A theoretical basis for emergent pattern discrimination in neural
systems through slow feature extraction
Neurons in the brain are able to detect and discriminate salient
spatio-temporal patterns in the firing activity of presynaptic neurons. It is
open how they can learn to achieve this, especially without the help of a
supervisor. We show that a well-known unsupervised learning algorithm for
linear neurons, Slow Feature Analysis (SFA), is able to acquire the
discrimination capability of one of the best algorithms for supervised linear
discrimination learning, the Fisher Linear Discriminant (FLD), given suitable
input statistics. We demonstrate the power of this principle by showing that
it enables readout neurons from simulated cortical microcircuits to learn
without any supervision to discriminate between spoken digits, and to detect
repeated firing patterns that are embedded into a stream of noise spike
trains with the same firing statistics. Both these computer simulations and
our theoretical analysis show that slow feature extraction enables neurons to
extract and collect information that is spread out over a trajectory of
firing states that lasts several hundred ms. In addition, it enables neurons
to learn without supervision to keep track of time (relative to a stimulus
onset, or the initiation of a motor response). Hence these results elucidate
how the brain could compute with trajectories of firing states, rather than
only with fixed point attractors. It also provides a theoretical basis for
understanding recent experimental results on the emergence of view- and
position-invariant classification of visual objects in inferior temporal
Reference: S. Klampfl and W. Maass.
A theoretical basis for emergent pattern discrimination in neural systems
through slow feature extraction.
Neural Computation, 22(12):2979-3035, 2010.
Epub 2010 Sep 21.