The Human Affectome

Neuroscience & Biobehavioral Reviews, Journal Year: 2023, Volume and Issue: 158, P. 105450 - 105450

Published: Nov. 3, 2023

Over the last decades, interdisciplinary field of affective sciences has seen proliferation rather than integration theoretical perspectives. This is due to differences in metaphysical and mechanistic assumptions about human phenomena (what they are how work) which, shaped by academic motivations values, have determined constructs operationalizations. An assumption on purpose can be used as a teleological principle guide construction common set assumptions—a framework for research. In this capstone paper special issue "Towards an Integrated Understanding Human Affectome", we gather tiered synthesize that account collectively. teleologically-grounded offers principled agenda launchpad both organizing existing perspectives generating new ones. Ultimately, hope Affectome brings us step closer not only integrated understanding phenomena, but

Language: Английский

---

How our understanding of memory replay evolves

Journal of Neurophysiology, Journal Year: 2023, Volume and Issue: 129(3), P. 552 - 580

Published: Feb. 8, 2023

Memory reactivations and replay, widely reported in the hippocampus cortex across species, have been implicated memory consolidation, planning, spatial skill learning. Technological advances electrophysiology, calcium imaging, human neuroimaging techniques enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution provided opportunities for developing robust analytic methods identify replay. In this article, we first review a large body of historically important representative replay studies from animal literature. We then discuss our current understanding functions learning, consolidation further progress computational modeling that has contributed these improvements. Next, past present analyses their limitations challenges. Finally, looking ahead, some promising detecting nonstereotypical, behaviorally nondecodable structures recordings. argue seamless integration multisite recordings, real-time decoding, closed-loop manipulation experiments will be essential delineating role wide range cognitive motor functions.

Language: Английский

---

Mediodorsal thalamus regulates task uncertainty to enable cognitive flexibility

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 18, 2025

The mediodorsal (MD) thalamus is a critical partner for the prefrontal cortex (PFC) in cognitive control. Accumulating evidence has shown that MD regulates task uncertainty decision making and enhance flexibility. However, computational mechanism of this process remains unclear. Here we trained biologically-constrained models to delineate mechanistic role context-dependent making. We show addition feedforward structure recurrent PFC increases robustness low cueing signal-to-noise ratio, enhances working memory, enables rapid context switching. Incorporating genetically identified thalamocortical connectivity interneuron cell types into model replicates key neurophysiological findings task-performing animals. Our reveals mechanisms geometric interpretations regulating cue switching enable makes experimentally testable predictions linking deficits with disrupted connectivity, excitation-inhibition imbalance dysfunctional inhibitory types. by which activity flexibility not fully understood. authors build incorporating Together experimental testing, circuit

Language: Английский

---

Connecting brain and behavior in clinical neuroscience: A network approach

Neuroscience & Biobehavioral Reviews, Journal Year: 2021, Volume and Issue: 130, P. 81 - 90

Published: July 26, 2021

In recent years, there has been an increase in applications of network science many different fields. clinical neuroscience and psychopathology, the developments have occurred mostly simultaneously, but without much collaboration between two The promise integrating these lies a united framework to tackle one fundamental questions our time: how understand link brain behavior. current overview, we bridge this gap by introducing conventions both fields, highlighting similarities, creating common language that enables exploitation synergies. We provide research examples autism research, as it accurately represents lines psychological networks. integrate behavior not only semantically, also practically, showcasing three methodological avenues allow combine networks behavioral data. As such, paper offers stepping stone further develop multi-modal

Language: Английский

---

Centering cognitive neuroscience on task demands and generalization

Nature Neuroscience, Journal Year: 2024, Volume and Issue: 27(9), P. 1656 - 1667

Published: July 29, 2024

Language: Английский

---

Hierarchical predictive coding in distributed pain circuits

Frontiers in Neural Circuits, Journal Year: 2023, Volume and Issue: 17

Published: March 3, 2023

Predictive coding is a computational theory on describing how the brain perceives and acts, which has been widely adopted in sensory processing motor control. Nociceptive pain involves large distributed network of circuits. However, it still unknown whether this completely decentralized or requires networkwide coordination. Multiple lines evidence from human animal studies have suggested that cingulate cortex insula (cingulate-insula network) are two major hubs mediating information afferents spinothalamic inputs, whereas subregions cortices distinct projections functional roles. In mini-review, we propose an updated hierarchical predictive framework for perception discuss its related computational, algorithmic, implementation issues. We suggest active inference as generalized algorithm, hierarchically organized traveling waves independent neural oscillations plausible mechanism to integrate bottom-up top-down across

Language: Английский

---

Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region

PLoS Biology, Journal Year: 2024, Volume and Issue: 22(11), P. e3002861 - e3002861

Published: Nov. 5, 2024

The CA1 region of the hippocampus is one most studied regions rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth experimental data on its structure function, it has been challenging integrate information obtained from diverse approaches. To address this challenge, we present community-based, full-scale silico model rat that integrates broad range data, synapse network, including reconstruction principal afferents, Schaffer collaterals, effects acetylcholine system. We tested validated each component final network model, made input assumptions, strategies explicit transparent. unique flexibility allows scientists potentially scientific questions. In article, describe methods used set up simulations reproduce vitro vivo experiments. Among several applications focus theta rhythm, prominent hippocampal oscillation associated with various behavioral correlates use our computer findings. Finally, make code, available through hippocampushub.eu portal, which also provides extensive analyses user-friendly interface facilitate adoption usage. This community-based represents valuable tool for integrating foundation further research into complex workings region.

Language: Английский

---

Understanding the layered brain architecture for motivation: Dynamical systems, computational neuroscience, and robotic approaches

˜The œPsychology of learning and motivation/˜The œpsychology of learning and motivation, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

---

Relationships and representations of brain structures, connectivity, dynamics and functions

Progress in Neuro-Psychopharmacology and Biological Psychiatry, Journal Year: 2025, Volume and Issue: unknown, P. 111332 - 111332

Published: March 1, 2025

Language: Английский

---

Pain, from perception to action: A computational perspective

iScience, Journal Year: 2022, Volume and Issue: 26(1), P. 105707 - 105707

Published: Dec. 1, 2022

Pain is driven by sensation and emotion, in turn, it motivates decisions actions. To fully appreciate the multidimensional nature of pain, we formulate study pain within a closed-loop framework sensory-motor prediction. In this cycle, prediction plays an important role, as interaction between actual sensory experience shapes perception subsequently, action. Perspective, describe roles two prominent computational theories-Bayesian inference reinforcement learning-in modeling adaptive behaviors. We show that serves common theme these theories, each theories can explain unique aspects perception-action cycle. discuss how models improve our mechanistic understandings pain-centered processes such anticipation, attention, placebo hypoalgesia, chronification.

Language: Английский

---