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

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 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.

Distinguishing Neural Adaptation and Predictive Coding Hypotheses in Auditory Change Detection

“…Our results are more consistent with previous studies suggesting that MMN is part of a system of predictive coding (Herholz et al 2009; Garrido et al 2009b; Mill et al 2011; Wacongne et al 2011; Todorovic and de Lange 2012) and not a simple reflection of a release from adaptation. The enhanced responses to both the early Dev and late Dev tones violated pattern expectations from the predictable standard XXXO pattern.…”

Rapid Sensory Adaptation Redux: A Circuit Perspective

“…As discussed previously in the context of synaptic plasticity, presynaptic neurons tuned to the standard stimulus (Figure 4, blue) will experience synaptic depression in response to the standard stimulus presentation and therefore will elicit reduced sensory drive in the postsynaptic neurons (Figure 4C, dashed lines). In a separate population of presynaptic neurons that are tuned to the deviant stimulus (Figure 4, orange), the standard stimulus will not evoke a response and therefore the synapses in this population will not be depressed (Mill et al, 2011; Taaseh et al, 2011) (Figure 4C, solid lines, warm colors). When these differentially tuned populations of neurons drive a common downstream brain region, the postsynaptic population integrating their inputs will demonstrate stimulus specific adaptation simply due to differential adaptation of the stimulus selective neurons (Figure 4B, dashed line).…”

Stimulus-specific adaptation, habituation and change detection in the gaze control system

“…It involves creating an expected template or model of the world based on the past, memorizing the template, comparing the incoming input with the expected template, detecting violations, and finally updating the expected template. Therefore, this task is of general interest not only to experimentalists but also to computational neuroscientists interested in modeling the brain (Mill et al 2011). In the following sections, I will address the question of where and how unexpected events are computed and represented in the brain, and discuss the relationships between temporal saliency mapping, habituation, and attention.…”