A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences

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 probabilities and the rate of switching independently. The model predicts greater SSA for higher rates of switching. Finally, the issue of whether rarity or novelty elicits SSA is addressed by comparing the responses of the model to deviants in the context of a sequence of a single standard or many standards. The results support the view that synaptic adaptation alone can explain almost all aspects of SSA reported to date, including its purported novelty component, and that non-trivial networks of depressing synapses can intensify this novelty response. Author Summary: For processing real-life auditory scenes, it is not enough that auditory neurons code only for basic stimulus properties, such as frequency and intensity; at some point, these isolated properties must be woven into a pattern. Stimulus-specific adaptation (SSA), whereby neurons adapt to common stimuli but otherwise remain sensitive to other, rare stimuli, has been proposed as a low-level substrate for such abstract pattern processing. SSA has been previously investigated using ‘oddball sequences’ of tones, in which one frequency is common, the other rare. In this article, we present the first neurocomputational model of SSA and show that it can reproduce a wide range of published data. We also propose a natural generalisation of the oddball paradigm, based on Markov chains, which allows the experimenter to manipulate other characteristics of the sequence such the rate of switching. Finally, we show that a small network of neurons can distinguish novelty from mere rarity; e.g., a B stands out in the sequence ABAAA in a way that it does not in CBADE, even though it is equally probable in both. We demonstrate that cascades of depressing synapses can adequately encode this difference, whereas the simple adaptation-based models proposed to date cannot.