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The detailed functioning of the human brain is still poorly understood. Brain simulations are a well-established way to complement experimental research, but must contend with the computational demands of the approximately $10^{11}$ neurons…

分布式、并行与集群计算 · 计算机科学 2025-10-01 Fabian Czappa , Marvin Kaster , Felix Wolf

Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine…

神经元与认知 · 定量生物学 2018-01-08 David Kappel , Robert Legenstein , Stefan Habenschuss , Michael Hsieh , Wolfgang Maass

Functionals are an important research subject in Mathematics and Computer Science as well as a challenge in Information Technologies where the current programming paradigm states that only symbolic computations are possible on higher order…

逻辑 · 数学 2018-09-13 Stanislaw Ambroszkiewicz

The relation between large-scale brain structure and function is an outstanding open problem in neuroscience. We approach this problem by studying the dynamical regime under which realistic spatio-temporal patterns of brain activity emerge…

神经元与认知 · 定量生物学 2014-05-27 Ariel Haimovici , Enzo Tagliazucchi , Pablo Balenzuela , Dante R. Chialvo

The human brain is a complex system, and understanding its mechanisms has been a long-standing challenge in neuroscience. The study of the functional connectome, which maps the functional connections between different brain regions, has…

神经与进化计算 · 计算机科学 2025-04-14 Tananun Songdechakraiwut , Yutong Wu

Fifty years ago, John von Neumann compared the architecture of the brain with that of computers that he invented and which is still in use today. In those days, the organisation of computers was based on concepts of brain organisation.…

神经元与认知 · 定量生物学 2008-02-28 Marcus Kaiser

The brain's complex functionality emerges from network interactions that go beyond dyadic connections, with higher-order interactions significantly contributing to this complexity. One method of capturing higher-order interactions is…

神经元与认知 · 定量生物学 2024-06-11 Behdad Khodabandehloo , Payam Jannatdoust , Babak Nadjar Araabi

Volumetric brain reconstructions provide an unprecedented opportunity to gain insights into the complex connectivity patterns of neurons in an increasing number of organisms. Here, we model and quantify the complexity of the resulting…

神经元与认知 · 定量生物学 2024-05-13 Anastasiya Salova , István A. Kovács

In this essay we elaborate on recent evidence demonstrating the presence of a second order phase transition in human brain dynamics and discuss its consequences for theoretical approaches to brain function. We review early evidence of…

神经元与认知 · 定量生物学 2015-06-12 Enzo Tagliazucchi , Dante R. Chialvo

In order to understand the complex cognitive functions of the human brain, it is essential to study the structural connectome, i.e., the wiring of different brain regions to each other through axonal pathways. However, the high degree of…

神经元与认知 · 定量生物学 2021-01-25 Anand Pathak , Shakti N. Menon , Sitabhra Sinha

We revisit the planning problem in the blocks world, and we implement a known heuristic for this task. Importantly, our implementation is biologically plausible, in the sense that it is carried out exclusively through the spiking of…

The contribution of structural connectivity to functional brain states remains poorly understood. We present a mathematical and computational study suited to assess the structure--function issue, treating a system of Jansen--Rit neural-mass…

神经元与认知 · 定量生物学 2020-07-10 Michael Forrester , Stephen Coombes , Jonathan J. Crofts , Stamatios N. Sotiropoulos , Reuben D. O'Dea

The human structural connectome has a complex internal community organization, characterized by a high degree of overlap and related to functional and cognitive phenomena. We explored connectivity properties in connectome networks and…

无序系统与神经网络 · 物理学 2025-02-24 V. Tiselko , O. Dogonasheva , A. Myshkin , O. Valba

Recurrent Neural Network models have elucidated the interplay between structure and dynamics in biological neural networks, particularly the emergence of irregular and rhythmic activities in cortex. However, most studies have focused on…

神经元与认知 · 定量生物学 2025-09-04 Nimrod Sherf , Xaq Pitkow , Krešimir Josić , Kevin E. Bassler

Functional networks provide a topological description of activity patterns in the brain, as they stem from the propagation of neural activity on the underlying anatomical or structural network of synaptic connections. This latter is well…

无序系统与神经网络 · 物理学 2021-02-11 Ali Safari , Paolo Moretti , Ibai Diez , Jesus M. Cortes , Miguel Ángel Muñoz

The foundation of cognitive flexibility and higher-order intelligence lies in the functional structure and activity of brain networks, which can be dynamically configured by both external environments and internal states. However, decoding…

神经元与认知 · 定量生物学 2026-04-28 Binghao Yang , Guangzong Chen

Statistical mechanics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism and phenomenology percolate throughout biological processes and set limits to effective computation. Under…

神经元与认知 · 定量生物学 2021-02-03 Luís F Seoane

Several efforts are currently underway to decipher the connectome or parts thereof in a variety of organisms. Ascertaining the detailed physiological properties of all the neurons in these connectomes, however, is out of the scope of such…

神经元与认知 · 定量生物学 2014-04-03 Venkatakrishnan Ramaswamy , Arunava Banerjee

The brain can be considered as a system that dynamically optimizes the structure of anatomical connections based on the efficiency requirements of functional connectivity. To illustrate the power of this principle in organizing the…

神经元与认知 · 定量生物学 2024-02-07 Carlos Calvo Tapia , Valeriy A. Makarov Slizneva , Cees van Leeuwen

The human connectome has been widely studied over the past decade. A principal finding is that it can be decomposed into communities of densely interconnected brain regions. This result, however, may be limited methodologically. Past…

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