  How intelligence arises from the brain is a central problem in science. A
crucial aspect of intelligence is dealing with uncertainty -- developing good
predictions about one's environment, and converting these predictions into
decisions. The brain itself seems to be noisy at many levels, from chemical
processes which drive development and neuronal activity to trial variability of
responses to stimuli. One hypothesis is that the noise inherent to the brain's
mechanisms is used to sample from a model of the world and generate
predictions. To test this hypothesis, we study the emergence of statistical
learning in NEMO, a biologically plausible computational model of the brain
based on stylized neurons and synapses, plasticity, and inhibition, and giving
rise to assemblies -- a group of neurons whose coordinated firing is tantamount
to recalling a location, concept, memory, or other primitive item of cognition.
We show in theory and simulation that connections between assemblies record
statistics, and ambient noise can be harnessed to make probabilistic choices
between assemblies. This allows NEMO to create internal models such as Markov
chains entirely from the presentation of sequences of stimuli. Our results
provide a foundation for biologically plausible probabilistic computation, and
add theoretical support to the hypothesis that noise is a useful component of
the brain's mechanism for cognition.
