a neurocomputational model of stimulus-specific adaptation to oddball and markov sequencesneurocomputational stimulus-specific适应古怪的模型和马尔可夫序列.pdfVIP

A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences 1 2 2 1 Robert Mill *, Martin Coath , Thomas Wennekers , Susan L. Denham 1 School of Psychology/Centre for Robotics and Neural Systems, University of Plymouth, Plymouth, United Kingdom, 2 School of Computing and Mathematics/Centre for Robotics and Neural Systems, University of Plymouth, Plymouth, United Kingdom Abstract Stimulus-specific adaptation (SSA) occurs when the spike rate of a neuron decreases with repetitions of the same stimulus, but recovers when a different stimulus is presented. It has been suggested that SSA in single auditory neurons may provide information to change detection mechanisms evident at other scales (e.g., mismatch negativity in the event related potential), and participate in the control of attention and the formation of auditory streams. This article presents a spiking- neuron model that accounts for SSA in terms of the convergence of depressing synapses that convey feature-specific inputs. The model is anatomically plausible, comprising just a few homogeneously connected populations, and does not require organised feature maps. The model is calibrated to match the SSA measured in the cortex of the awake rat, as reported in one study. The effect of frequency separation, deviant probability, repetition rate and duration upon SSA are investigated. With the same parameter set, the model generates responses consistent with a wide range of published data obtained in other auditory regions using other stimulus configurations, such as block, sequential and random stimuli. A new stimulus paradigm is introduced, which generalises the oddball concept to Markov chains, allowing the experimenter to vary the tone probabil