Digital twin brain simulator for real-time consciousness monitoring and virtual intervention using primate electrocorticogram data

npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)

Published: Feb. 10, 2025

At the forefront of bridging computational brain modeling with personalized medicine, this study introduces a novel, real-time, electrocorticogram (ECoG) simulator, based on digital twin concept. Utilizing advanced data assimilation techniques, specifically Variational Bayesian Recurrent Neural Network model hierarchical latent units, simulator dynamically predicts ECoG signals reflecting real-time states. By assimilating broad from macaque monkeys across awake and anesthetized conditions, successfully updated its states in enhancing precision signal simulations. Behind successful assimilation, self-organization was observed, individuality. This facilitated simulation virtual drug administration uncovered functional networks underlying changes function during anesthesia. These results show that proposed can simulate high accuracy is also useful for revealing information processing dynamics.

Language: Английский

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A new predictive coding model for a more comprehensive account of delusions

The Lancet Psychiatry, Journal Year: 2024, Volume and Issue: 11(4), P. 295 - 302

Published: Jan. 16, 2024

Language: Английский

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Predictive coding networks for temporal prediction

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(4), P. e1011183 - e1011183

Published: April 1, 2024

One of the key problems brain faces is inferring state world from a sequence dynamically changing stimuli, and it not yet clear how sensory system achieves this task. A well-established computational framework for describing perceptual processes in provided by theory predictive coding. Although original proposals coding have discussed temporal prediction, later work developing mostly focused on static questions neural implementation properties networks remain open. Here, we address these present formulation model that can be naturally implemented recurrent networks, which activity dynamics rely only local inputs to neurons, learning utilises Hebbian plasticity. Additionally, show approximate performance Kalman filter predicting behaviour linear systems, behave as variant does track its own subjective posterior variance. Importantly, achieve similar accuracy without performing complex mathematical operations, but just employing simple computations biological networks. Moreover, when trained with natural dynamic inputs, found produce Gabor-like, motion-sensitive receptive fields resembling those observed real neurons visual areas. In addition, demonstrate effectively generalized nonlinear systems. Overall, models presented paper biologically plausible circuits predict future stimuli may guide research understanding specific areas involved prediction.

Language: Английский

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Modelling phenomenological differences in aetiologically distinct visual hallucinations using deep neural networks

Frontiers in Human Neuroscience, Journal Year: 2024, Volume and Issue: 17

Published: Jan. 3, 2024

Visual hallucinations (VHs) are perceptions of objects or events in the absence sensory stimulation that would normally support such perceptions. Although all VHs share this core characteristic, there substantial phenomenological differences between have different aetiologies, as those arising from Neurodegenerative conditions, visual loss, psychedelic compounds. Here, we examine potential mechanistic basis these by leveraging recent advances visualising learned representations a coupled classifier and generative deep neural network-an approach call 'computational (neuro)phenomenology'. Examining three aetiologically distinct populations which occur-Neurodegenerative conditions (Parkinson's Disease Lewy Body Dementia), loss (Charles Bonnet Syndrome, CBS), psychedelics-we identified dimensions relevant to distinguishing classes VHs: realism (veridicality), dependence on input (spontaneity), complexity. By selectively tuning parameters visualisation algorithm reflect influence along each were able generate 'synthetic VHs' characteristic experienced aetiology. We verified validity experimentally two studies examined phenomenology CBS patients, people with experience. These confirmed existence across groups, crucially, found appropriate synthetic rated being representative group's hallucinatory phenomenology. Together, our findings highlight diversity associated causal factors demonstrate how network model can successfully capture distinctive characteristics

Language: Английский

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Adaptation in the visual system: Networked fatigue or suppressed prediction error signalling?

Cortex, Journal Year: 2024, Volume and Issue: 177, P. 302 - 320

Published: June 10, 2024

Our brains are constantly adapting to changes in our visual environments. Neural adaptation exerts a persistent influence on the activity of sensory neurons and perceptual experience, however there is lack consensus regarding how implemented system. One account describes fatigue-based mechanisms embedded within local networks stimulus-selective (networked fatigue models). Another depicts as product stimulus expectations (predictive coding In this review, I evaluate neuroimaging psychophysical evidence that poses fundamental problems for predictive models neural adaptation. Specifically, discuss observations distinct repetition expectation effects, well incorrect predictions repulsive aftereffects made by accounts. Based evidence, argue networked provide more parsimonious effects Although can be formed based recent stimulation history, any consequences these likely co-occur (or interact) with conclude proposing novel, testable hypotheses relating interactions between other processes, focusing feature extrapolation phenomena.

Language: Английский

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Predictive Coding algorithms induce brain-like responses in Artificial Neural Networks

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 20, 2025

Abstract This study explores whether predictive coding (PC) inspired Deep Neural Networks can serve as biologically plausible neural network models of the brain. We compared two PC-inspired training objectives, a and contrastive approach, to supervised baseline in simple Recurrent Network (RNN) architecture. evaluated on key signatures PC, including mismatch responses, formation priors, learning semantic information. Our results show that models, especially locally trained model, exhibited these PC-like behaviors better than Supervised or an Untrained RNN. Further, we found activity regularization evokes response-like effects across all suggesting it may proxy for energy-saving principles PC. Finally, find Gain Control (an important mechanism PC framework) be implemented using weight regularization. Overall, our findings indicate are able capture computational processing brain, promising foundation building artificial networks. work contributes understanding relationship between biological networks, highlights potential algorithms advancing brain modelling well brain-inspired machine learning.

Language: Английский

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Unpacking the Complexities of Consciousness: Theories and Reflections

Neuroscience & Biobehavioral Reviews, Journal Year: 2025, Volume and Issue: unknown, P. 106053 - 106053

Published: Feb. 1, 2025

As the field of consciousness science matures, research agenda has expanded from an initial focus on neural correlates consciousness, to developing and testing theories consciousness. Several have been put forward, each aiming elucidate relationship between brain function. However, there is ongoing, intense debate regarding whether these examine same phenomenon. And, despite ongoing efforts, it seems like so far failed converge around any single theory, instead exhibits significant polarization. To advance this discussion, proponents five prominent consciousness-Global Neuronal Workspace Theory (GNWT), Higher-Order Theories (HOT), Integrated Information (IIT), Recurrent Processing (RPT), Predictive (PP)-engaged in a public 2022, as part annual meeting Association for Scientific Study Consciousness (ASSC). They were invited clarify explananda their theories, articulate core mechanisms underpinning corresponding explanations, outline foundational premises. This was followed by open discussion that delved into testability potential evidence could refute them, areas consensus disagreement. Most importantly, demonstrated at stage, more controversy than agreement pertaining most basic questions what is, how identify conscious states, required theory Addressing crucial advancing towards deeper understanding comparison competing theories.

Language: Английский

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Pupil dilation offers a time-window on prediction error

Published: Feb. 19, 2025

Task-evoked pupil dilation has been linked to many cognitive variables, perhaps most notably unexpected events. Zénon (2019) proposed a unifying framework stating that related cognition should be considered from an information-theory perspective. In the current study, we investigated whether pupil’s response decision outcome in context of associative learning reflects prediction error defined formally as information gain, while also exploring time course this signal. To do so, adapted simple model trial-by-trial stimulus probabilities based on theory previous literature. We analyzed two data sets which participants performed perceptual decision-making tasks required was recorded. Our findings consistently showed significant proportion variability post-feedback during can explained by formal quantification gain shortly after feedback presentation both task contexts. later window, relationship between information-theoretic variables and differed per task. For first time, present evidence dilates or constricts along with seems dependent, specifically increasing decreasing average uncertainty (entropy) across trials. This study offers empirical showcasing how offer valuable insights into process updating learning, highlighting promising utility readily accessible physiological indicator for investigating internal belief states.

Language: Английский

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Pupil dilation offers a time-window on prediction error

Published: Feb. 19, 2025

Task-evoked pupil dilation has been linked to many cognitive variables, perhaps most notably unexpected events. Zénon (2019) proposed a unifying framework stating that related cognition should be considered from an information-theory perspective. In the current study, we investigated whether pupil’s response decision outcome in context of associative learning reflects prediction error defined formally as information gain, while also exploring time course this signal. To do so, adapted simple model trial-by-trial stimulus probabilities based on theory previous literature. We analyzed two data sets which participants performed perceptual decision-making tasks required was recorded. Our findings consistently showed significant proportion variability post-feedback during can explained by formal quantification gain shortly after feedback presentation both task contexts. later window, relationship between information-theoretic variables and differed per task. For first time, present evidence dilates or constricts along with seems dependent, specifically increasing decreasing average uncertainty (entropy) across trials. This study offers empirical showcasing how offer valuable insights into process updating learning, highlighting promising utility readily accessible physiological indicator for investigating internal belief states.

Language: Английский

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Beyond Markov: Transformers, memory, and attention

Cognitive Neuroscience, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: April 15, 2025

This paper asks what predictive processing models of brain function can learn from the success transformer architectures. We suggest that reason architectures have been successful is they implicitly commit to a non-Markovian generative model - in which we need memory contextualize our current observations and make predictions about future. Interestingly, both notions working cognitive science rely heavily upon concept attention. will argue move beyond Markov crucial construction capable dealing with much sequential data certainly language brains contend with. characterize two broad approaches this problem deep temporal hierarchies autoregressive transformers being an example latter. Our key conclusions are benefit their use embedding spaces place strong metric priors on implicit latent variable utilize direct form attention highlights most relevant, not only recent, previous elements sequence help predict next.

Language: Английский

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