Predictive coding

…Topics include recurrent networks, predictive coding, active inference, generalisation during development, executive control and working memory. Duties include model and task design, simulation or robotics experiments, analysis and first-author papers. The full-time appointment initially lasts two years; the expected annual salary is JPY 4.6–5.9 million. Recruitment remains open until all positions are filled, with an early start preferred. Follow the emai

…by mimicking the brain’s information coding via discrete voltage spikes. However, it remains unclear how both artificial and natural networks of spiking neurons can learn energy-efficient information processing strategies. Here we propose Predictive Coding Light (PCL), a recurrent hierarchical spiking neural network for unsupervised representation learning. In contrast to previous predictive coding approaches, PCL does not transmit prediction errors to higher

In a first part of the talk, I will present Predictive Coding Light (PCL), a novel unsupervised learning architecture for spiking neural networks. In contrast to conventional predictive coding approaches, which only transmit prediction errors to higher processing stages, PCL learns inhibitory lateral and top-down connectivity to suppress the most predictable spikes and passes a compressed representation of the input to higher processing stages. We show that PCL reproduces a range of biological findings and exhibits a favorable tradeoff between energy consumption and downstream classification p

Aggression can be a fundamental social behavior in many species and is importantly modulated by prior aggressive experience. However, how this experience shapes neural activity and whether it predicts behaviors in novel, more challenging aggression contexts are unknown. Moreover, imitation learning theories of aggression suggest that this behavior can be transferred through observation alone1, but whether this learning leverages the same neural mechanisms as experience is unclear. To address these two questions, here we combine novel behavioral mapping strategies with cell-type-specific and mu

…Predictive processing accounts of hallucinations suggest that these alterations bias patients’ perception toward their sensory expectations. However, 1) whether this framework holds when considering real-world hallucinations with complex content and 2) which upstream neural mechanisms cause these alterations in learning are hotly debated. To address these questions, we used fMRI to record neur

Artificial neural networks are frequently used to model neural systems, and have been shown to recapitulate features of biological networks, including in the hippocampus [1] and visual cortex [2] [3]. However, despite it’s success in neural modelling, the predominant learning algorithm, backpropagation, has long been considered biologically implausible [4]. One reason is the weight-transport problem - or the problem of how a global error signal can be accurately transmitted across the network to minimize the error resulting from each layer’s parameters. Previous solutions to the weight-transpo

Recurrent neural networks (RNNs) have increasingly been employed to model cortical function, but conventional architectures lack biological realism. Notably, they often ignore two key biological principles: i) Dale’s Law, which constrains neurons to either be strictly excitatory or inhibitory, and ii) Sparse, structured connectivity motifs observed in the brain. Incorporating these constraints however poses challenges, as they often affect model performance negatively. Our work presents a novel approach that integrates Dale’s backpropagation, a sign-constrained training method, with topologica

…[1], respectively relaying, in a predictive coding interpretation, top-down expectations and bottom-up prediction error signals [2], which target and originate from distinct cortical layers with different dominant frequencies [3]. However, this oscillatory-multiplexed routing theory presents several challenges. Multiplexed routing might occur indeed without need for distinct frequencies of operation [4]. Furthermore, phasic enhancement

…by models of corollary discharge and predictive coding, we hypothesize that in the sensory phase, juvenile auditory areas gradually learn to canc

Understanding how the brain encodes stimuli is a fundamental problem in computational neuroscience. This has inspired the development of artificial neural networks that learn representations by incorporating brain-like learning abilities. Recent work has focused on learning representations (latents) by capturing similarity between inputs and latents, but these methods have typically been used to learn downstream features from the input and have not been studied in the context of a generative paradigm, where one can map the representations back to the input space, incorporating not only bottom-

The ability to reconstruct imagery represented by the brain has the potential to give us an intuitive understanding of what the brain “sees”. Reconstruction of visual input from human fMRI data has garnered significant attention in recent years. Comparatively less focus has been directed towards vision reconstruction from single-cell recordings, despite its potential to provide a more direct measure of the information represented by the brain. Here, we achieve high-quality reconstructions of videos presented to mice, from the activity of neurons in their visual cortex. With our method of video

One of key hallmarks of cognitive flexibility is the capability of quickly performing neural computation to accommodate dynamic, context-switching environments and deal with task uncertainty [1-3]. Accumulating evidence from human and animal experiments have suggested that the mediodorsal (MD) thalamus and prefrontal cortex (PFC) are a pair of critical partners to achieve cognitive control. e found that such multiplicative thalamocortical gating enables a local Hebbian-weight amplification for prefrontal synaptic plasticity and is the key to facilitate a low-rank structure in prefrontal comput

…experiments have identified a class of predictive grid cells that encode the animal’s upcoming location 1,2. Continuous attractor networks (CAN) explain how grid cells develop their characteristic hexagonal firing pattern and enable path integration. But how does predictive coding arise

Eli Sennesh presents divide-and-conquer predictive coding, a probabilistic proposal for how brains might implement prediction and error correction. The conversation relates this framework to communication across cortical circuits and distinguishes theoretical explanations from computational modelling, building on questions raised by active predictive coding.

Rajesh Rao revisits predictive coding and its extension from sensory inference to action, including proposed roles for cortical layers. He also describes brain-to-brain interfaces and neural co-processors that use artificial networks to restore or augment function, before discussing his separate work on the undeciphered Indus script.

Applying the principles of predictive coding to reading has successfully increased our understanding of the neuro-cognitive processes underlying the extraction of meaning from text. Central is integrating prior knowledge with the sensory input to highlight the informative parts of a visual percept, thus increasing the efficiency of the representation and processing in the visual pathway. Current computer vision models (e.g., deep learning models) can easily predict letter identities; however, their inner workings remain opaque, making it extremely hard to infer how the model implements the t

…evidence gathering according to the predictive coding hypothesis, or it might simply indicate computational bottlenecks. Here, we claim that dynamical systems theory and large-scale network modeling may offer crucial insights to adjudicate this debate. We attempt to harmonize brain dynamics with dual processing modes by suggesting a link with another aspect of neuro

…accepted computational principles of predictive coding so th

…We show that predictive coding, constrained by endogenous rhythms, accounts for these quizzical results and enables robust lexical speech recovery. We build upon the major hypotheses that the rhythmic st

…phenomenon potentially explained by predictive coding. This theory suggests that the brain anticipates incoming information, minimizing further processing for familiar stimuli. For example, fusiform face area (FFA) consistently shows decreased neural response to repeated presentation of the same faces. According to the traditional model of visual processing, information progresses from the lower brain regions' superficial cortical layers to the higher areas' middle layers. Within this framewor

Predictive coding theories argue that recent experience 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

Predictive coding theories propose that the brain constantly updates its internal 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,

…According to predictive coding theories, sensory perception would rely on the continuous comparison of expected sensory inputs with those actually received, and the error signal generated in case of mismatch be

…that the brain uses a probabilistic predictive coding scheme to update beliefs about temperature changes based on their uncertainty. We find that these expectations directly modulate the perception of pain in the thermal grill illusion as well as the constituent innocuous cold and warm