Differential effects of prediction error and adaptation along the auditory cortical hierarchy during deviance processing

Short-term neuronal and synaptic plasticity act in synergy for deviance detection in spiking networks

Sensory areas of cortex respond more strongly to infrequent stimuli when these violate previously established regularities, a phenomenon known as deviance detection (DD). Previous modeling work has mainly attempted to explain DD on the basis of synaptic plasticity. However, a large fraction of cortical neurons also exhibit firing rate adaptation, an underexplored potential mechanism. Here, we investigate DD in a spiking neuronal network model with two types of short-term plasticity, fast synaptic short-term depression (STD) and slower threshold adaptation (TA). We probe the model with an oddball stimulation paradigm and assess DD by evaluating the network responses. We find that TA is sufficient to elicit DD. It achieves this by habituating neurons near the stimulation site that respond earliest to the frequently presented standard stimulus (local fatigue), which diminishes the response and promotes the recovery (global fatigue) of the wider network. Further, we find a synergy effect between STD and TA, where they interact with each other to achieve greater DD than the sum of their individual effects. We show that this synergy is caused by the local fatigue added by STD, which inhibits the global response to the frequently presented stimulus, allowing greater recovery of TA-mediated global fatigue and making the network more responsive to the deviant stimulus. Finally, we show that the magnitude of DD strongly depends on the timescale of stimulation. We conclude that highly predictable information can be encoded in strong local fatigue, which allows greater global recovery and subsequent heightened sensitivity for DD.

Subanaesthetic doses of ketamine reduce but do not eliminate predictive coding responses: implications for mechanisms of sensory disconnection

BACKGROUND

Sensory disconnection is a key feature of sleep and anaesthesia. We have proposed that predictive coding offers a framework for understanding the mechanisms of disconnection. Low doses of ketamine that do not induce disconnection should thus diminish predictive coding, but not abolish it.

METHODS

Ketamine was administered to 14 participants up to a blood concentration of 0.3 μg ml-1 Participants were played a series of tones comprising a roving oddball sequence while electroencephalography evoked response potentials were recorded. We fit a Bayesian observer model to the tone sequence, correlating neural activity with the prediction errors generated by the model using linear mixed effects models and cluster-based statistics.

RESULTS

Ketamine modulated prediction errors associated with the transition of one tone to the next (transitional probability), but not how often tones changed (environmental volatility), of the system. Transitional probability was reduced when blood concentrations of ketamine were increased to 0.2-0.3 μg ml-1 (96-208 ms, P=0.003); however, correlates of prediction error were still evident in the electroencephalogram (124-168 ms, P=0.003). Prediction errors related to environmental volatility were associated with electroencephalographic activity before ketamine (224-284 ms, P=0.028) and during 0.2-0.3 μg ml-1 ketamine (108-248 ms, P=0.003). At this subanaesthetic dose, ketamine did not exert a dose-dependent modulation of prediction error.

CONCLUSIONS

Subanaesthetic dosing of ketamine reduced correlates of predictive coding but did not eliminate them. Future studies should evaluate whether states of sensory disconnection, including anaesthetic doses of ketamine, are associated with a complete absence of predictive coding responses.

CLINICAL TRIAL REGISTRATION

NCT03284307.

Intention-based predictive information modulates auditory deviance processing

The human brain is highly responsive to (deviant) sounds violating an auditory regularity. Respective brain responses are usually investigated in situations when the sounds were produced by the experimenter. Acknowledging that humans also actively produce sounds, the present event-related potential study tested for differences in the brain responses to deviants that were produced by the listeners by pressing one of two buttons. In one condition, deviants were unpredictable with respect to the button-sound association. In another condition, deviants were predictable with high validity yielding correctly predicted deviants and incorrectly predicted (mispredicted) deviants. Temporal principal component analysis revealed deviant-specific N1 enhancement, mismatch negativity (MMN) and P3a. N1 enhancements were highly similar for each deviant type, indicating that the underlying neural mechanism is not affected by intention-based expectation about the self-produced forthcoming sound. The MMN was abolished for predictable deviants, suggesting that the intention-based prediction for a deviant can overwrite the prediction derived from the auditory regularity (predicting a standard). The P3a was present for each deviant type but was largest for mispredicted deviants. It is argued that the processes underlying P3a not only evaluate the deviant with respect to the fact that it violates an auditory regularity but also with respect to the intended sensorial effect of an action. Overall, our results specify current theories of auditory predictive processing, as they reveal that intention-based predictions exert different effects on different deviance-specific brain responses.