Topic: prediction errors

Predictive processing offers a unifying view of neural computation, proposing that brains continuously anticipate sensory input 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

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

Predictive processing is a computational framework that aims to explain how the brain processes 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.

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

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

Our brains predict future sensory input based on their current beliefs about 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

When you hear two versions of the same event, you can either update the memory of the first version with information from the second (updating) or retain both separately (differentiation). 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