Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex DOI Creative Commons
Jean‐Paul Noel, Edoardo Balzani, Cristina Savin

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: July 31, 2023

Abstract Natural behaviors occur in closed action-perception loops and are supported by dynamic flexible beliefs abstracted away from our immediate sensory milieu. How this real-world flexibility is instantiated neural circuits remains unknown. Here we have macaques navigate a virtual environment primarily leveraging (optic flow) signals, or more heavily relying on acquired internal models. We record single-unit spiking activity simultaneously the dorsomedial superior temporal area (MSTd), parietal 7a, dorso-lateral prefrontal cortex (dlPFC). Results show that while animals were able to maintain adaptive task-relevant regardless of context, fine-grain statistical dependencies between neurons, particularly 7a dlPFC, dynamically remapped with changing computational demands. In but not destroying these abolished area’s ability for cross-context decoding. Lastly, correlation analyses suggested unit-to-unit couplings less they did so MSTd, population codes behavior impacted loss evidence. conclude functional connectivity neurons maintains stable code context-invariant during naturalistic loops.

Language: Английский

Emergence of belief-like representations through reinforcement learning DOI Creative Commons
Jay A. Hennig, Sandra Romero Pinto, Takahiro Yamaguchi

et al.

PLoS Computational Biology, Journal Year: 2023, Volume and Issue: 19(9), P. e1011067 - e1011067

Published: Sept. 11, 2023

To behave adaptively, animals must learn to predict future reward, or value. do this, are thought reward predictions using reinforcement learning. However, in contrast classical models, estimate value only incomplete state information. Previous work suggests that partially observable tasks by first forming "beliefs"-optimal Bayesian estimates of the hidden states task. Although this is one way solve problem partial observability, it not way, nor most computationally scalable solution complex, real-world environments. Here we show a recurrent neural network (RNN) can directly from observations, generating prediction errors resemble those observed experimentally, without any explicit objective estimating beliefs. We integrate statistical, functional, and dynamical systems perspectives on beliefs RNN's learned representation encodes belief information, but when capacity sufficiently large. These results illustrate how explicitly beliefs, yielding useful for with limited capacity.

Language: Английский

Citations

15
Fragmentation and Multithreading of Experience in the Default-Mode Network DOI Creative Commons

Fahd Yazin,

Gargi Majumdar,

Neil Bramley

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 25, 2024

Abstract Reliance on internal predictive models of the world is central to many theories human cognition. Yet it unknown whether humans acquired multiple separate models, each evolved for a specific domain, or maintain globally unified representation. Using fMRI, we show that during naturalistic experiences (during movie watching narrative listening), adult participants selectively engage three topographically distinct midline prefrontal cortical regions, different forms predictions. Regions responded abstract spatial, referential (social), and temporal domains model updates implying representations each. Prediction-error-driven neural transitions in these indicative updates, preceded subjective belief changes domain-specific manner. We find parallel top-down predictions are integrated with sensory streams Precuneus, shaping participants’ ongoing experience. Results generalized across modalities content, suggesting recruit abstract, modular both vision language. Our results highlight key feature modeling: fragmenting information into before global integration.

Language: Английский

Citations

1
Dorsolateral prefrontal cortex drives strategic aborting by optimizing long-run policy extraction DOI Creative Commons
Jean‐Paul Noel, Ruiyi Zhang, Xaq Pitkow

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 28, 2024

Abstract Real world choices often involve balancing decisions that are optimized for the short-vs. long-term. Here, we reason apparently sub-optimal single trial in macaques may fact reflect long-term, strategic planning. We demonstrate freely navigating VR sequentially presented targets will strategically abort offers, forgoing more immediate rewards on individual trials to maximize session-long returns. This behavior is highly specific individual, demonstrating about their own long-run performance. Reinforcement-learning (RL) models suggest this algorithmically supported by modular actor-critic networks with a policy module not only optimizing long-term value functions, but also informed of state-action values allowing rapid optimization. The artificial suggests changes matched offer ought be evident as soon offers made, even if aborting occurs much later. confirm prediction units and population dynamics macaque dorsolateral prefrontal cortex (dlPFC), parietal area 7a or dorsomedial superior temporal (MSTd), upcoming reward-maximizing upon presentation. These results cast dlPFC specialized module, stand contrast recent work distributed recurrent nature belief-networks.

Language: Английский

Citations

0
Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex DOI Creative Commons
Jean‐Paul Noel, Edoardo Balzani, Cristina Savin

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: July 31, 2023

Abstract Natural behaviors occur in closed action-perception loops and are supported by dynamic flexible beliefs abstracted away from our immediate sensory milieu. How this real-world flexibility is instantiated neural circuits remains unknown. Here we have macaques navigate a virtual environment primarily leveraging (optic flow) signals, or more heavily relying on acquired internal models. We record single-unit spiking activity simultaneously the dorsomedial superior temporal area (MSTd), parietal 7a, dorso-lateral prefrontal cortex (dlPFC). Results show that while animals were able to maintain adaptive task-relevant regardless of context, fine-grain statistical dependencies between neurons, particularly 7a dlPFC, dynamically remapped with changing computational demands. In but not destroying these abolished area’s ability for cross-context decoding. Lastly, correlation analyses suggested unit-to-unit couplings less they did so MSTd, population codes behavior impacted loss evidence. conclude functional connectivity neurons maintains stable code context-invariant during naturalistic loops.

Language: Английский

Citations

1