the performance).

FWIW, I ended up doing my PhD on Genetic Algorithms / Genetic Programming : where the biological inspiration (and understanding) works both ways (IMHO).