A Model of Place Field Reorganization During Reward Maximization DOI Creative Commons
M Ganeshkumar, Blake Bordelon, Jacob A. Zavatone-Veth

et al.

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

Published: Dec. 16, 2024

Abstract When rodents learn to navigate in a novel environment, high density of place fields emerges at reward locations, elongate against the trajectory, and individual change spatial selectivity while demonstrating stable behavior. Why demonstrate these characteristic phenomena during learning remains elusive. We develop normative framework using maximization objective, whereby temporal difference (TD) error drives field reorganization improve policy learning. Place are modeled Gaussian radial basis functions represent states an directly synapse actorcritic for Each field’s amplitude, center, width, as well downstream weights, updated online each time step maximize cumulative reward. that this unifies three disparate observed navigation experiments. Furthermore, we show convergence when single target relearning multiple new targets. To conclude, model recapitulates several aspects hippocampal dynamics mechanisms offer testable predictions future

Language: Английский

Learning produces an orthogonalized state machine in the hippocampus DOI Creative Commons
Weinan Sun, Johan Winnubst, Maanasa Natrajan

et al.

Nature, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 12, 2025

Abstract Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning behaviour. have been observed in the hippocampus 1 , but their algorithmic form learning mechanisms remain obscure. Here we large-scale, longitudinal two-photon calcium imaging record activity from thousands of neurons CA1 region while mice learned efficiently collect rewards two subtly different linear tracks virtual reality. Throughout learning, both animal behaviour hippocampal neural progressed through multiple stages, gradually revealing improved task representation mirrored behavioural efficiency. The process involved progressive decorrelations initially similar within across tracks, ultimately resulting orthogonalized representations resembling a state machine capturing inherent structure task. This decorrelation was driven individual acquiring task-state-specific responses (that is, ‘state cells’). Although various standard artificial networks did not naturally capture these dynamics, clone-structured causal graph, hidden Markov model variant, uniquely reproduced final states trajectory seen animals. cellular population dynamics constrain underlying cognitive map formation hippocampus, pointing inference as fundamental computational principle, implications for biological intelligence.

Language: Английский

Citations

4
Spaces and sequences in the hippocampus: a homological perspective DOI Creative Commons

Andrey Babichev,

Vladmir Vashin,

Yuri Dabaghian

et al.

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

Published: Feb. 10, 2025

Topological techniques have become a popular tool for studying information flows in neural networks. In particular, simplicial homology theory is used to analyze how cognitive representations of space emerge from large conglomerates independent neuronal contributions. Meanwhile, growing number studies suggest that many functions are sustained by serial patterns activity. Here, we investigate stashes such using path —an impartial, universal approach does not require priori assumptions about the sequences’ nature, functionality, underlying mechanisms, or other contexts. We focus on hippocampus—a key enabler learning and memory mammalian brains—and quantify ordinal arrangement its activity similarly topology has previously been studied terms homologies. The results reveal vast majority sequences produced during spatial navigation structurally equivalent one another. Only few classes distinct form an schema remains stable as pool consolidates. Importantly, structure both maps upheld combinations short sequences, suggesting brief motifs dominate physiological computations. This organization emerges stabilizes timescales characteristic learning, displaying similar dynamics. Yet, generally do reflect topological affinities—spatial sequential analyses address qualitatively different aspects spike flows, representing two complementary formats processing. Significance statement study employs examine hippocampus, critical region memory. While traditional, approaches model maps, provides framework analyzing without presupposing their nature function. findings limited sequence classes, supported motifs, over corresponding periods learning. Notably, derived these capture affinities, highlighting but dimensions

Language: Английский

Citations

0
The adaptive value of behavioral inhibition DOI Creative Commons
Rodrigo Sosa

Current Opinion in Behavioral Sciences, Journal Year: 2025, Volume and Issue: 63, P. 101523 - 101523

Published: April 17, 2025

Language: Английский

Citations

0
Spatial periodicity in grid cell firing is explained by a neural sequence code of 2-D trajectories DOI Open Access

Rebecca RG,

Giorgio A. Ascoli,

Nate M Sutton

et al.

Published: April 24, 2025

Spatial periodicity in grid cell firing has been interpreted as a neural metric for space providing animals with coordinate system navigating physical and mental spaces. However, the specific computational problem being solved by cells remained elusive. Here, we provide mathematical proof that spatial is only possible solution to sequence code of 2-D trajectories hexagonal pattern most parsimonious such code. We thereby likely teleological cause existence reveal underlying nature global geometric organization maps direct consequence simple local A provides intuitive explanations many previously puzzling experimental observations may transform our thinking about cells.

Language: Английский

Citations

0
Spatial periodicity in grid cell firing is explained by a neural sequence code of 2-D trajectories DOI Creative Commons

Rebecca RG,

Giorgio A. Ascoli, Nate Sutton

et al.

eLife, Journal Year: 2025, Volume and Issue: 13

Published: May 21, 2025

Spatial periodicity in grid cell firing has been interpreted as a neural metric for space providing animals with coordinate system navigating physical and mental spaces. However, the specific computational problem being solved by cells remained elusive. Here, we provide mathematical proof that spatial is only possible solution to sequence code of 2-D trajectories hexagonal pattern most parsimonious such code. We thereby likely teleological cause existence reveal underlying nature global geometric organization maps direct consequence simple local A provides intuitive explanations many previously puzzling experimental observations may transform our thinking about cells.

Language: Английский

Citations

0
The algorithmic agent perspective and computational neuropsychiatry: from etiology to advanced therapy in major depressive disorder DOI Open Access
Giulio Ruffini, Francesca Castaldo, Edmundo Lopez-Sola

et al.

Published: March 14, 2024

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through mechanistic modeling of this disorder. Using the Kolmogorov Theory consciousness (KT), we develop foundational model where algorithmic agents interact with world to maximize an Objective Function evaluating affective \textit{valence}. Depression, defined in context by state persistently low valence, may arise from various factors---including inaccurate models (cognitive biases), dysfunctional (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, map agent brain circuits functional networks, framing etiological routes linking depression biotypes. Finally, explore how stimulation, psychotherapy, plasticity-enhancing compounds such as psychedelics can synergistically repair neural optimize therapies using personalized computational models.

Language: Английский

Citations

2
Top-down attention shifts behavioral and neural event boundaries in narratives with overlapping event scripts DOI

Alexandra De Soares,

Tony Kim,

Franck Mugisho

et al.

Current Biology, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

Citations

2
Spatial periodicity in grid cell firing is explained by a neural sequence code of 2-D trajectories DOI Creative Commons

Rebecca RG,

Giorgio A. Ascoli, Nate Sutton

et al.

eLife, Journal Year: 2024, Volume and Issue: unknown

Published: June 5, 2024

Spatial periodicity in grid cell firing has been interpreted as a neural metric for space providing animals with coordinate system navigating physical and mental spaces. However, the specific computational problem being solved by cells remained elusive. Here, we provide mathematical proof that spatial is only possible solution to sequence code of 2-D trajectories hexagonal pattern most parsimonious such code. We thereby likely teleological cause existence reveal underlying nature global geometric organization maps direct consequence simple local A provides intuitive explanations many previously puzzling experimental observations may transform our thinking about cells.

Language: Английский

Citations

1
A hierarchical active inference model of spatial alternation tasks and the hippocampal-prefrontal circuit DOI Creative Commons
Toon Van de Maele, Bart Dhoedt, Tim Verbelen

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Nov. 15, 2024

Cognitive problem-solving benefits from cognitive maps aiding navigation and planning. Physical space involves hippocampal (HC) allocentric codes, while abstract task engages medial prefrontal cortex (mPFC) task-specific codes. Previous studies show that challenging tasks, like spatial alternation, require integrating these two types of maps. The disruption the HC-mPFC circuit impairs performance. We propose a hierarchical active inference model clarifying how this solves interaction tasks by bridging physical task-space Simulations demonstrate model's dual layers develop effective for space. alternation through reciprocal interactions between layers. Disrupting its communication decision-making, which is consistent with empirical evidence. Additionally, adapts to switching multiple rules, providing mechanistic explanation supports effects disruption. How interact when executing not fully understood. This paper models hippocampal-prefrontal circuits memory-guided taskspace.

Language: Английский

Citations

1
Policy optimization emerges from noisy representation learning DOI Creative Commons
Jonah W. Brenner, Chenguang Li, Gabriel Kreiman

et al.

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

Published: Nov. 3, 2024

A bstract Nervous systems learn representations of the world and policies to act within it. We present a framework that uses reward-dependent noise facilitate policy opti- mization in representation learning networks. These networks balance extracting normative features task-relevant information solve tasks. Moreover, their changes reproduce several experimentally observed shifts neural code during task learning. Our presents biologically plausible mechanism for emergent optimization amid evidence plays vital role governing dynamics. Code is available at: NeuralThermalOptimization.

Language: Английский

Citations

0