Up
Previous
Next
Sparse codes and spikes
Bruno A. Olshausen
Dept. of Psychology and
Center for Neuroscience
UC Davis
It has been shown that the spatial receptive field properties of
cortical simple cells can be accounted for in terms of an efficient
coding strategy that has been adapted to the statistics of natural
images (Olshausen & Field, 1996; Bell & Sejnowski 1997; van Hateren &
van der Schaaf, 1998). Here, I extend this work to the time domain to
show how the spatiotemporal receptive field properties of these
neurons may also be explained in terms of efficient coding principles.
Time-varying natural images are modeled as a superposition of sparse,
independent events in both space and time. The events are
characterized using an overcomplete set of spatiotemporal basis
functions which are assumed to be translation-invariant in the time
domain. When adapted to natural movies, the basis functions of the
model converge to a set of spatially localized, oriented, bandpass
functions that translate over time, similar to the space-time
receptive fields of V1 neurons. These results are similar to those
obtained previously with ICA (van Hateren & Ruderman, 1998), but there
is an important difference in how the output activities of the model
are computed due to the use of an overcomplete basis set. Rather than
computing the output activities by simply convolving the basis
functions with the movie, the outputs are sparsified across both space
and time, producing a non-linear code having a spike-like character in
time. Thus, continuous, time-varying images are represented as a
series of sharp, punctate events in time, similar to neural spike
trains (Rieke et al., 1997). These results suggest that both the
receptive field structure and the spiking nature of V1 neurons may be
accounted for in terms of a single principle of efficient coding.
Possibilities for testing this hypothesis shall be discussed.