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Linear and nonlinear integrate-and-fire neurons driven by synaptic shot noise with reversal potentials

Physical Review E 2024年第2期109卷 024407-024407页

作者： Magnus J. E. Richardson Warwick Mathematics Institute University of Warwick Coventry CV4 7AL United Kingdom

The steady-state firing rate and firing-rate response of the leaky and exponential integrate-and-fire models receiving synaptic shot noise with excitatory and inhibitory reversal potentials is examined. For the particular case where the underlying synaptic conductances are exponentially distributed, it is shown that the master equation for a population of such model neurons can be reduced from an integrodifferential form to a more tractable set of three differential equations. The system is nevertheless more challenging analytically than for current-based synapses: where possible, analytical results are provided with an efficient numerical scheme and code provided for other quantities. The increased tractability of the framework developed supports an ongoing critical comparison between models in which synapses are treated with and without reversal potentials, such as recently in the context of networks with balanced excitatory and inhibitory conductances.

关键词： neuroscience, neural computation & artificial intelligence Integrate-and-fire model Spiking neuron models

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Essay: Exploring the Physics of Basic Medical Research

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Physical Review Letters 2024年第9期132卷 090001-090001页

作者： Vahid Sandoghdar Max Planck Institute for the Science of Light Staudtstr. 2 91058 Erlangen Germany Max-Planck-Zentrum für Physik und Medizin Kußmaulallee 2 91054 Erlangen Germany and Department of Physics Friedrich-Alexander University Erlangen-Nuremberg 91058 Erlangen Germany

The 20th century witnessed the emergence of many paradigm-shifting technologies from the physics community, which have revolutionized medical diagnostics and patient care. However, fundamental medical research has been mostly guided by methods from areas such as cell biology, biochemistry, and genetics, with fairly small contributions from physicists. In this Essay, I outline some key phenomena in the human body that are based on physical principles and yet govern our health over a vast range of length and time scales. I advocate that research in life sciences can greatly benefit from the methodology, know-how, and mindset of the physics community and that the pursuit of basic research in medicine is compatible with the mission of physics.

关键词： Biomolecular & subcellular processes Cellular organization, physiology & dynamics Medical physics & public health neuroscience, neural computation & artificial intelligence

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Topology-dependent coalescence controls scaling exponents in finite networks

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Physical Review Research 2024年第2期6卷 023131-023131页

作者： Roxana Zeraati Victor Buendía Tatiana A. Engel Anna Levina University of Tübingen Tübingen 72076 Germany Max Planck Institute for Biological Cybernetics Tübingen 72076 Germany Princeton Neuroscience Institute Princeton University Princeton New Jersey 08540 USA Cold Spring Harbor Laboratory Cold Spring Harbor New York 11724 USA Bernstein Center for Computational Neuroscience Tübingen Tübingen 72076 Germany

Studies of neural avalanches across different data modalities led to the prominent hypothesis that the brain operates near a critical point. The observed exponents often indicate the mean-field directed-percolation universality class, leading to the fully connected or random network models to study the avalanche dynamics. However, cortical networks have distinct nonrandom features and spatial organization that is known to affect critical exponents. Here we show that distinct empirical exponents arise in networks with different topology and depend on the network size. In particular, we find apparent scale-free behavior with mean-field exponents appearing as quasicritical dynamics in structured networks. This quasicritical dynamics cannot be easily discriminated from an actual critical point in small networks. We find that the local coalescence in activity dynamics can explain the distinct exponents. Therefore, both topology and system size should be considered when assessing criticality from empirical observables.

关键词： Neuronal avalanches neuroscience, neural computation & artificial intelligence Phase transitions Brain network

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Neuronal temporal filters as normal mode extractors

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Physical Review Research 2024年第1期6卷 013111-013111页

作者： Siavash Golkar Jules Berman David Lipshutz Robert Mihai Haret Tim Gollisch Dmitri B. Chklovskii Center for Computational Neuroscience Flatiron Institute and Neuroscience Institute NYU Medical Center New York 10016 New York USA

This article is part of the Physical Review Research collection titled Physics of neuroscience.

关键词： neural encoding Neuronal dynamics neuroscience, neural computation & artificial intelligence

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Unveiling the odor representation in the inner brain of Drosophila through compressed sensing

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Physical Review Research 2024年第2期6卷 023298-023298页

作者： Kiri Choi Won Kyu Kim Changbong Hyeon School of Computational Sciences Korea Institute for Advanced Study Seoul 02455 South Korea Department of Molecular Cellular and Developmental Biology Yale University New Haven 06511 CT United States Quantitative Biology Institute Yale University New Haven 06511 CT United States Swartz Foundation for Theoretical Neuroscience Yale University New Haven 06511 CT United States

The olfaction begins when odorant molecules stimulate a finite number of receptor types in the olfactory neurons. According to the recent connectomics data of the Drosophila olfactory system, the olfactory neural circuit is multilayered and there is an overall dimensionality reduction from the input to output layer. Compressed sensing (CS) is an algorithm that enables the recovery of high-dimensional signals from the data compressed in a lower dimension when the representation of such signals is sufficiently sparse. By analyzing the recent Drosophila connectomics data, we find that the organization of the Drosophila olfactory system effectively satisfies the necessary conditions for CS to work. The neural activity profile of projection neurons (PNs) can be faithfully recovered from a low-dimensional response profile of mushroom body output neurons (MBONs), which can be reconstructed using the electrophysiological recordings of a wide range of odorants. By leveraging the residuals calculated between the measured and the predicted MBON responses, we visualize the perceptual odor space using the residual spectrum and discuss the differentiability of an odor from others. Our study highlights the sparse coding of odor to the receptor space as an essential component for odor identifiability, clarifying the principles underlying the concentration-dependent odor percept. Further, simultaneous exposure of the olfactory system to many different odorants saturates the neural activity profile of PNs, significantly degrading the capacity of signal recovery and resulting in a perceptual state analogous to “olfactory white.” Our study applying CS to the real connectomics data encompassing the neural circuitry from PNs to MBONs via Kenyon cells provides quantitative insights into the neural signal processing and odor representation in the inner brain of Drosophila.

关键词： Biological information processing Biological neural networks neuroscience, neural computation & artificial intelligence Olfaction Sensory processes Biological networks Biological network methods Neuronal network models

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Changes in functional connectivity preserve scale-free neuronal and behavioral dynamics

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Physical Review E 2023年第5期108卷 L052301-L052301页

作者： Anja Rabus Davor Curic Victorita E. Ivan Ingrid M. Esteves Aaron J. Gruber Jörn Davidsen Complexity Science Group Department of Physics and Astronomy University of Calgary Calgary Alberta Canada T2N 1N4 Canadian Centre for Behavioral Neuroscience University of Lethbridge Lethbridge Alberta Canada T1K 3M4 Hotchkiss Brain Institute University of Calgary Calgary Alberta Canada T2N 4N1

Does the brain optimize itself for storage and transmission of information, and if so, how? The critical brain hypothesis is based in statistical physics and posits that the brain self-tunes its dynamics to a critical point or regime to maximize the repertoire of neuronal responses. Yet, the robustness of this regime, especially with respect to changes in the functional connectivity, remains an unsolved fundamental challenge. Here, we show that both scale-free neuronal dynamics and self-similar features of behavioral dynamics persist following significant changes in functional connectivity. Specifically, we find that the psychedelic compound ibogaine that is associated with an altered state of consciousness fundamentally alters the functional connectivity in the retrosplenial cortex of mice. Yet, the scale-free statistics of movement and of neuronal avalanches among behaviorally related neurons remain largely unaltered. This indicates that the propagation of information within biological neural networks is robust to changes in functional organization of subpopulations of neurons, opening up a new perspective on how the adaptive nature of functional networks may lead to optimality of information transmission in the brain.

关键词： Complex systems Dynamic critical phenomena Neuronal avalanches Neuronal dynamics Neuronal network activity neuroscience, neural computation & artificial intelligence Scaling laws of complex systems Brain network Cortical networks

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Optimality Pressures toward Lateralization of Complex Brain Functions

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Physical Review X 2023年第3期13卷 031028-031028页

作者： Luís F. Seoane Departamento de Biología de Sistemas Centro Nacional de Biotecnología (CSIC) C/ Darwin 3 Madrid 28049 Spain Grupo Interdisciplinar de Sistemas Complejos (GISC) Madrid Spain

Most animal brains present two mirror-symmetric sides, but closer inspection reveals a range of asymmetries (in shape and function) that seem more salient in more cognitively complex species. Sustaining symmetric, redundant neural circuitry has associated metabolic costs, but it might aid in implementing computations within noisy environments or with faulty pieces. It has been suggested that the complexity of a computational task might play a role in breaking bilaterally symmetric circuits into fully lateralized ones; however, a rigorous, mathematically grounded theory of how this mechanism might work is missing. Here, we provide such a mathematical framework, starting with the simplest assumptions but extending our results to a comprehensive range of biologically relevant scenarios. We show mathematically that only fully lateralized or bilateral solutions are relevant within our framework (dismissing configurations in which circuits are only partially engaged). We provide maps that show when each configuration is preferred depending on costs, fitness contributed, circuit reliability, and task complexity. We discuss evolutionary paths leading from bilateral to lateralized configurations and other possible outcomes. The implications of these results for evolution, development, and rehabilitation of damaged or aging brains is discussed. Our work constitutes a limit case that should constrain and underlie similar mappings when other aspects (besides task complexity and circuit reliability) are considered.

关键词： Complex systems computational complexity Emergent biological functions from complex interactions Evolutionary & population dynamics Neuroplasticity neuroscience, neural computation & artificial intelligence

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Scaling of whole-brain dynamics reproduced by high-order moments of turbulence indicators

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Physical Review Research 2023年第3期5卷 033183-033183页

作者： Yonatan Sanz Perl Pablo Mininni Enzo Tagliazucchi Morten L. Kringelbach Gustavo Deco Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina Cognitive Neuroscience Center (CNC) Universidad de San Andrés Buenos Aires Argentina Center for Brain and Cognition Computational Neuroscience Group Universitat Pompeu Fabra Barcelona Spain Paris Brain Institute (ICM) Paris France Universidad de Buenos Aires Facultad de Ciencias Exactas y Naturales Departamento de Física Ciudad Universitaria 1428 Buenos Aires Argentina CONICET–Universidad de Buenos Aires Instituto de Física Interdisciplinaria y Aplicada (INFINA) Ciudad Universitaria 1428 Buenos Aires Argentina Latin American Brain Health Institute (BrainLat) Universidad Adolfo Ibáñez 8320000 Santiago Chile Department of Psychiatry University of Oxford Oxford United Kingdom Center for Music in the Brain Department of Clinical Medicine Aarhus University Århus Denmark Life and Health Sciences Research Institute (ICVS) School of Medicine University of Minho Braga Portugal Centre for Eudaimonia and Human Flourishing University of Oxford Oxford United Kingdom Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Institució Catalana de la Recerca i Estudis Avancats (ICREA) Barcelona Spain

We investigate how brain activity can be supported by a turbulent regime based on the deviations of a self-similar scaling of high-order structure functions within the phenomenological Kolmogorov's theory. By analyzing a large neuroimaging data set, we establish the relationship between scaling exponents and their order, showing that brain activity has more than one invariant scale, and thus orders higher than 2 are needed to accurately describe its underlying statistical properties. Furthermore, we build whole-brain models of coupled oscillators to show that high-order information allows for a better description of the brain's empirical information transmission and reactivity.

关键词： neuroscience, neural computation & artificial intelligence Nonequilibrium & irreversible thermodynamics Turbulent mixing Brain Mammals

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Intrinsic Motivation in Dynamical Control Systems

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PRX Life 2024年第3期2卷 033009页

作者： Stas Tiomkin Ilya Nemenman Daniel Polani Naftali Tishby Computer Engineering Department Charles W. Davidson College of Engineering Department of Biology Department of Physics and Initiative in Theory and Modeling of Living Systems Adaptive Systems Research Group The Rachel and Selim Benin School of Computer Science and Engineering and Edmond and Lilly Safra Center for Brain Sciences (ELSC)

Biological systems often choose actions without an explicit reward signal, a phenomenon known as intrinsic motivation. The computational principles underlying this behavior remain poorly understood. In this study, we investigate an information-theoretic approach to intrinsic motivation, based on maximizing an agent's empowerment (the mutual information between its past actions and future states). We show that this approach generalizes previous attempts to formalize intrinsic motivation, and we provide a computationally efficient algorithm for computing the necessary quantities. We test our approach on several benchmark control problems, and we explain its success in guiding intrinsically motivated behaviors by relating our information-theoretic control function to fundamental properties of the dynamical system representing the combined agent-environment system. This opens the door for designing practical artificial, intrinsically motivated controllers and for linking animal behaviors to their dynamical properties.

关键词： Engineering Information & communication theory neuroscience, neural computation & artificial intelligence Dynamical systems Machine learning

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One nose but two nostrils: Learning to align with sparse connections between two olfactory cortices

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PRX Life 2024年第4期2卷 043016-043016页

作者： Bo Liu Shanshan Qin Venkatesh N. Murthy Yuhai Tu Center for Brain Science and Department of Molecular and Cellular Biology Harvard University Cambridge Massachusetts 02138 USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge Massachusetts 02138 USA John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA Present address: Center for Computational Neuroscience Flatiron Institute New York New York 10010 USA. IBM Thomas J. Watson Research Center Yorktown Heights New York 10598 USA

The integration of neural representations in the two brain hemispheres has been studied extensively in vision, audition, and somatosensation, but less so in olfaction. Recent experiments have revealed that odor responses in cortical neurons driven by separate stimulation of the two nostrils are highly correlated. This bilateral alignment points to structured, nonrandom interhemispheric connections, but how this structure leads to alignment is unclear. Here, we hypothesized that continuous exposure to environmental odors shapes these projections and modeled it as online learning with a local Hebbian rule. We found that Hebbian learning with sparse connections achieves bilateral alignment, exhibiting a linear tradeoff between speed and accuracy (lower learning rate leads to higher alignment level but slower convergence). Furthermore, we identified an inverse scaling relationship between the number of cortical neurons and the interhemispheric projection density required for desired alignment accuracy. Thus, more cortical neurons allow sparser interhemispheric projections, which was explained analytically. We next compared the alignment performance of local Hebbian rule and the global stochastic gradient descent (SGD) learning rule used in artificial neural networks. We found that although SGD leads to the same alignment accuracy with a slightly reduced sparsity, the same inverse scaling relation holds. Our analysis showed that their similar performance originates from the fact that the update vectors of the two learning rules align with each other throughout the entire learning process. Our work suggests that a biologically plausible mechanism with sparse connections suffices for correlated bilateral responses. The quantitative comparison between the local Hebbian rule and the global SGD rule may inspire efficient sparse local learning algorithms for more complex problems.

关键词： Biological neural networks Learning neural basis of learning & memory Neuronal network activity neuroscience, neural computation & artificial intelligence Sensory processes

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