There is increasing evidence from in vivo recordings in monkeys
trained to respond to stimuli by making left- or rightward eye
movements, that firing rates in certain groups of `visual' neurons
mimic drift-diffusion processes, rising to a (fixed) threshold prior
to movement initiation. This supplements earlier observations of
psychologists, that human reaction time and error rate data can be
fitted by random walk and diffusion models, and has renewed interest
in optimal decision-making ideas from information theory and
statistical decision theory as a clue to neural mechanisms.
I will review some results from decision theory and stochastic
ordinary differential equations, and show how they may be extended and
applied to derive explicit parameter dependencies in optimal
performance that may be tested on human and animal subjects. I will
then describe a biophysically-based model of a pool of neurons in a
brainstem organ - locus coeruleus - that is implicated in widespread
norepinephrine release. This neurotransmitter can effect transient
gain and response threshold changes in cortical circuits of the type that
the abstract drift-diffusion analysis requires. I will argue that, in
spite of many gaps and leaps of faith, a rational account of how neural
spikes give rise to simple behaviors is beginning to emerge.
This work is in collaboration with Eric Brown, Rafal Bogacz, Jeff
Moehlis and Jonathan Cohen (Princeton University), and Ed Clayton,
Janusz Rajkowski and Gary Aston-Jones (University of Pennsylvania).
It is supported by the National Institutes of Mental Health.