Connectionist symbol processing

[John Lazzaro]

>  I would suggest that most recurrent neural net architectures
>  are not fundamentally more 'neural' than hidden Markov models -
>  think of an HMM as a neural net with second-order weights
>  and linear activation functions.

We presented a continuous-time analog-circuit implementation of
a HMM state decoder a few years ago at NIPS -- I've always felt
that if you can make a clock-free analog system compute an 
algorithm well in silicon, its reasonable to expect it can be
implemented usefully in biological neurons as well ...

 Lazzaro, J. P., Wawrzynek J., and Lippmann, R. (1996).
A micropower analog VLSI HMM state decoder for wordspotting. In
Jordan, M., Mozer, M., and Petsche, T. (eds), {\it Advances in Neural
Information Processing Systems 9}.  Cambridge, MA: MIT Press.

 Lazzaro, J., Wawrzynek, J., Lippmann, R. P. (1997).
A micropower analog circuit implementation of hidden markov model
state decoding. {\it IEEE Journal Solid State Circuits} {\bf 32}:8,
1200--1209.

http://www.cs.berkeley.edu/~lazzaro/biblio/decoder.ps.gz

							--john lazzaro