Title
Optimal Decisions: From Neural Spikes, Through Stochastic Differential Equations, To Behavior
Abstract
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 neurons in oculo-motor areas 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. We review 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. We then briefly describe a biophysically-based model of a pool of neurons in locus coeruleus, a brainstem nucleus implicated in widespread norepinephrine release. This neurotransmitter can effect transient gain changes in cortical circuits of the type that the abstract drift-diffusion analysis requires. We also describe how optimal gain schedules can be computed in the presence of time-varying noisy signals. We argue that a rational account of how neural spikes give rise to simple behaviors is beginning to emerge. key words: stochastic differential equations, drift-diffusion process, dynamical systems, phase oscillators, decision-making models.
Year
DOI
Venue
2005
10.1093/ietfec/e88-a.10.2496
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES
Keywords
Field
DocType
stochastic differential equations, drift-diffusion process, dynamical systems, phase oscillators, decision-making models
Information theory,Ordinary differential equation,Random walk,Computer science,Algorithm,Stochastic differential equation,Dynamical systems theory,Eye movement,Decision theory,Artificial intelligence,Stimulus (physiology)
Journal
Volume
Issue
ISSN
E88A
10
0916-8508
Citations 
PageRank 
References 
5
0.85
6
Authors
9
Name
Order
Citations
PageRank
Philip Holmes121526.66
Eric Shea-Brown232337.92
Jeff Moehlis327634.17
Rafal Bogacz416528.70
juan gao550.85
Gary Aston-Jones6337.04
Ed Clayton7193.80
Janusz Rajkowski8256.30
Jonathan D Cohen929265.10