STDP enables spiking neurons to detect hidden causes of their inputs
B. Nessler, M. Pfeiffer, and W. Maass
The principles by which spiking neurons contribute to the astounding
computational power of generic cortical microcircuits, and how
spike-timing-dependent plasticity (STDP) of synaptic weights could generate
and maintain this computational function, are unknown. We show here that
STDP, in conjunction with a stochastic soft winner-take-all (WTA) circuit,
induces spiking neurons to generate through their synaptic weights implicit
internal models for subclasses (or “causes”) of the high-dimensional spike
patterns of hundreds of pre-synaptic neurons. Hence these neurons will fire
after learning whenever the current input best matches their internal model.
The resulting computational function of soft WTA circuits, a common network
motif of cortical microcircuits, could therefore be a drastic dimensionality
reduction of information streams, together with the autonomous creation of
internal models for the probability distributions of their input patterns. We
show that the autonomous generation and maintenance of this computational
function can be explained on the basis of rigorous mathematical principles.
In particular, we show that STDP is able to approximate a stochastic online
Expectation-Maximization (EM) algorithm for modeling the input data. A
corresponding result is shown for Hebbian learning in artificial neural
Reference: B. Nessler, M. Pfeiffer, and W. Maass.
STDP enables spiking neurons to detect hidden causes of their inputs.
In Proc. of NIPS 2009: Advances in Neural Information Processing
Systems, volume 22, pages 1357-1365. MIT Press, 2010.