"A Learning Analog Neural Network Chip..."

[Gert Cauwenberghs]

FTP-host: archive.cis.ohio-state.edu
FTP-filename: /pub/neuroprose/cauwenberghs.nips93.ps.Z

A preprint of the paper:

A Learning Analog Neural Network Chip with Continuous-Time Recurrent Dynamics,
  by Gert Cauwenberghs,  8 pages including figures,
  to appear in Advances in Neural Information Processing Systems, vol. 6, 1994,

is available on the neuroprose repository, in compressed PostScript format:

anonymous binary ftp to archive.cis.ohio-state.edu
cd pub/neuroprose
get cauwenberghs.nips93.ps.Z
uncompress and print.

The abstract follows below.
--- Gert Cauwenberghs
    (gert at jhunix.hcf.jhu.edu)

We present experimental results on supervised learning of dynamical features in
an analog VLSI neural network chip.  The recurrent network, containing six
continuous-time analog neurons and 42 free parameters (connection strengths and
thresholds), is trained to generate time-varying outputs approximating given
periodic signals presented to the network.  The chip implements a stochastic
perturbative algorithm, which observes the error gradient along random
directions in the parameter space for error-descent learning.  In addition to
the integrated learning functions and the generation of pseudo-random
perturbations, the  chip provides for teacher forcing and long-term storage of
the volatile  parameters.  The network learns a 1 kHz circular trajectory
in 100 sec.  The chip occupies 2 X 2 mm in a 2 um CMOS process, and dissipates
1.2 mW.