Prediction errors

…and update internal models based on prediction errors. In this talk, I will present converging evidence for the computational mechanisms underlying this framework across human neuroscience and deep neural networks. I will begin with recent work showing that large-scale distributed prediction-error encoding in the human brain directly predicts how sensory representations reorganize through predictive learning. I will then turn to PredNet, a popular predictive coding in

Minimising cortical prediction errors is thought to be a key computation underlying perception, action, and learning. Yet, how the cortex represents and uses uncertainty in this process remains unclear. In the first part of this talk, I will present a normative framework showing how uncertainty can modulate prediction error activity to yield uncertainty-modulated prediction errors (UPEs), hypothesised to be represented by layer 2/3 pyramidal neurons. We propose that these UPEs are computed through inhibitory mechanisms involving SST and PV interneurons. A circuit model demonstrates how cortica

Serotonin (5-HT) is implicated in decision-making, such as signaling prediction errors and cue values, as well as to the regulation of behavioral states of wakefulness and arousal, but the relationship between these two domains of function is not well understood. On one hand, serotonin activity is negatively associated with locomotor behavior, which has been seen as behavioral inhibition. On the other hand, serotonin transients are also associated with surprise or uncertainty, as well as novelty responses, including exploration-related facial movements (e.g., whisking, sniffing). Because ther

Humans and other animals approach reward and avoid punishment and pay attention to cues predicting these events. Such motivated behavior thus appears to be guided by value, which directs behavior towards or away from positively or negatively valenced outcomes. Moreover, it is facilitated by (top-down) salience, which enhances attention to behaviorally relevant learned cues predicting the occurrence of valenced outcomes. Using human neuroimaging, we recently separated value (ventral striatum, posterior ventromedial prefrontal cortex) from salience (anterior ventromedial cortex, occipital cortex

…the world around us, but interpreting prediction errors can be challenging in a volatile environment because they can be caused by stochastic sensory noise or by outdated predictions. Noisy signals should be integrated with prior beliefs to improve precision, but the two should be segregated when environmental changes render prior beliefs irrelevant. Bayesian inference provides a statistically optimal solution to deal with such problems of causal uncertainty [1]. However, the method quickly becomes memory intensive an

…Prediction errors, the deviation between expected and actual outcomes, are presumably involved in mediating the competition between these processes. However, the mechanisms underlying this competition remain unclear. Here, we present a Hopfield network model that aims to account for the results of a behavioral study of memory updating. Participants heard conversational stimuli that were modified relative t

Sensorimotor adaptation, crucial for refining movements in response to unexpected errors, fundamentally depends on the cerebellum. The olivary climbing fiber (CF) input to cerebellar Purkinje cells (PC) acts as a “teacher” signal during motor learning and drives cerebellar plasticity. Although the CF input reliably evokes PC dendritic spikes, it is not the only signal that modulates this post-synaptic response. To disentangle the modulation of the pre-synaptic CF burst from that of its post-synaptic PC dendritic response, we simultaneously imaged PC dendrites and CF terminals with 2-photon mic

…wrong beliefs when confronted with prediction errors. One should integrate out noise to improve precision, but simultaneously recognize when predictions are irrelevant due to environmental change. Bayesian inference prescribes a statistically optimal solution to this problem, but it is memory intensive and computationally complex, thus unlikely to be used continuously by humans.Here, we systematically investigated which hallmarks of Bayesian inference are present in previously published human response

…establishes expectations that generate prediction errors when violated. Neural correlates of prediction errors have been widely reported, but their perceptual consequences remain unknown. We had observers perform a dual-report task on the orientation of briefly presented gratings within predictable or random sequences. Observers first made a speeded response to categorize grating orientation (clockwise or counterclockwise from vertical), then reproduced the orientation without time pressure by rotating a bar. This allowed us to separately

…models of the world to minimize prediction errors and optimize sensory processing. However, the neural mechanisms that link the encoding of prediction errors and optimization of sensory representations remain unclear. Here, we provide direct evidence of how predictive learning shapes the representational geometry of the human brain. We recorded magnetoencephalography (MEG) in human participants listening to acoustic sequences with different levels of regularity. Representational similarity analysis revealed how,

…sensory information by making predictions about the environment and minimizing prediction errors. It can also be used to explain some of the key symptoms of psychotic disorders such as schizophrenia. In my talk, I will provide an overview of our progress in this endeavor.

Value-based decision-making in a context-changing environment is known to be guided by the two distinctive reinforcement learning (RL) strategies: goal-directed and habitual learning. Despite decades-long efforts to reveal that different environmental variables influence these RL processes, it remains unknown exactly how we create a situation by manipulating a set of environmental variables to guide these processes separately. This is mostly because of the complex interactions between environmental variables and RL, often beyond the experimenter’s comprehension. Here we show that an RL algorit

Contemporary Bayesian theories of the mind highlight the role of prediction errors as a driving force in learning processes and the updating of our models of the world and of ourselves. This process is postulated to manifest both at explicit levels (e.g., subjective responses) and at implicit levels (e.g., eye movements, reaction time) of behavior. We developed a novel associative learning task in immersive virtual reality, in which a cue probabilistically predicted the subsequent location of a target to which participants had to reach. Specifically, the current study (N = 30) examined: (1) wh

The brain learns an internal model of the world by making predictions about upcoming inputs and comparing its predictions with actual incoming sensory information. Prediction errors are essential for learning an accurate internal representation of the world because they indicate where the internal representation needs to be improved to make a better prediction in the future. Promisingly, a neural correlate for prediction errors has been found in the activity of pyramidal neurons in layer 2/3 of diverse cortical areas. To make contextually appropriate predictions in a stochastic environment, th

The remarkable ability of neural networks to make predictions about the immediate future and recognize unexpected events is a ubiquitous hallmark of intelligent life. In recent years, researchers have begun to unravel the neural substrates underlying this predictive information processing [1]. An integral part of predictive processing is a subset of excitatory neurons that encode prediction errors: while negative prediction-error (nPE) neurons are only activated when sensory signals are weaker than predicted, positive prediction-error (pPE) neurons respond only when sensory signals exceed the

At any given moment the brain receives more sensory information than it can use to guide adaptive behaviour, creating the need for mechanisms that promote efficient processing of incoming sensory signals. One way in which the brain might reduce its sensory processing load is to encode successive presentations of the same stimulus in a more efficient form, a process known as neural adaptation. Conversely, when a stimulus violates an expected pattern, it should evoke an enhanced neural response. Such a scheme for sensory encoding has been formalised in predictive coding theories, which propose t