Predictive feedback, early sensory representations and fast responses to predicted stimuli depend on NMDA receptors (original) (raw)
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Receptors, circuits and neural dynamics for prediction
Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior; e.g., hearing a siren, we expect to see an ambulance and quickly make way. While theoretical and computational frameworks for prediction exist, circuit and receptor-level mechanisms are unclear. Using high-density EEG and Bayesian modeling, we show that trial history and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audio-visual prediction task. Low-dose ketamine, a NMDA receptor blocker – but not the control drug dexmedetomidine – perturbed predictions, their representation in frontal cortex, and feedback to posterior cortex. This study suggests predictions depend on frontal alpha activity and NMDA receptors, and ketamine blocks access to learned predictive information.One Sentence SummaryPredictions depend on NMDA receptors, representation in frontal cortex, and feedback to sensory cortex for comparison...
Title: Receptors, circuits and neural dynamics for prediction
2020
Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior; e.g., hearing a siren, we expect to see an ambulance and quickly make way. While theoretical and computational frameworks for prediction exist, circuit and receptor-level mechanisms are unclear. Using high-density 15 EEG and Bayesian modeling, we show that trial history and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audio-visual prediction task. Low-dose ketamine, a NMDA receptor blocker – but not the control drug dexmedetomidine – perturbed predictions, their representation in frontal cortex, and feedback to posterior cortex. This study suggests predictions depend on frontal alpha 20 activity and NMDA receptors, and ketamine blocks access to learned predictive information. One Sentence Summary: Predictions depend on NMDA receptors, representation in frontal cortex, and feedback to sensory cortex for c...
The Journal of Neuroscience, 2018
Using predictions based on environmental regularities is fundamental for adaptive behavior. While it is widely accepted that predictions across different stimulus attributes (e.g., time and content) facilitate sensory processing, it is unknown whether predictions across these attributes rely on the same neural mechanism. Here, to elucidate the neural mechanisms of predictions, we combine invasive electrophysiological recordings (human electrocorticography in 4 females and 2 males) with computational modeling while manipulating predictions about content (“what”) and time (“when”). We found that “when” predictions increased evoked activity over motor and prefrontal regions both at early (∼180 ms) and late (430–450 ms) latencies. “What” predictability, however, increased evoked activity only over prefrontal areas late in time (420–460 ms). Beyond these dissociable influences, we found that “what” and “when” predictability interactively modulated the amplitude of early (165 ms) evoked r...
Bayesian mapping reveals that attention boosts neural responses to predicted and unpredicted stimuli
2016
Predictive coding posits that the human brain continually monitors the environment for regularities and detects inconsistencies. It is unclear, however, what effect attention has on expectation processes, as there have been relatively few studies and the results of these have yielded contradictory findings. Here, we employed Bayesian model comparison to adjudicate between two alternative computational models. The Opposition model states that attention boosts neural responses equally to predicted and unpredicted stimuli, whereas the Interaction model assumes that attentional boosting of neural signals depends on the level of predictability. We designed a novel, audiospatial attention task that orthogonally manipulated attention and prediction by playing oddball sequences in either the attended or unattended ear. We observed sensory prediction error responses, with electroencephalography, across all attentional manipulations. Crucially, posterior probability maps revealed that, overal...
2021
According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in taskdriven attention and execution. In sum, we find that: (i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; (ii) there is no evidence, at the network level, for a distinction between error and prediction processing. According to the theory of predictive coding (PC) 1-5 , our brain constantly attempts to model the probability of its own future states, with the goal of minimizing uncertainty 4. More specifically, the brain is considered a hierarchically organized system where, at each level of processing, higher layers try to predict the latent causes of the sensory input coming from lower layers 6,7. Thus, neurons at higher levels encode predictions about the upcoming signal, which is continuously compared with the effective signal received from lower levels. Through this comparison, the brain either reinforces existing predictions or it updates them, if these do not match the incoming signal 8. When predictions are violated, a prediction error signal 5,9,10 is fed back to the neurons encoding predictions. These recursive loops of predictions and error signals ultimately allow the individual to maintain up-to-date representations about its own internal states 11 and the surrounding external stimuli. Over the past two decades, PC theory has received extensive support from a vast range of theoretical and experimental studies, both in relation to primary sensory processes 5,12-14 and higher level cognitive processes 15,16 , such as decision making and naturalistic speech comprehension 14,17,18. Moreover, evidence has been obtained with a variety of methods, mostly with functional magnetic resonance imaging (fMRI), but also electroencephalography 19-21 , computational simulations 22 , transcranial magnetic stimulation 23 , and physiological recordings of single neurons (for a review, see 24). Since 1999, when Rao and Ballard published their seminal simulation work on predictive coding in the visual cortex 5 , there has been a proliferation of attempts to implement PC in the human brain. Initially, it was argued that predictive processing occurs at the cellular level 25 , where the activity of neural populations is modulated by higher-order predictions and units signalling precision of those predictions. According to Bastos and colleagues 26 , PC is a typical property of the human cerebral neocortex because its structure suits a hierarchical signal exchange between cortical layers. In particular, error signals seem to be computed in the granular layers (especially layer IV), while predictions would be encoded in layers II and III 26. These mechanisms have been identified in a large set of brain areas, including the primary sensory and motor cortices, motor association cortices, dorsal and ventral prefrontal cortices, parietal cortex, anterior cingulate cortex, insula, hippocampus, amygdala, basal ganglia, thalamus, hypothalamus, cerebellum and the superior colliculus 27,28. However, in all these regions, neuronal
Stimulus prediction in the hippocampus
Perception can be cast as a process of inference, in which bottom-up signals are combined with top-down predictions in sensory systems. However, the source of these top-down predictions, especially when complex and multisensory, remains largely unknown. We hypothesised that the hippocampus — which rapidly learns arbitrary associations and has bidirectional connections with sensory systems — may be involved. We exposed humans to auditory cues predicting visual shapes, while measuring high-resolution fMRI signals in visual cortex and the hippocampus. Using multivariate reconstruction methods, we discovered a dissociation between these regions: representations in visual cortex were dominated by whichever shape was presented, whereas representations in the hippocampus (CA3 and subiculum, but not CA1) reflected only which shape was predicted by the cue. The strength of hippocampal predictions correlated across participants with the amount of expectation-related facilitation in visual cor...
Task relevance modulates the behavioural and neural effects of sensory predictions
PLoS biology, 2017
The brain is thought to generate internal predictions to optimize behaviour. However, it is unclear whether predictions signalling is an automatic brain function or depends on task demands. Here, we manipulated the spatial/temporal predictability of visual targets, and the relevance of spatial/temporal information provided by auditory cues. We used magnetoencephalography (MEG) to measure participants' brain activity during task performance. Task relevance modulated the influence of predictions on behaviour: spatial/temporal predictability improved spatial/temporal discrimination accuracy, but not vice versa. To explain these effects, we used behavioural responses to estimate subjective predictions under an ideal-observer model. Model-based time-series of predictions and prediction errors (PEs) were associated with dissociable neural responses: predictions correlated with cue-induced beta-band activity in auditory regions and alpha-band activity in visual regions, while stimulus-...
Probabilistic Decision Making by Slow Reverberation in Cortical Circuits
Neuron, 2002
motor output of the animal's decision). Indeed, Shadlen and Newsome found that activity of LIP cells signals the monkey's perceptual choice in both correct and error trials (Shadlen and Newsome, 1996, 2001). Activity of Summary LIP neurons showed a slow ramping time course during stimulus viewing and persisted throughout a delay be-Recent physiological studies of alert primates have revealed cortical neural correlates of key steps in a tween the stimulus and the monkey's saccadic response. LIP neurons do not simply reflect sensory sig-perceptual decision-making process. To elucidate synaptic mechanisms of decision making, I investi-nals, because their activity is correlated with what the monkey decides, when the decision varies from trial gated a biophysically realistic cortical network model for a visual discrimination experiment. In the model, to trial even at zero stimulus coherence. LIP neuronal activity cannot be purely a motor signal either, since its slow recurrent excitation and feedback inhibition produce attractor dynamics that amplify the difference time course varies systematically with the motion signal strength (the quality of the sensory information), even between conflicting inputs and generates a binary choice. The model is shown to account for salient though the saccadic motor output is basically the same (Shadlen and Newsome, 2001). characteristics of the observed decision-correlated neural activity, as well as the animal's psychometric Similar decision-correlated neural activity has been reported in prefrontal cortex during the same visual mo-function and reaction times. These results suggest that recurrent excitation mediated by NMDA receptors tion discrimination task (Kim and Shadlen, 1999) and in medial premotor cortex during a vibrotactile discrimina-provides a candidate cellular mechanism for the slow time integration of sensory stimuli and the formation of tion task (Romo et al., 1997; Herná ndez et al., 2002). Assuming that the neural activity signaling decisions is categorical choices in a decision-making neocortical network. generated within a cortical circuit, an intriguing question is: what are the basic cellular and synaptic mechanisms of a decision-making circuit? A clue comes from the
Striatal prediction error modulates cortical coupling
The Journal of …, 2010
Both perceptual inference and motor responses are shaped by learned probabilities. For example, stimulus-induced responses in sensory cortices and preparatory activity in premotor cortex reflect how (un)expected a stimulus is. This is in accordance with predictive coding accounts of brain function, which posit a fundamental role of prediction errors for learning and adaptive behavior. We used functional magnetic resonance imaging and recent advances in computational modeling to investigate how (failures of) learned predictions about visual stimuli influence subsequent motor responses. Healthy volunteers discriminated visual stimuli that were differentially predicted by auditory cues. Critically, the predictive strengths of cues varied over time, requiring subjects to continuously update estimates of stimulus probabilities. This online inference, modeled using a hierarchical Bayesian learner, was reflected behaviorally: speed and accuracy of motor responses increased significantly with predictability of the stimuli. We used nonlinear dynamic causal modeling to demonstrate that striatal prediction errors are used to tune functional coupling in cortical networks during learning. Specifically, the degree of striatal trial-by-trial prediction error activity controls the efficacy of visuomotor connections and thus the influence of surprising stimuli on premotor activity. This finding substantially advances our understanding of striatal function and provides direct empirical evidence for formal learning theories that posit a central role for prediction error-dependent plasticity.