Emergence of optimal decoding of population coding through STDP
The brain faces the problem to infer reliable hidden causes from large populations of noisy neurons, for example the direction of a moving object from spikes in area MT. It is known that a theoretically optimal likelihood decoding could be carried out by simple linear readout neurons if weights of synaptic connections would be set to certain values that depend on the tuning functions of sensory neurons. We show here that such theoretically optimal readout weights emerge autonomously through STDP in conjunction with lateral inhibition between readout neurons. In particular, we identify a class of optimal STDP learning rules with homeostatic plasticity, for which the autonomous emergence of optimal readouts can be explained on the basis of a rigorous learning theory. This theory shows that the considered network motif approximates Expectation Maximization for creating internal generative models for hidden causes of high-dimensional spike inputs. Notably, we find that this optimal functionality can be well approximated by a variety of STDP rules beyond those predicted by theory. Furthermore we show that this learning process is very stable, and automatically adjusts weights to changes in the number of readout neurons, in the tuning functions of sensory neurons, and in the statistics of external stimuli.