Information decomposition and the informational architecture of the brain

Trends in Cognitive Sciences, Год журнала: 2024, Номер 28(4), С. 352 - 368

Опубликована: Янв. 9, 2024

To explain how the brain orchestrates information-processing for cognition, we must understand information itself. Importantly, is not a monolithic entity. Information decomposition techniques provide way to split into its constituent elements: unique, redundant, and synergistic information. We review disentangling redundant interactions redefining our understanding of integrative function neural organisation. navigates trade-offs between redundancy synergy, converging evidence integrating structural, molecular, functional underpinnings synergy redundancy; their roles in cognition computation; they might arise over evolution development. Overall, provides guiding principle informational architecture cognition.

Язык: Английский

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The Entangled Brain

Journal of Cognitive Neuroscience, Год журнала: 2022, Номер 35(3), С. 349 - 360

Опубликована: Авг. 25, 2022

Abstract The Entangled Brain (Pessoa, L., 2002. MIT Press) promotes the idea that we need to understand brain as a complex, entangled system. Why does complex systems perspective, one entails emergent properties, matter for science? In fact, many neuroscientists consider these ideas distraction. We discuss three principles of organization inform question interactional complexity brain: (1) massive combinatorial anatomical connectivity; (2) highly distributed functional coordination; and (3) networks/circuits units. To motivate challenges mapping structure function, neural circuits illustrating high typical in brain. potential avenues testing network-level including those relying on computations across multiple regions. implications science, characterize decentralized heterarchical anatomical–functional organization. view advocated has important causation, too, because traditional accounts causality provide poor candidates explanation interactionally like given distributed, mutual, reciprocal nature interactions. Ultimately, make progress understanding how supports mental functions, dissolve boundaries within brain—those suggested be associated with perception, cognition, action, emotion, motivation—as well outside brain, bring down walls between biology, psychology, mathematics, computer philosophy, so on.

Язык: Английский

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Downstream network transformations dissociate neural activity from causal functional contributions

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 24, 2024

Abstract Neuroscientists rely on distributed spatio-temporal patterns of neural activity to understand how units contribute cognitive functions and behavior. However, the extent which reliably indicates a unit's causal contribution behavior is not well understood. To address this issue, we provide systematic multi-site perturbation framework that captures time-varying contributions elements collectively produced outcome. Applying our intuitive toy examples artificial networks revealed recorded may be generally informative their due transformations within network. Overall, findings emphasize limitations inferring mechanisms from activities offer rigorous lesioning for elucidating contributions.

Язык: Английский

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A new approach for estimating effective connectivity from activity in neural networks

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Фев. 6, 2024

Abstract Inferring and understanding the underlying connectivity structure of a system solely from observed activity its constituent components is challenge in many areas science. In neuroscience, techniques for estimating are paramount when attempting to understand network neural systems their recorded patterns. To date, no universally accepted method exists inference effective connectivity, which describes how node mechanistically affects other nodes. Here, focussing on purely excitatory networks small intermediate size continuous dynamics, we provide systematic comparison different approaches connectivity. Starting with Hopf neuron model conjunction known ground truth structural reconstruct system’s matrix using variety algorithms. We show that, sparse non-linear delays, combining lagged-cross-correlation (LCC) approach recently published derivative-based covariance analysis provides most reliable estimation matrix. also that linear networks, LCC has comparable performance based transfer entropy, at drastically lower computational cost. highlight works best decreases larger less networks. Applying dynamics without time find it does not outperform methods. Employing model, then use estimated as basis forward simulation order recreate under certain conditions, method, LCC, results higher trace-to-trace correlations than methods noise-driven systems. Finally, apply empirical biological data. subset nervous nematode C. Elegans . computationally simple performs better another published, more expensive reservoir computing-based method. Our comparatively can be used reliably estimate directed presence spatio-temporal delays noise. concrete suggestions scenario common research, where only neuronal set neurons known.

Язык: Английский

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Dynamics of specialization in neural modules under resource constraints

Nature Communications, Год журнала: 2025, Номер 16(1)

Опубликована: Янв. 2, 2025

Язык: Английский

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Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 13, 2025

Язык: Английский

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Hybrid Neural Networks for Volitional Movement

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Фев. 23, 2025

Abstract Massive interconnectivity in large-scale neural networks is the key feature underlying their powerful and complex functionality. We have developed hybrid network (HNN) models that allow us to find statistical structure this connectivity. Describing critical for understanding biological artificial networks. The HNNs are composed of neurons, a subset which trained reproduce responses individual neurons recorded experimentally. experimentally observed firing rates came from populations motor cortices monkeys performing reaching task. After training, these (recurrent spiking) underwent same state transitions as those empirical data, result helps resolve long-standing question prescribed vs ongoing control volitional movement. Because all aspects exposed, we were able analyze dynamic statistics connections between neurons. Our results show dynamics extrinsic input changed connectivity cause transitions. Two processes at synaptic level recognized: one many different contributed buildup membrane potential another more specific triggered an action potential. facilitate modeling realistic neuron-neuron provide foundational descriptions

Язык: Английский

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A General Framework for Characterizing Optimal Communication in Brain Networks

Опубликована: Март 18, 2025

Communication in brain networks is the foundation of cognitive function and behavior. A multitude evolutionary pressures, including minimization metabolic costs while maximizing communication efficiency, contribute to shaping structure dynamics these networks. However, how efficiency characterized depends on assumed model dynamics. Traditional models include shortest path signaling, random walker navigation, broadcasting, diffusive processes. Yet, a general model-agnostic framework for characterizing optimal neural remains be established.Our study addresses this challenge by assigning through game theory, based combination structural data from human cortical with computational We quantified exact influence exerted each node over every other using an exhaustive multi-site virtual lesioning scheme, creating maps various These descriptions show patterns unfold given network if regions maximize their one another. By comparing large variety models, we found that most closely resembles broadcasting which leverage multiple parallel channels information dissemination. Moreover, influential within cortex are formed its rich-club. exploit topological vantage point across numerous pathways, thereby significantly enhancing effective reach even when anatomical connections weak.Our work provides rigorous versatile reveals features underlying communication.

Язык: Английский

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A general framework for characterizing optimal communication in brain networks

eLife, Год журнала: 2025, Номер 13

Опубликована: Март 25, 2025

Efficient communication in brain networks is foundational for cognitive function and behavior. However, how efficiency defined depends on the assumed model of signaling dynamics, e.g., shortest path signaling, random walker navigation, broadcasting, diffusive processes. Thus, a general model-agnostic framework characterizing optimal neural needed. We address this challenge by assigning through virtual multi-site lesioning regime combined with game theory, applied to large-scale models human dynamics. Our quantifies exact influence each node exerts over every other, generating maps given underlying These descriptions reveal patterns unfold if regions are set maximize their one another. Comparing these variety showed that closely resembles broadcasting which leverage multiple parallel channels information dissemination. Moreover, we found brain’s most influential its rich-club, exploiting topological vantage point across numerous pathways enhance reach even connections weak. Altogether, our work provides rigorous versatile communication, uncovers regions, features influence.

Язык: Английский

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Causal Influences Decouple From Their Underlying Network Structure In Echo State Networks

2022 International Joint Conference on Neural Networks (IJCNN), Год журнала: 2022, Номер unknown, С. 1 - 8

Опубликована: Июль 18, 2022

Echo State Networks (ESN) are versatile recurrent neural network models in which the hidden layer remains unaltered during training. Interactions among nodes of this static backbone (the structure) produce diverse representations (i.e., dynamics) given stimuli that harnessed by a read-out mechanism to perform computations needed for solving task behavior). Moreover, ESNs accessible neuronal circuits, since they relatively inexpensive train. Therefore, have become attractive neuroscientists studying relationship between structure, function, and behavior. For instance, it is not yet clear how distinctive connectivity patterns brain networks (structure) support effective interactions their (dynamics) these give rise computation (behavior). To address question, we employed an ESN with biologically inspired structure used systematic multi-site lesioning framework quantify causal contribution each node network's output, thus providing link We then focused on structure-function decomposed influence all other nodes, using same framework. found properly engineered interact largely irrespective underlying structure. However, topology where ESN's leakage rate non-optimal dynamics diminished, determine interactions. Our results suggest relations can be into two components, direct indirect The former based influences relying structural connections. latter describe communication any through intermediate nodes. These widely distributed may crucially contribute efficient performance ESNs.

Язык: Английский

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