1
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Hartl B, Risi S, Levin M. Evolutionary Implications of Self-Assembling Cybernetic Materials with Collective Problem-Solving Intelligence at Multiple Scales. ENTROPY (BASEL, SWITZERLAND) 2024; 26:532. [PMID: 39056895 PMCID: PMC11275831 DOI: 10.3390/e26070532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 06/10/2024] [Accepted: 06/14/2024] [Indexed: 07/28/2024]
Abstract
In recent years, the scientific community has increasingly recognized the complex multi-scale competency architecture (MCA) of biology, comprising nested layers of active homeostatic agents, each forming the self-orchestrated substrate for the layer above, and, in turn, relying on the structural and functional plasticity of the layer(s) below. The question of how natural selection could give rise to this MCA has been the focus of intense research. Here, we instead investigate the effects of such decision-making competencies of MCA agential components on the process of evolution itself, using in silico neuroevolution experiments of simulated, minimal developmental biology. We specifically model the process of morphogenesis with neural cellular automata (NCAs) and utilize an evolutionary algorithm to optimize the corresponding model parameters with the objective of collectively self-assembling a two-dimensional spatial target pattern (reliable morphogenesis). Furthermore, we systematically vary the accuracy with which the uni-cellular agents of an NCA can regulate their cell states (simulating stochastic processes and noise during development). This allows us to continuously scale the agents' competency levels from a direct encoding scheme (no competency) to an MCA (with perfect reliability in cell decision executions). We demonstrate that an evolutionary process proceeds much more rapidly when evolving the functional parameters of an MCA compared to evolving the target pattern directly. Moreover, the evolved MCAs generalize well toward system parameter changes and even modified objective functions of the evolutionary process. Thus, the adaptive problem-solving competencies of the agential parts in our NCA-based in silico morphogenesis model strongly affect the evolutionary process, suggesting significant functional implications of the near-ubiquitous competency seen in living matter.
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Affiliation(s)
- Benedikt Hartl
- Allen Discovery Center, Tufts University, Medford, MA 02155, USA;
- Institute for Theoretical Physics, Center for Computational Materials Science (CMS), TU Wien, 1040 Wien, Austria
| | - Sebastian Risi
- Digital Design, IT University of Copenhagen, 2300 Copenhagen, Denmark;
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, MA 02155, USA;
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
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2
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Rhamidda SL, Girardi-Schappo M, Kinouchi O. Optimal input reverberation and homeostatic self-organization toward the edge of synchronization. CHAOS (WOODBURY, N.Y.) 2024; 34:053127. [PMID: 38767461 DOI: 10.1063/5.0202743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/25/2024] [Indexed: 05/22/2024]
Abstract
Transient or partial synchronization can be used to do computations, although a fully synchronized network is sometimes related to the onset of epileptic seizures. Here, we propose a homeostatic mechanism that is capable of maintaining a neuronal network at the edge of a synchronization transition, thereby avoiding the harmful consequences of a fully synchronized network. We model neurons by maps since they are dynamically richer than integrate-and-fire models and more computationally efficient than conductance-based approaches. We first describe the synchronization phase transition of a dense network of neurons with different tonic spiking frequencies coupled by gap junctions. We show that at the transition critical point, inputs optimally reverberate through the network activity through transient synchronization. Then, we introduce a local homeostatic dynamic in the synaptic coupling and show that it produces a robust self-organization toward the edge of this phase transition. We discuss the potential biological consequences of this self-organization process, such as its relation to the Brain Criticality hypothesis, its input processing capacity, and how its malfunction could lead to pathological synchronization and the onset of seizure-like activity.
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Affiliation(s)
- Sue L Rhamidda
- Departamento de Física, FFCLRP, Universidade de São Paulo, Ribeirão Preto, SP 14040-901, Brazil
| | - Mauricio Girardi-Schappo
- Departamento de Física, Universidade Federal de Santa Catarina, Florianópolis, SC 88040-900, Brazil
| | - Osame Kinouchi
- Departamento de Física, FFCLRP, Universidade de São Paulo, Ribeirão Preto, SP 14040-901, Brazil
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3
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van Nifterick AM, Scheijbeler EP, Gouw AA, de Haan W, Stam CJ. Local signal variability and functional connectivity: Sensitive measures of the excitation-inhibition ratio? Cogn Neurodyn 2024; 18:519-537. [PMID: 38699618 PMCID: PMC11061092 DOI: 10.1007/s11571-023-10003-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/08/2023] [Accepted: 08/13/2023] [Indexed: 05/05/2024] Open
Abstract
A novel network version of permutation entropy, the inverted joint permutation entropy (JPEinv), holds potential as non-invasive biomarker of abnormal excitation-inhibition (E-I) ratio in Alzheimer's disease (AD). In this computational modelling study, we test the hypotheses that this metric, and related measures of signal variability and functional connectivity, are sensitive to altered E-I ratios. The E-I ratio in each neural mass of a whole-brain computational network model was systematically varied. We evaluated whether JPEinv, local signal variability (by permutation entropy) and functional connectivity (by weighted symbolic mutual information (wsMI)) were related to E-I ratio, on whole-brain and regional level. The hub disruption index can identify regions primarily affected in terms of functional connectivity strength (or: degree) by the altered E-I ratios. Analyses were performed for a range of coupling strengths, filter and time-delay settings. On whole-brain level, higher E-I ratios were associated with higher functional connectivity (by JPEinv and wsMI) and lower local signal variability. These relationships were nonlinear and depended on the coupling strength, filter and time-delay settings. On regional level, hub-like regions showed a selective decrease in functional degree (by JPEinv and wsMI) upon a lower E-I ratio, and non-hub-like regions showed a selective increase in degree upon a higher E-I ratio. These results suggest that abnormal functional connectivity and signal variability, as previously reported in patients across the AD continuum, can inform us about altered E-I ratios. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-10003-x.
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Affiliation(s)
- Anne M. van Nifterick
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Elliz P. Scheijbeler
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Alida A. Gouw
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Willem de Haan
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
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4
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Joo P, Kim M, Kish B, Nair VV, Tong Y, Liu Z, O'Brien ARW, Harte SE, Harris RE, Lee U, Wang Y. Brain network hypersensitivity underlies pain crises in sickle cell disease. Sci Rep 2024; 14:7315. [PMID: 38538687 PMCID: PMC10973361 DOI: 10.1038/s41598-024-57473-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/18/2024] [Indexed: 04/04/2024] Open
Abstract
Sickle cell disease (SCD) is a genetic disorder causing painful and unpredictable Vaso-occlusive crises (VOCs) through blood vessel blockages. In this study, we propose explosive synchronization (ES) as a novel approach to comprehend the hypersensitivity and occurrence of VOCs in the SCD brain network. We hypothesized that the accumulated disruptions in the brain network induced by SCD might lead to strengthened ES and hypersensitivity. We explored ES's relationship with patient reported outcome measures (PROMs) as well as VOCs by analyzing EEG data from 25 SCD patients and 18 matched controls. SCD patients exhibited lower alpha frequency than controls. SCD patients showed correlation between frequency disassortativity (FDA), an ES condition, and three important PROMs. Furthermore, stronger FDA was observed in SCD patients with a higher frequency of VOCs and EEG recording near VOC. We also conducted computational modeling on SCD brain network to study FDA's role in network sensitivity. Our model demonstrated that a stronger FDA could be linked to increased sensitivity and frequency of VOCs. This study establishes connections between SCD pain and the universal network mechanism, ES, offering a strong theoretical foundation. This understanding will aid predicting VOCs and refining pain management for SCD patients.
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Affiliation(s)
- Pangyu Joo
- Department of Anesthesiology, Center for Consciousness Science, Center for the Study of Complex Systems, Michigan Psychedelic Center, University of Michigan, Arbor Lakes Building 1 Suite 2200, 4251 Plymouth Road, Ann Arbor, MI, 48105, USA
| | - Minkyung Kim
- Department of Anesthesiology, Center for Consciousness Science, Center for the Study of Complex Systems, Michigan Psychedelic Center, University of Michigan, Arbor Lakes Building 1 Suite 2200, 4251 Plymouth Road, Ann Arbor, MI, 48105, USA
| | - Brianna Kish
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | | | - Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Ziyue Liu
- Indiana Center for Musculoskeletal Health, Indiana University, Indianapolis, IN, USA
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew R W O'Brien
- Division of Hematology/Oncology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Steven E Harte
- Department of Anesthesiology, Chronic Pain and Fatigue Research Center, University of Michigan, Ann Arbor, MI, USA
| | - Richard E Harris
- Department of Anesthesiology, Chronic Pain and Fatigue Research Center, University of Michigan, Ann Arbor, MI, USA
- Susan Samueli Integrative Health Institute, and Department of Anesthesiology and Perioperative Care, School of Medicine, University of California at Irvine, Irvine, CA, USA
| | - UnCheol Lee
- Department of Anesthesiology, Center for Consciousness Science, Center for the Study of Complex Systems, Michigan Psychedelic Center, University of Michigan, Arbor Lakes Building 1 Suite 2200, 4251 Plymouth Road, Ann Arbor, MI, 48105, USA.
| | - Ying Wang
- Division of Hematology/Oncology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Anesthesia, Stark Neurosciences Research Institute, Indiana University School of Medicine, Stark Neuroscience Building, Rm# 514E, 320 West 15th Street, Indianapolis, IN, 46202, USA.
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5
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Morrell MC, Nemenman I, Sederberg A. Neural criticality from effective latent variables. eLife 2024; 12:RP89337. [PMID: 38470471 PMCID: PMC10957169 DOI: 10.7554/elife.89337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024] Open
Abstract
Observations of power laws in neural activity data have raised the intriguing notion that brains may operate in a critical state. One example of this critical state is 'avalanche criticality', which has been observed in various systems, including cultured neurons, zebrafish, rodent cortex, and human EEG. More recently, power laws were also observed in neural populations in the mouse under an activity coarse-graining procedure, and they were explained as a consequence of the neural activity being coupled to multiple latent dynamical variables. An intriguing possibility is that avalanche criticality emerges due to a similar mechanism. Here, we determine the conditions under which latent dynamical variables give rise to avalanche criticality. We find that populations coupled to multiple latent variables produce critical behavior across a broader parameter range than those coupled to a single, quasi-static latent variable, but in both cases, avalanche criticality is observed without fine-tuning of model parameters. We identify two regimes of avalanches, both critical but differing in the amount of information carried about the latent variable. Our results suggest that avalanche criticality arises in neural systems in which activity is effectively modeled as a population driven by a few dynamical variables and these variables can be inferred from the population activity.
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Affiliation(s)
- Mia C Morrell
- Department of Physics, New York UniversityNew YorkUnited States
| | - Ilya Nemenman
- Department of Physics, Department of Biology, Initiative in Theory and Modeling of Living Systems, Emory UniversityAtlantaUnited States
| | - Audrey Sederberg
- Department of Neuroscience, University of Minnesota Medical SchoolMinneapolisUnited States
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6
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Bai X, Yu C, Zhai J. Topological data analysis of the firings of a network of stochastic spiking neurons. Front Neural Circuits 2024; 17:1308629. [PMID: 38239606 PMCID: PMC10794443 DOI: 10.3389/fncir.2023.1308629] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 12/06/2023] [Indexed: 01/22/2024] Open
Abstract
Topological data analysis is becoming more and more popular in recent years. It has found various applications in many different fields, for its convenience in analyzing and understanding the structure and dynamic of complex systems. We used topological data analysis to analyze the firings of a network of stochastic spiking neurons, which can be in a sub-critical, critical, or super-critical state depending on the value of the control parameter. We calculated several topological features regarding Betti curves and then analyzed the behaviors of these features, using them as inputs for machine learning to discriminate the three states of the network.
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Affiliation(s)
| | - Chaojun Yu
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
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7
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van der Molen T, Spaeth A, Chini M, Bartram J, Dendukuri A, Zhang Z, Bhaskaran-Nair K, Blauvelt LJ, Petzold LR, Hansma PK, Teodorescu M, Hierlemann A, Hengen KB, Hanganu-Opatz IL, Kosik KS, Sharf T. Protosequences in human cortical organoids model intrinsic states in the developing cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.29.573646. [PMID: 38234832 PMCID: PMC10793448 DOI: 10.1101/2023.12.29.573646] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Neuronal firing sequences are thought to be the basic building blocks of neural coding and information broadcasting within the brain. However, when sequences emerge during neurodevelopment remains unknown. We demonstrate that structured firing sequences are present in spontaneous activity of human brain organoids and ex vivo neonatal brain slices from the murine somatosensory cortex. We observed a balance between temporally rigid and flexible firing patterns that are emergent phenomena in human brain organoids and early postnatal murine somatosensory cortex, but not in primary dissociated cortical cultures. Our findings suggest that temporal sequences do not arise in an experience-dependent manner, but are rather constrained by an innate preconfigured architecture established during neurogenesis. These findings highlight the potential for brain organoids to further explore how exogenous inputs can be used to refine neuronal circuits and enable new studies into the genetic mechanisms that govern assembly of functional circuitry during early human brain development.
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Affiliation(s)
- Tjitse van der Molen
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA 93106, USA
- Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA 93106, USA
| | - Alex Spaeth
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Mattia Chini
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Julian Bartram
- Department of Biosystems Science and Engineering, ETH Zürich, Klingelbergstrasse 48, 4056 Basel, Switzerland
| | - Aditya Dendukuri
- Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA 93106, USA
| | - Zongren Zhang
- Department of Physics, University of California Santa Barbara, Santa Barbara, CA 93106
| | - Kiran Bhaskaran-Nair
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Lon J. Blauvelt
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Linda R. Petzold
- Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA 93106, USA
| | - Paul K. Hansma
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA 93106, USA
- Department of Physics, University of California Santa Barbara, Santa Barbara, CA 93106
| | - Mircea Teodorescu
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Andreas Hierlemann
- Department of Biosystems Science and Engineering, ETH Zürich, Klingelbergstrasse 48, 4056 Basel, Switzerland
| | - Keith B. Hengen
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Ileana L. Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Kenneth S. Kosik
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA 93106, USA
- Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA 93106, USA
| | - Tal Sharf
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Institute for the Biology of Stem Cells, University of California Santa Cruz, Santa Cruz, CA 95064, USA
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8
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Wang SH, Siebenhühner F, Arnulfo G, Myrov V, Nobili L, Breakspear M, Palva S, Palva JM. Critical-like Brain Dynamics in a Continuum from Second- to First-Order Phase Transition. J Neurosci 2023; 43:7642-7656. [PMID: 37816599 PMCID: PMC10634584 DOI: 10.1523/jneurosci.1889-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 06/07/2023] [Accepted: 09/25/2023] [Indexed: 10/12/2023] Open
Abstract
The classic brain criticality hypothesis postulates that the brain benefits from operating near a continuous second-order phase transition. Slow feedback regulation of neuronal activity could, however, lead to a discontinuous first-order transition and thereby bistable activity. Observations of bistability in awake brain activity have nonetheless remained scarce and its functional significance unclear. Moreover, there is no empirical evidence to support the hypothesis that the human brain could flexibly operate near either a first- or second-order phase transition despite such a continuum being common in models. Here, using computational modeling, we found bistable synchronization dynamics to emerge through elevated positive feedback and occur exclusively in a regimen of critical-like dynamics. We then assessed bistability in vivo with resting-state MEG in healthy adults (7 females, 11 males) and stereo-electroencephalography in epilepsy patients (28 females, 36 males). This analysis revealed that a large fraction of the neocortices exhibited varying degrees of bistability in neuronal oscillations from 3 to 200 Hz. In line with our modeling results, the neuronal bistability was positively correlated with classic assessment of brain criticality across narrow-band frequencies. Excessive bistability was predictive of epileptic pathophysiology in the patients, whereas moderate bistability was positively correlated with task performance in the healthy subjects. These empirical findings thus reveal the human brain as a one-of-a-kind complex system that exhibits critical-like dynamics in a continuum between continuous and discontinuous phase transitions.SIGNIFICANCE STATEMENT In the model, while synchrony per se was controlled by connectivity, increasing positive local feedback led to gradually emerging bistable synchrony with scale-free dynamics, suggesting a continuum between second- and first-order phase transitions in synchrony dynamics inside a critical-like regimen. In resting-state MEG and SEEG, bistability of ongoing neuronal oscillations was pervasive across brain areas and frequency bands and was observed only with concurring critical-like dynamics as the modeling predicted. As evidence for functional relevance, moderate bistability was positively correlated with executive functioning in the healthy subjects, and excessive bistability was associated with epileptic pathophysiology. These findings show that critical-like neuronal dynamics in vivo involves both continuous and discontinuous phase transitions in a frequency-, neuroanatomy-, and state-dependent manner.
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Affiliation(s)
- Sheng H Wang
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Doctoral Programme Brain & Mind, University of Helsinki, 00014 Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, 00290 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
| | - Felix Siebenhühner
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, 00290 Helsinki, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa, 16136 Genoa, Italy
| | - Vladislav Myrov
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
| | - Lino Nobili
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Children's Sciences, University of Genoa, 16136 Genoa, Italy
- Child Neuropsychiatry Unit, Istituto Di Ricovero e Cura a Carattere Scientifico Istituto Giannina Gaslini, 16147 Genoa, Italy
- Centre of Epilepsy Surgery "C. Munari," Department of Neuroscience, Niguarda Hospital, 20162 Milan, Italy
| | - Michael Breakspear
- College of Engineering, Science and Environment, College of Health and Medicine, University of Newcastle, Callaghan, 2308 Australia
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, Glasgow G12 8QB, United Kingdom
| | - J Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
- Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, Glasgow G12 8QB, United Kingdom
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9
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Torres F, Basaran AC, Schuller IK. Thermal Management in Neuromorphic Materials, Devices, and Networks. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205098. [PMID: 36067752 DOI: 10.1002/adma.202205098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/30/2022] [Indexed: 06/15/2023]
Abstract
Machine learning has experienced unprecedented growth in recent years, often referred to as an "artificial intelligence revolution." Biological systems inspire the fundamental approach for this new computing paradigm: using neural networks to classify large amounts of data into sorting categories. Current machine-learning schemes implement simulated neurons and synapses on standard computers based on a von Neumann architecture. This approach is inefficient in energy consumption, and thermal management, motivating the search for hardware-based systems that imitate the brain. Here, the present state of thermal management of neuromorphic computing technology and the challenges and opportunities of the energy-efficient implementation of neuromorphic devices are considered. The main features of brain-inspired computing and quantum materials for implementing neuromorphic devices are briefly described, the brain criticality and resistive switching-based neuromorphic devices are discussed, the energy and electrical considerations for spiking-based computation are presented, the fundamental features of the brain's thermal regulation are addressed, the physical mechanisms for thermal management and thermoelectric control of materials and neuromorphic devices are analyzed, and challenges and new avenues for implementing energy-efficient computing are described.
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Affiliation(s)
- Felipe Torres
- Physics Department, Faculty of Science, University of Chile, 653, Santiago, 7800024, Chile
- Center of Nanoscience and Nanotechnology (CEDENNA), Av. Ecuador 3493, Santiago, 9170124, Chile
| | - Ali C Basaran
- Department of Physics and Center for Advanced Nanoscience, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ivan K Schuller
- Department of Physics and Center for Advanced Nanoscience, University of California San Diego, La Jolla, CA, 92093, USA
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10
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Fuscà M, Siebenhühner F, Wang SH, Myrov V, Arnulfo G, Nobili L, Palva JM, Palva S. Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data. Nat Commun 2023; 14:4736. [PMID: 37550300 PMCID: PMC10406818 DOI: 10.1038/s41467-023-40056-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 07/10/2023] [Indexed: 08/09/2023] Open
Abstract
Neuronal oscillations and their synchronization between brain areas are fundamental for healthy brain function. Yet, synchronization levels exhibit large inter-individual variability that is associated with behavioral variability. We test whether individual synchronization levels are predicted by individual brain states along an extended regime of critical-like dynamics - the Griffiths phase (GP). We use computational modelling to assess how synchronization is dependent on brain criticality indexed by long-range temporal correlations (LRTCs). We analyze LRTCs and synchronization of oscillations from resting-state magnetoencephalography and stereo-electroencephalography data. Synchronization and LRTCs are both positively linearly and quadratically correlated among healthy subjects, while in epileptogenic areas they are negatively linearly correlated. These results show that variability in synchronization levels is explained by the individual position along the GP with healthy brain areas operating in its subcritical and epileptogenic areas in its supercritical side. We suggest that the GP is fundamental for brain function allowing individual variability while retaining functional advantages of criticality.
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Affiliation(s)
- Marco Fuscà
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Felix Siebenhühner
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University, and Helsinki University Hospital, Helsinki, Finland
| | - Sheng H Wang
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- CEA, NeuroSpin, Gif-sur-Yvette, France
- MIND team, Inria, Université Paris-Saclay, Bures-sur-Yvette, France
| | - Vladislav Myrov
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Dept. of Informatics, Bioengineering, Robotics and System engineering, University of Genoa, Genoa, Italy
| | - Lino Nobili
- Child Neuropsychiatry Unit, IRCCS, Istituto G. Gaslini, Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy
- "Claudio Munari" Epilepsy Surgery Centre, Niguarda Hospital, Milan, Italy
| | - J Matias Palva
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Satu Palva
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK.
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
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11
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Janarek J, Drogosz Z, Grela J, Ochab JK, Oświęcimka P. Investigating structural and functional aspects of the brain's criticality in stroke. Sci Rep 2023; 13:12341. [PMID: 37524891 PMCID: PMC10390586 DOI: 10.1038/s41598-023-39467-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/26/2023] [Indexed: 08/02/2023] Open
Abstract
This paper addresses the question of the brain's critical dynamics after an injury such as a stroke. It is hypothesized that the healthy brain operates near a phase transition (critical point), which provides optimal conditions for information transmission and responses to inputs. If structural damage could cause the critical point to disappear and thus make self-organized criticality unachievable, it would offer the theoretical explanation for the post-stroke impairment of brain function. In our contribution, however, we demonstrate using network models of the brain, that the dynamics remain critical even after a stroke. In cases where the average size of the second-largest cluster of active nodes, which is one of the commonly used indicators of criticality, shows an anomalous behavior, it results from the loss of integrity of the network, quantifiable within graph theory, and not from genuine non-critical dynamics. We propose a new simple model of an artificial stroke that explains this anomaly. The proposed interpretation of the results is confirmed by an analysis of real connectomes acquired from post-stroke patients and a control group. The results presented refer to neurobiological data; however, the conclusions reached apply to a broad class of complex systems that admit a critical state.
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Affiliation(s)
- Jakub Janarek
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland
| | - Zbigniew Drogosz
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland
| | - Jacek Grela
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland
- Mark Kac Center for Complex Systems Research, Jagiellonian University, 30-348, Kraków, Poland
| | - Jeremi K Ochab
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland.
- Mark Kac Center for Complex Systems Research, Jagiellonian University, 30-348, Kraków, Poland.
| | - Paweł Oświęcimka
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland
- Mark Kac Center for Complex Systems Research, Jagiellonian University, 30-348, Kraków, Poland
- Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, 31-342, Kraków, Poland
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12
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Kloucek MB, Machon T, Kajimura S, Royall CP, Masuda N, Turci F. Biases in inverse Ising estimates of near-critical behavior. Phys Rev E 2023; 108:014109. [PMID: 37583208 DOI: 10.1103/physreve.108.014109] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 04/27/2023] [Indexed: 08/17/2023]
Abstract
Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and they may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer to criticality than one would expect from the data. Data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging data set from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world data sets.
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Affiliation(s)
- Maximilian B Kloucek
- School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
- Bristol Centre for Functional Nanomaterials, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
| | - Thomas Machon
- School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
| | - Shogo Kajimura
- Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto 606-8585, Japan
| | - C Patrick Royall
- Gulliver UMR CNRS 7083, ESPCI Paris, Université PSL, 75005 Paris, France
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York 14260-2900, USA
- Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, Buffalo, New York 14260-5030, USA
| | - Francesco Turci
- School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
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13
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Shine JM. Neuromodulatory control of complex adaptive dynamics in the brain. Interface Focus 2023; 13:20220079. [PMID: 37065268 PMCID: PMC10102735 DOI: 10.1098/rsfs.2022.0079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/23/2023] [Indexed: 04/18/2023] Open
Abstract
How is the massive dimensionality and complexity of the microscopic constituents of the nervous system brought under sufficiently tight control so as to coordinate adaptive behaviour? A powerful means for striking this balance is to poise neurons close to the critical point of a phase transition, at which a small change in neuronal excitability can manifest a nonlinear augmentation in neuronal activity. How the brain could mediate this critical transition is a key open question in neuroscience. Here, I propose that the different arms of the ascending arousal system provide the brain with a diverse set of heterogeneous control parameters that can be used to modulate the excitability and receptivity of target neurons-in other words, to act as control parameters for mediating critical neuronal order. Through a series of worked examples, I demonstrate how the neuromodulatory arousal system can interact with the inherent topological complexity of neuronal subsystems in the brain to mediate complex adaptive behaviour.
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Affiliation(s)
- James M. Shine
- Brain and Mind Center, The University of Sydney, Sydney, Australia
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14
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Stöber TM, Batulin D, Triesch J, Narayanan R, Jedlicka P. Degeneracy in epilepsy: multiple routes to hyperexcitable brain circuits and their repair. Commun Biol 2023; 6:479. [PMID: 37137938 PMCID: PMC10156698 DOI: 10.1038/s42003-023-04823-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 04/06/2023] [Indexed: 05/05/2023] Open
Abstract
Due to its complex and multifaceted nature, developing effective treatments for epilepsy is still a major challenge. To deal with this complexity we introduce the concept of degeneracy to the field of epilepsy research: the ability of disparate elements to cause an analogous function or malfunction. Here, we review examples of epilepsy-related degeneracy at multiple levels of brain organisation, ranging from the cellular to the network and systems level. Based on these insights, we outline new multiscale and population modelling approaches to disentangle the complex web of interactions underlying epilepsy and to design personalised multitarget therapies.
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Affiliation(s)
- Tristan Manfred Stöber
- Frankfurt Institute for Advanced Studies, 60438, Frankfurt am Main, Germany
- Institute for Neural Computation, Faculty of Computer Science, Ruhr University Bochum, 44801, Bochum, Germany
- Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Goethe University, 60590, Frankfurt, Germany
| | - Danylo Batulin
- Frankfurt Institute for Advanced Studies, 60438, Frankfurt am Main, Germany
- CePTER - Center for Personalized Translational Epilepsy Research, Goethe University, 60590, Frankfurt, Germany
- Faculty of Computer Science and Mathematics, Goethe University, 60486, Frankfurt, Germany
| | - Jochen Triesch
- Frankfurt Institute for Advanced Studies, 60438, Frankfurt am Main, Germany
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, India
| | - Peter Jedlicka
- ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Faculty of Medicine, Justus Liebig University Giessen, 35390, Giessen, Germany.
- Institute of Clinical Neuroanatomy, Neuroscience Center, Goethe University, 60590, Frankfurt am Main, Germany.
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15
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Anderson ED, Barbey AK. Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach. Hum Brain Mapp 2023; 44:1647-1665. [PMID: 36537816 PMCID: PMC9921238 DOI: 10.1002/hbm.26164] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 09/18/2022] [Accepted: 11/10/2022] [Indexed: 12/24/2022] Open
Abstract
Central to modern neuroscientific theories of human intelligence is the notion that general intelligence depends on a primary brain region or network, engaging spatially localized (rather than global) neural representations. Recent findings in network neuroscience, however, challenge this assumption, providing evidence that general intelligence may depend on system-wide network mechanisms, suggesting that local representations are necessary but not sufficient to account for the neural architecture of human intelligence. Despite the importance of this key theoretical distinction, prior research has not systematically investigated the role of local versus global neural representations in predicting general intelligence. We conducted a large-scale connectome-based predictive modeling study (N = 297), administering resting-state fMRI and a comprehensive cognitive battery to evaluate the efficacy of modern neuroscientific theories of human intelligence, including spatially localized theories (Lateral Prefrontal Cortex Theory, Parieto-Frontal Integration Theory, and Multiple Demand Theory) and recent global accounts (Process Overlap Theory and Network Neuroscience Theory). The results of our study demonstrate that general intelligence can be predicted by local functional connectivity profiles but is most robustly explained by global profiles of whole-brain connectivity. Our findings further suggest that the improved efficacy of global theories is not reducible to a greater strength or number of connections, but instead results from considering both strong and weak connections that provide the basis for intelligence (as predicted by the Network Neuroscience Theory). Our results highlight the importance of considering local neural representations in the context of a global information-processing architecture, suggesting future directions for theory-driven research on system-wide network mechanisms underlying general intelligence.
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Affiliation(s)
- Evan D. Anderson
- Decision Neuroscience LaboratoryBeckman Institute for Advanced Science and Technology, University of IllinoisUrbanaIllinoisUSA
- Neuroscience ProgramUniversity of IllinoisUrbanaIllinoisUSA
- Ball Aerospace and Technologies CorpBroomfieldColoradoUSA
- Air Force Research LaboratoryWright‐Patterson AFBOhioUSA
| | - Aron K. Barbey
- Decision Neuroscience LaboratoryBeckman Institute for Advanced Science and Technology, University of IllinoisUrbanaIllinoisUSA
- Neuroscience ProgramUniversity of IllinoisUrbanaIllinoisUSA
- Department of PsychologyUniversity of IllinoisUrbanaIllinoisUSA
- Department of BioengineeringUniversity of IllinoisUrbanaIllinoisUSA
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16
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Haruna J, Toshio R, Nakano N. Path integral approach to universal dynamics of reservoir computers. Phys Rev E 2023; 107:034306. [PMID: 37073052 DOI: 10.1103/physreve.107.034306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 02/06/2023] [Indexed: 04/20/2023]
Abstract
In this work, we give a characterization of the reservoir computer (RC) by the network structure, especially the probability distribution of random coupling constants. First, based on the path integral method, we clarify the universal behavior of the random network dynamics in the thermodynamic limit, which depends only on the asymptotic behavior of the second cumulant generating functions of the network coupling constants. This result enables us to classify the random networks into several universality classes, according to the distribution function of coupling constants chosen for the networks. Interestingly, it is revealed that such a classification has a close relationship with the distribution of eigenvalues of the random coupling matrix. We also comment on the relation between our theory and some practical choices of random connectivity in the RC. Subsequently, we investigate the relationship between the RC's computational power and the network parameters for several universality classes. We perform several numerical simulations to evaluate the phase diagrams of the steady reservoir states, common-signal-induced synchronization, and the computational power in the chaotic time series inference tasks. As a result, we clarify the close relationship between these quantities, especially a remarkable computational performance near the phase transitions, which is realized even near a nonchaotic transition boundary. These results may provide us with a new perspective on the designing principle for the RC.
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Affiliation(s)
- Junichi Haruna
- Department of Physics, Kyoto University, Kyoto 606-8502, Japan
| | - Riki Toshio
- Department of Physics, Kyoto University, Kyoto 606-8502, Japan
| | - Naoto Nakano
- Graduate School of Advanced Mathematical Sciences, Meiji University, Tokyo 164-8525, Japan
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17
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Pei L, Zhou X, Leung FKS, Ouyang G. Differential associations between scale-free neural dynamics and different levels of cognitive ability. Psychophysiology 2023; 60:e14259. [PMID: 36700291 DOI: 10.1111/psyp.14259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 12/14/2022] [Accepted: 01/08/2023] [Indexed: 01/27/2023]
Abstract
As indicators of cognitive function, scale-free neural dynamics are gaining increasing attention in cognitive neuroscience. Although the functional relevance of scale-free dynamics has been extensively reported, one fundamental question about its association with cognitive ability remains unanswered: is the association universal across a wide spectrum of cognitive abilities or confined to specific domains? Based on dual-process theory, we designed two categories of tasks to analyze two types of cognitive processes-automatic and controlled-and examined their associations with scale-free neural dynamics characterized from resting-state electroencephalography (EEG) recordings obtained from a large sample of human adults (N = 102). Our results showed that resting-state scale-free neural dynamics did not predict individuals' behavioral performance in tasks that primarily engaged the automatic process but did so in tasks that primarily engaged the controlled process. In addition, by fitting the scale-free parameters separately in different frequency bands, we found that the cognitive association of scale-free dynamics was more strongly manifested in higher-band EEG spectrum. Our findings indicate that resting-state scale-free dynamics are not universal neural indicators for all cognitive abilities but are mainly associated with high-level cognition that entails controlled processes. This finding is compatible with the widely claimed role of scale-free dynamics in reflecting properties of complex dynamic systems.
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Affiliation(s)
- Leisi Pei
- Faculty of Education, The University of Hong Kong, Hong Kong, China
| | - Xinlin Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | | | - Guang Ouyang
- Faculty of Education, The University of Hong Kong, Hong Kong, China
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18
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Riquelme JL, Hemberger M, Laurent G, Gjorgjieva J. Single spikes drive sequential propagation and routing of activity in a cortical network. eLife 2023; 12:79928. [PMID: 36780217 PMCID: PMC9925052 DOI: 10.7554/elife.79928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 12/19/2022] [Indexed: 02/14/2023] Open
Abstract
Single spikes can trigger repeatable firing sequences in cortical networks. The mechanisms that support reliable propagation of activity from such small events and their functional consequences remain unclear. By constraining a recurrent network model with experimental statistics from turtle cortex, we generate reliable and temporally precise sequences from single spike triggers. We find that rare strong connections support sequence propagation, while dense weak connections modulate propagation reliability. We identify sections of sequences corresponding to divergent branches of strongly connected neurons which can be selectively gated. Applying external inputs to specific neurons in the sparse backbone of strong connections can effectively control propagation and route activity within the network. Finally, we demonstrate that concurrent sequences interact reliably, generating a highly combinatorial space of sequence activations. Our results reveal the impact of individual spikes in cortical circuits, detailing how repeatable sequences of activity can be triggered, sustained, and controlled during cortical computations.
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Affiliation(s)
- Juan Luis Riquelme
- Max Planck Institute for Brain ResearchFrankfurt am MainGermany,School of Life Sciences, Technical University of MunichFreisingGermany
| | - Mike Hemberger
- Max Planck Institute for Brain ResearchFrankfurt am MainGermany
| | - Gilles Laurent
- Max Planck Institute for Brain ResearchFrankfurt am MainGermany
| | - Julijana Gjorgjieva
- Max Planck Institute for Brain ResearchFrankfurt am MainGermany,School of Life Sciences, Technical University of MunichFreisingGermany
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19
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Fosque LJ, Alipour A, Zare M, Williams-García RV, Beggs JM, Ortiz G. Quasicriticality explains variability of human neural dynamics across life span. Front Comput Neurosci 2022; 16:1037550. [PMID: 36532868 PMCID: PMC9747757 DOI: 10.3389/fncom.2022.1037550] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/27/2022] [Indexed: 08/26/2023] Open
Abstract
Aging impacts the brain's structural and functional organization and over time leads to various disorders, such as Alzheimer's disease and cognitive impairment. The process also impacts sensory function, bringing about a general slowing in various perceptual and cognitive functions. Here, we analyze the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) resting-state magnetoencephalography (MEG) dataset-the largest aging cohort available-in light of the quasicriticality framework, a novel organizing principle for brain functionality which relates information processing and scaling properties of brain activity to brain connectivity and stimulus. Examination of the data using this framework reveals interesting correlations with age and gender of test subjects. Using simulated data as verification, our results suggest a link between changes to brain connectivity due to aging and increased dynamical fluctuations of neuronal firing rates. Our findings suggest a platform to develop biomarkers of neurological health.
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Affiliation(s)
- Leandro J. Fosque
- Department of Physics, Indiana University, Bloomington, IN, United States
| | - Abolfazl Alipour
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
| | | | | | - John M. Beggs
- Department of Physics, Indiana University, Bloomington, IN, United States
| | - Gerardo Ortiz
- Department of Physics, Indiana University, Bloomington, IN, United States
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20
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Neto JP, Spitzner FP, Priesemann V. Sampling effects and measurement overlap can bias the inference of neuronal avalanches. PLoS Comput Biol 2022; 18:e1010678. [PMID: 36445932 PMCID: PMC9733887 DOI: 10.1371/journal.pcbi.1010678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 12/09/2022] [Accepted: 10/24/2022] [Indexed: 12/02/2022] Open
Abstract
To date, it is still impossible to sample the entire mammalian brain with single-neuron precision. This forces one to either use spikes (focusing on few neurons) or to use coarse-sampled activity (averaging over many neurons, e.g. LFP). Naturally, the sampling technique impacts inference about collective properties. Here, we emulate both sampling techniques on a simple spiking model to quantify how they alter observed correlations and signatures of criticality. We describe a general effect: when the inter-electrode distance is small, electrodes sample overlapping regions in space, which increases the correlation between the signals. For coarse-sampled activity, this can produce power-law distributions even for non-critical systems. In contrast, spike recordings do not suffer this particular bias and underlying dynamics can be identified. This may resolve why coarse measures and spikes have produced contradicting results in the past.
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Affiliation(s)
- Joao Pinheiro Neto
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - F. Paul Spitzner
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Viola Priesemann
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- Bernstein Center for Computational Neuroscience, Göttingen, Germany
- Georg-August University Göttingen, Göttingen, Germany
- * E-mail:
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21
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Martinez-Saito M. Discrete scaling and criticality in a chain of adaptive excitable integrators. CHAOS, SOLITONS & FRACTALS 2022; 163:112574. [DOI: 10.1016/j.chaos.2022.112574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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22
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Braccini M, Roli A, Barbieri E, Kauffman SA. On the Criticality of Adaptive Boolean Network Robots. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1368. [PMID: 37420388 DOI: 10.3390/e24101368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/21/2022] [Accepted: 09/22/2022] [Indexed: 07/09/2023]
Abstract
Systems poised at a dynamical critical regime, between order and disorder, have been shown capable of exhibiting complex dynamics that balance robustness to external perturbations and rich repertoires of responses to inputs. This property has been exploited in artificial network classifiers, and preliminary results have also been attained in the context of robots controlled by Boolean networks. In this work, we investigate the role of dynamical criticality in robots undergoing online adaptation, i.e., robots that adapt some of their internal parameters to improve a performance metric over time during their activity. We study the behavior of robots controlled by random Boolean networks, which are either adapted in their coupling with robot sensors and actuators or in their structure or both. We observe that robots controlled by critical random Boolean networks have higher average and maximum performance than that of robots controlled by ordered and disordered nets. Notably, in general, adaptation by change of couplings produces robots with slightly higher performance than those adapted by changing their structure. Moreover, we observe that when adapted in their structure, ordered networks tend to move to the critical dynamical regime. These results provide further support to the conjecture that critical regimes favor adaptation and indicate the advantage of calibrating robot control systems at dynamical critical states.
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Affiliation(s)
- Michele Braccini
- Department of Computer Science and Engineering, Università di Bologna, Campus of Cesena, I-47521 Cesena, Italy
| | - Andrea Roli
- Department of Computer Science and Engineering, Università di Bologna, Campus of Cesena, I-47521 Cesena, Italy
- European Centre for Living Technology, I-30123 Venezia, Italy
| | - Edoardo Barbieri
- Department of Computer Science and Engineering, Università di Bologna, Campus of Cesena, I-47521 Cesena, Italy
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23
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Beggs JM. Addressing skepticism of the critical brain hypothesis. Front Comput Neurosci 2022; 16:703865. [PMID: 36185712 PMCID: PMC9520604 DOI: 10.3389/fncom.2022.703865] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/08/2022] [Indexed: 11/16/2022] Open
Abstract
The hypothesis that living neural networks operate near a critical phase transition point has received substantial discussion. This “criticality hypothesis” is potentially important because experiments and theory show that optimal information processing and health are associated with operating near the critical point. Despite the promise of this idea, there have been several objections to it. While earlier objections have been addressed already, the more recent critiques of Touboul and Destexhe have not yet been fully met. The purpose of this paper is to describe their objections and offer responses. Their first objection is that the well-known Brunel model for cortical networks does not display a peak in mutual information near its phase transition, in apparent contradiction to the criticality hypothesis. In response I show that it does have such a peak near the phase transition point, provided it is not strongly driven by random inputs. Their second objection is that even simple models like a coin flip can satisfy multiple criteria of criticality. This suggests that the emergent criticality claimed to exist in cortical networks is just the consequence of a random walk put through a threshold. In response I show that while such processes can produce many signatures criticality, these signatures (1) do not emerge from collective interactions, (2) do not support information processing, and (3) do not have long-range temporal correlations. Because experiments show these three features are consistently present in living neural networks, such random walk models are inadequate. Nevertheless, I conclude that these objections have been valuable for refining research questions and should always be welcomed as a part of the scientific process.
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Affiliation(s)
- John M. Beggs
- Department of Physics, Indiana University Bloomington, Bloomington, IN, United States
- Program in Neuroscience, Indiana University Bloomington, Bloomington, IN, United States
- *Correspondence: John M. Beggs,
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24
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Heiney K, Huse Ramstad O, Fiskum V, Sandvig A, Sandvig I, Nichele S. Neuronal avalanche dynamics and functional connectivity elucidate information propagation in vitro. Front Neural Circuits 2022; 16:980631. [PMID: 36188125 PMCID: PMC9520060 DOI: 10.3389/fncir.2022.980631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
Cascading activity is commonly observed in complex dynamical systems, including networks of biological neurons, and how these cascades spread through the system is reliant on how the elements of the system are connected and organized. In this work, we studied networks of neurons as they matured over 50 days in vitro and evaluated both their dynamics and their functional connectivity structures by observing their electrophysiological activity using microelectrode array recordings. Correlations were obtained between features of their activity propagation and functional connectivity characteristics to elucidate the interplay between dynamics and structure. The results indicate that in vitro networks maintain a slightly subcritical state by striking a balance between integration and segregation. Our work demonstrates the complementarity of these two approaches—functional connectivity and avalanche dynamics—in studying information propagation in neurons in vitro, which can in turn inform the design and optimization of engineered computational substrates.
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Affiliation(s)
- Kristine Heiney
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
- *Correspondence: Kristine Heiney
| | - Ola Huse Ramstad
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Vegard Fiskum
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Axel Sandvig
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology, St. Olav's Hospital, Trondheim, Norway
- Department of Community Medicine and Rehabilitation, St. Olav's Hospital, Trondheim, Norway
- Department of Clinical Neuroscience, Umeå University Hospital, Umeå, Sweden
| | - Ioanna Sandvig
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology, St. Olav's Hospital, Trondheim, Norway
| | - Stefano Nichele
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
- Department of Computer Science and Communication, Østfold University College, Halden, Norway
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25
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Wilkerson G, Moschoyiannis S, Jensen HJ. Spontaneous emergence of computation in network cascades. Sci Rep 2022; 12:14925. [PMID: 36056137 PMCID: PMC9440044 DOI: 10.1038/s41598-022-19218-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 08/25/2022] [Indexed: 11/21/2022] Open
Abstract
Neuronal network computation and computation by avalanche supporting networks are of interest to the fields of physics, computer science (computation theory as well as statistical or machine learning) and neuroscience. Here we show that computation of complex Boolean functions arises spontaneously in threshold networks as a function of connectivity and antagonism (inhibition), computed by logic automata (motifs) in the form of computational cascades. We explain the emergent inverse relationship between the computational complexity of the motifs and their rank-ordering by function probabilities due to motifs, and its relationship to symmetry in function space. We also show that the optimal fraction of inhibition observed here supports results in computational neuroscience, relating to optimal information processing.
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Affiliation(s)
- Galen Wilkerson
- School of Computer Science and Electronic Engineering, University of Surrey, Guildford, GU2 7XH, UK.
- Department of Mathematics, Centre for Complexity Science, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
| | - Sotiris Moschoyiannis
- School of Computer Science and Electronic Engineering, University of Surrey, Guildford, GU2 7XH, UK
| | - Henrik Jeldtoft Jensen
- Department of Mathematics, Centre for Complexity Science, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
- Institute of Innovative Research, Tokyo Institute of Technology, 4259, Nagatsuta-cho, Yokohama, 226-8502, Japan
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Kauffman SA, Roli A. What is consciousness? Artificial intelligence, real intelligence, quantum mind and qualia. Biol J Linn Soc Lond 2022. [DOI: 10.1093/biolinnean/blac092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Abstract
We approach the question ‘What is consciousness?’ in a new way, not as Descartes’ ‘systematic doubt’, but as how organisms find their way in their world. Finding one’s way involves finding possible uses of features of the world that might be beneficial or avoiding those that might be harmful. ‘Possible uses of X to accomplish Y’ are ‘affordances’. The number of uses of X is indefinite (or unknown), the different uses are unordered, are not listable, and are not deducible from one another. All biological adaptations are either affordances seized by heritable variation and selection or, far faster, by the organism acting in its world finding uses of X to accomplish Y. Based on this, we reach rather astonishing conclusions:
1. Artificial general intelligence based on universal Turing machines (UTMs) is not possible, since UTMs cannot ‘find’ novel affordances.
2. Brain-mind is not purely classical physics for no classical physics system can be an analogue computer whose dynamical behaviour can be isomorphic to ‘possible uses’.
3. Brain-mind must be partly quantum—supported by increasing evidence at 6.0 to 7.3 sigma.
4. Based on Heisenberg’s interpretation of the quantum state as ‘potentia’ converted to ‘actuals’ by measurement, where this interpretation is not a substance dualism, a natural hypothesis is that mind actualizes potentia. This is supported at 5.2 sigma. Then mind’s actualizations of entangled brain-mind-world states are experienced as qualia and allow ‘seeing’ or ‘perceiving’ of uses of X to accomplish Y. We can and do jury-rig. Computers cannot.
5. Beyond familiar quantum computers, we discuss the potentialities of trans-Turing systems.
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Affiliation(s)
| | - Andrea Roli
- Department of Computer Science and Engineering, Alma Mater Studiorum Università di Bologna , Campus of Cesena, Via dell’Università, Cesena , Italy
- European Centre for Living Technology , Dorsoduro, Venezia , Italy
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27
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Maria Pani S, Saba L, Fraschini M. Clinical applications of EEG power spectra aperiodic component analysis: a mini-review. Clin Neurophysiol 2022; 143:1-13. [DOI: 10.1016/j.clinph.2022.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 08/09/2022] [Accepted: 08/11/2022] [Indexed: 11/03/2022]
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28
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Yu C. Toward a Unified Analysis of the Brain Criticality Hypothesis: Reviewing Several Available Tools. Front Neural Circuits 2022; 16:911245. [PMID: 35669452 PMCID: PMC9164306 DOI: 10.3389/fncir.2022.911245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
The study of the brain criticality hypothesis has been going on for about 20 years, various models and methods have been developed for probing this field, together with large amounts of controversial experimental findings. However, no standardized protocol of analysis has been established so far. Therefore, hoping to make some contributions to standardization of such analysis, we review several available tools used for estimating the criticality of the brain in this paper.
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Wang L, Fan H, Xiao J, Lan Y, Wang X. Criticality in reservoir computer of coupled phase oscillators. Phys Rev E 2022; 105:L052201. [PMID: 35706173 DOI: 10.1103/physreve.105.l052201] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 04/12/2022] [Indexed: 06/15/2023]
Abstract
Accumulating evidence shows that the cerebral cortex is operating near a critical state featured by power-law size distribution of neural avalanche activities, yet evidence of this critical state in artificial neural networks mimicking the cerebral cortex is still lacking. Here we design an artificial neural network of coupled phase oscillators and, by the technique of reservoir computing in machine learning, train it for predicting chaos. It is found that when the machine is properly trained, oscillators in the reservoir are synchronized into clusters whose sizes follow a power-law distribution. This feature, however, is absent when the machine is poorly trained. Additionally, it is found that despite the synchronization degree of the original network, once properly trained, the reservoir network is always developed to the same critical state, exemplifying the "attractor" nature of this state in machine learning. The generality of the results is verified in different reservoir models and by different target systems, and it is found that the scaling exponent of the distribution is independent of the reservoir details and the bifurcation parameters of the target system, but is modified when the dynamics of the target system is changed to a different type. The findings shed light on the nature of machine learning, and are helpful to the design of high-performance machines in physical systems.
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Affiliation(s)
- Liang Wang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China
| | - Huawei Fan
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China
| | - Jinghua Xiao
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Yueheng Lan
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Xingang Wang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China
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30
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Evertz R, Hicks DG, Liley DTJ. Alpha blocking and 1/fβ spectral scaling in resting EEG can be accounted for by a sum of damped alpha band oscillatory processes. PLoS Comput Biol 2022; 18:e1010012. [PMID: 35427355 PMCID: PMC9045666 DOI: 10.1371/journal.pcbi.1010012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 04/27/2022] [Accepted: 03/11/2022] [Indexed: 11/18/2022] Open
Abstract
The dynamical and physiological basis of alpha band activity and 1/fβ noise in the EEG are the subject of continued speculation. Here we conjecture, on the basis of empirical data analysis, that both of these features may be economically accounted for through a single process if the resting EEG is conceived of being the sum of multiple stochastically perturbed alpha band damped linear oscillators with a distribution of dampings (relaxation rates). The modulation of alpha-band and 1/fβ noise activity by changes in damping is explored in eyes closed (EC) and eyes open (EO) resting state EEG. We aim to estimate the distribution of dampings by solving an inverse problem applied to EEG power spectra. The characteristics of the damping distribution are examined across subjects, sensors and recording condition (EC/EO). We find that there are robust changes in the damping distribution between EC and EO recording conditions across participants. The estimated damping distributions are found to be predominantly bimodal, with the number and position of the modes related to the sharpness of the alpha resonance and the scaling (β) of the power spectrum (1/fβ). The results suggest that there exists an intimate relationship between resting state alpha activity and 1/fβ noise with changes in both governed by changes to the damping of the underlying alpha oscillatory processes. In particular, alpha-blocking is observed to be the result of the most weakly damped distribution mode becoming more heavily damped. The results suggest a novel way of characterizing resting EEG power spectra and provides new insight into the central role that damped alpha-band activity may play in characterising the spatio-temporal features of resting state EEG. The resting human electroencephalogram (EEG) exhibits two dominant spectral features: the alpha rhythm (8–13 Hz) and its associated attenuation between eyes-closed and eyes-open resting state (alpha blocking), and the 1/fβ scaling of the power spectrum. While these phenomena are well studied a thorough understanding of their respective generative processes remains elusive. By employing a theoretical approach that follows from neural population models of EEG we demonstrate that it is possible to economically account for both of these phenomena using a singular mechanistic framework: resting EEG is assumed to arise from the summed activity of multiple uncorrelated, stochastically driven, damped alpha band linear oscillatory processes having a distribution of relaxation rates or dampings. By numerically estimating these damping distributions from eyes-closed and eyes-open EEG data, in a total of 136 participants, it is found that such damping distributions are predominantly bimodal in shape. The most weakly damped mode is found to account for alpha band power, with alpha blocking being driven by an increase in the damping of this weakly damped mode, whereas the second, and more heavily damped mode, is able to explain 1/fβ scaling present in the resting state EEG spectra.
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Affiliation(s)
- Rick Evertz
- Optical Sciences Centre, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Department of Physics and Astronomy, Swinburne University of Technology, Hawthorn, Victoria, Australia
- * E-mail: (RE); (DGH); (DTJL)
| | - Damien G. Hicks
- Optical Sciences Centre, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Department of Physics and Astronomy, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- * E-mail: (RE); (DGH); (DTJL)
| | - David T. J. Liley
- Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
- * E-mail: (RE); (DGH); (DTJL)
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31
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The Analysis of Mammalian Hearing Systems Supports the Hypothesis That Criticality Favors Neuronal Information Representation but Not Computation. ENTROPY 2022; 24:e24040540. [PMID: 35455203 PMCID: PMC9029204 DOI: 10.3390/e24040540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/25/2022] [Accepted: 04/10/2022] [Indexed: 11/17/2022]
Abstract
In the neighborhood of critical states, distinct materials exhibit the same physical behavior, expressed by common simple laws among measurable observables, hence rendering a more detailed analysis of the individual systems obsolete. It is a widespread view that critical states are fundamental to neuroscience and directly favor computation. We argue here that from an evolutionary point of view, critical points seem indeed to be a natural phenomenon. Using mammalian hearing as our example, we show, however, explicitly that criticality does not describe the proper computational process and thus is only indirectly related to the computation in neural systems.
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32
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Levin M. Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds. Front Syst Neurosci 2022; 16:768201. [PMID: 35401131 PMCID: PMC8988303 DOI: 10.3389/fnsys.2022.768201] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/24/2022] [Indexed: 12/11/2022] Open
Abstract
Synthetic biology and bioengineering provide the opportunity to create novel embodied cognitive systems (otherwise known as minds) in a very wide variety of chimeric architectures combining evolved and designed material and software. These advances are disrupting familiar concepts in the philosophy of mind, and require new ways of thinking about and comparing truly diverse intelligences, whose composition and origin are not like any of the available natural model species. In this Perspective, I introduce TAME-Technological Approach to Mind Everywhere-a framework for understanding and manipulating cognition in unconventional substrates. TAME formalizes a non-binary (continuous), empirically-based approach to strongly embodied agency. TAME provides a natural way to think about animal sentience as an instance of collective intelligence of cell groups, arising from dynamics that manifest in similar ways in numerous other substrates. When applied to regenerating/developmental systems, TAME suggests a perspective on morphogenesis as an example of basal cognition. The deep symmetry between problem-solving in anatomical, physiological, transcriptional, and 3D (traditional behavioral) spaces drives specific hypotheses by which cognitive capacities can increase during evolution. An important medium exploited by evolution for joining active subunits into greater agents is developmental bioelectricity, implemented by pre-neural use of ion channels and gap junctions to scale up cell-level feedback loops into anatomical homeostasis. This architecture of multi-scale competency of biological systems has important implications for plasticity of bodies and minds, greatly potentiating evolvability. Considering classical and recent data from the perspectives of computational science, evolutionary biology, and basal cognition, reveals a rich research program with many implications for cognitive science, evolutionary biology, regenerative medicine, and artificial intelligence.
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Affiliation(s)
- Michael Levin
- Allen Discovery Center at Tufts University, Medford, MA, United States
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Cambridge, MA, United States
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33
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Approximate Entropy of Spiking Series Reveals Different Dynamical States in Cortical Assemblies. ELECTRONICS 2022. [DOI: 10.3390/electronics11060936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Self-organized criticality theory proved that information transmission and computational performances of neural networks are optimal in critical state. By using recordings of the spontaneous activity originated by dissociated neuronal assemblies coupled to Micro-Electrode Arrays (MEAs), we tested this hypothesis using Approximate Entropy (ApEn) as a measure of complexity and information transfer. We analysed 60 min of electrophysiological activity of three neuronal cultures exhibiting either sub-critical, critical or super-critical behaviour. The firing patterns on each electrode was studied in terms of the inter-spike interval (ISI), whose complexity was quantified using ApEn. We assessed that in critical state the local complexity (measured in terms of ApEn) is larger than in sub- and super-critical conditions (mean ± std, ApEn about 0.93 ± 0.09, 0.66 ± 0.18, 0.49 ± 0.27, for the cultures in critical, sub-critical and super-critical state, respectively—differences statistically significant). Our estimations were stable when considering epochs as short as 5 min (pairwise cross-correlation of spatial distribution of mean ApEn of 94 ± 5%). These preliminary results indicate that ApEn has the potential of being a reliable and stable index to monitor local information transmission in a neuronal network during maturation. Thus, ApEn applied on ISI time series appears to be potentially useful to reflect the overall complex behaviour of the neural network, even monitoring a single specific location.
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34
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Kim M, Harris RE, DaSilva AF, Lee U. Explosive Synchronization-Based Brain Modulation Reduces Hypersensitivity in the Brain Network: A Computational Model Study. Front Comput Neurosci 2022; 16:815099. [PMID: 35311218 PMCID: PMC8927545 DOI: 10.3389/fncom.2022.815099] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/18/2022] [Indexed: 11/29/2022] Open
Abstract
Fibromyalgia (FM) is a chronic pain condition that is characterized by hypersensitivity to multimodal sensory stimuli, widespread pain, and fatigue. We have previously proposed explosive synchronization (ES), a phenomenon wherein a small perturbation to a network can lead to an abrupt state transition, as a potential mechanism of the hypersensitive FM brain. Therefore, we hypothesized that converting a brain network from ES to general synchronization (GS) may reduce the hypersensitivity of FM brain. To find an effective brain network modulation to convert ES into GS, we constructed a large-scale brain network model near criticality (i.e., an optimally balanced state between order and disorders), which reflects brain dynamics in conscious wakefulness, and adjusted two parameters: local structural connectivity and signal randomness of target brain regions. The network sensitivity to global stimuli was compared between the brain networks before and after the modulation. We found that only increasing the local connectivity of hubs (nodes with intense connections) changes ES to GS, reducing the sensitivity, whereas other types of modulation such as decreasing local connectivity, increasing and decreasing signal randomness are not effective. This study would help to develop a network mechanism-based brain modulation method to reduce the hypersensitivity in FM.
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Affiliation(s)
- MinKyung Kim
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Richard E. Harris
- Chronic Pain and Fatigue Research Center, Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States
| | - Alexandre F. DaSilva
- Headache & Orofacial Pain Effort Laboratory, Biologic & Materials Sciences Department, University of Michigan School of Dentistry, Ann Arbor, MI, United States
| | - UnCheol Lee
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
- *Correspondence: UnCheol Lee,
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35
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Páscoa dos Santos F, Verschure PFMJ. Excitatory-Inhibitory Homeostasis and Diaschisis: Tying the Local and Global Scales in the Post-stroke Cortex. Front Syst Neurosci 2022; 15:806544. [PMID: 35082606 PMCID: PMC8785563 DOI: 10.3389/fnsys.2021.806544] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 11/29/2021] [Indexed: 12/28/2022] Open
Abstract
Maintaining a balance between excitatory and inhibitory activity is an essential feature of neural networks of the neocortex. In the face of perturbations in the levels of excitation to cortical neurons, synapses adjust to maintain excitatory-inhibitory (EI) balance. In this review, we summarize research on this EI homeostasis in the neocortex, using stroke as our case study, and in particular the loss of excitation to distant cortical regions after focal lesions. Widespread changes following a localized lesion, a phenomenon known as diaschisis, are not only related to excitability, but also observed with respect to functional connectivity. Here, we highlight the main findings regarding the evolution of excitability and functional cortical networks during the process of post-stroke recovery, and how both are related to functional recovery. We show that cortical reorganization at a global scale can be explained from the perspective of EI homeostasis. Indeed, recovery of functional networks is paralleled by increases in excitability across the cortex. These adaptive changes likely result from plasticity mechanisms such as synaptic scaling and are linked to EI homeostasis, providing a possible target for future therapeutic strategies in the process of rehabilitation. In addition, we address the difficulty of simultaneously studying these multiscale processes by presenting recent advances in large-scale modeling of the human cortex in the contexts of stroke and EI homeostasis, suggesting computational modeling as a powerful tool to tie the meso- and macro-scale processes of recovery in stroke patients.
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Affiliation(s)
- Francisco Páscoa dos Santos
- Eodyne Systems SL, Barcelona, Spain
- Laboratory of Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS), Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain
- Department of Information and Communications Technologies (DTIC), Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Paul F. M. J. Verschure
- Laboratory of Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS), Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
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36
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Khajehabdollahi S, Prosi J, Giannakakis E, Martius G, Levina A. When to Be Critical? Performance and Evolvability in Different Regimes of Neural Ising Agents. ARTIFICIAL LIFE 2022; 28:458-478. [PMID: 35984417 DOI: 10.1162/artl_a_00383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
It has long been hypothesized that operating close to the critical state is beneficial for natural and artificial evolutionary systems. We put this hypothesis to test in a system of evolving foraging agents controlled by neural networks that can adapt the agents' dynamical regime throughout evolution. Surprisingly, we find that all populations that discover solutions evolve to be subcritical. By a resilience analysis, we find that there are still benefits of starting the evolution in the critical regime. Namely, initially critical agents maintain their fitness level under environmental changes (for example, in the lifespan) and degrade gracefully when their genome is perturbed. At the same time, initially subcritical agents, even when evolved to the same fitness, are often inadequate to withstand the changes in the lifespan and degrade catastrophically with genetic perturbations. Furthermore, we find the optimal distance to criticality depends on the task complexity. To test it we introduce a hard task and a simple task: For the hard task, agents evolve closer to criticality, whereas more subcritical solutions are found for the simple task. We verify that our results are independent of the selected evolutionary mechanisms by testing them on two principally different approaches: a genetic algorithm and an evolutionary strategy. In summary, our study suggests that although optimal behaviour in the simple task is obtained in a subcritical regime, initializing near criticality is important to be efficient at finding optimal solutions for new tasks of unknown complexity.
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Affiliation(s)
- Sina Khajehabdollahi
- University of Tübingen, Department of Computer Science
- Max Planck Institute for Biological Cybernetics.
| | - Jan Prosi
- University of Tübingen, Department of Computer Science
- Max Planck Institute for Biological Cybernetics
| | - Emmanouil Giannakakis
- University of Tübingen, Department of Computer Science
- Max Planck Institute for Biological Cybernetics
| | | | - Anna Levina
- University of Tübingen, Department of Computer Science
- Max Planck Institute for Biological Cybernetics
- Bernstein Center for Computational Neuroscience Tübingen
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37
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Kim M, Kim H, Huang Z, Mashour GA, Jordan D, Ilg R, Lee U. Criticality Creates a Functional Platform for Network Transitions Between Internal and External Processing Modes in the Human Brain. Front Syst Neurosci 2021; 15:657809. [PMID: 34899199 PMCID: PMC8657781 DOI: 10.3389/fnsys.2021.657809] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 10/29/2021] [Indexed: 11/13/2022] Open
Abstract
Continuous switching between internal and external modes in the brain appears important for generating models of the self and the world. However, how the brain transitions between these two modes remains unknown. We propose that a large synchronization fluctuation of brain networks, emerging only near criticality (i.e., a balanced state between order and disorder), spontaneously creates temporal windows with distinct preferences for integrating the network's internal information or for processing external stimuli. Using a computational model, electroencephalography (EEG) analysis, and functional magnetic resonance imaging (fMRI) analysis during alterations of consciousness in humans, we report that synchronized and incoherent networks, respectively, bias toward internal and external information with specific network configurations. In the brain network model and EEG-based network, the network preferences are the most prominent at criticality and in conscious states associated with the bandwidth 4-12 Hz, with alternating functional network configurations. However, these network configurations are selectively disrupted in different states of consciousness such as general anesthesia, psychedelic states, minimally conscious states, and unresponsive wakefulness syndrome. The network preference for internal information integration is only significant in conscious states and psychedelic states, but not in other unconscious states, suggesting the importance of internal information integration in maintaining consciousness. The fMRI co-activation pattern analysis shows that functional networks that are sensitive to external stimuli-such as default mode, dorsal attentional, and frontoparietal networks-are activated in incoherent states, while insensitive networks, such as global activation and deactivation networks, are dominated in highly synchronized states. We suggest that criticality produces a functional platform for the brain's capability for continuous switching between two modes, which is crucial for the emergence of consciousness.
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Affiliation(s)
- Minkyung Kim
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Hyoungkyu Kim
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Zirui Huang
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
| | - George A Mashour
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States.,Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, United States
| | - Denis Jordan
- Applied Mathematics and Statistics, University of Applied Sciences Northwestern Switzerland, Muttenz, Switzerland.,Department of Anesthesiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,Department of Neurology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Rüdiger Ilg
- Applied Mathematics and Statistics, University of Applied Sciences Northwestern Switzerland, Muttenz, Switzerland.,Department of Anesthesiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,Department of Neurology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - UnCheol Lee
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
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38
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Mosam F, Vidaurre D, De Giuli E. Breakdown of random matrix universality in Markov models. Phys Rev E 2021; 104:024305. [PMID: 34525643 DOI: 10.1103/physreve.104.024305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 07/26/2021] [Indexed: 11/07/2022]
Abstract
Biological systems need to react to stimuli over a broad spectrum of timescales. If and how this ability can emerge without external fine-tuning is a puzzle. We consider here this problem in discrete Markovian systems, where we can leverage results from random matrix theory. Indeed, generic large transition matrices are governed by universal results, which predict the absence of long timescales unless fine-tuned. We consider an ensemble of transition matrices and motivate a temperature-like variable that controls the dynamic range of matrix elements, which we show plays a crucial role in the applicability of the large matrix limit: as the dynamic range increases, a phase transition occurs whereby the random matrix theory result is avoided, and long relaxation times ensue, in the entire "ordered" phase. We furthermore show that this phase transition is accompanied by a drop in the entropy rate and a peak in complexity, as measured by predictive information [Bialek, Nemenman, and Tishby Neural Comput. 13, 2409 (2001)NEUCEB0899-766710.1162/089976601753195969]. Extending the Markov model to a Hidden Markov model (HMM), we show that observable sequences inherit properties of the hidden sequences, allowing HMMs to be understood in terms of more accessible Markov models. We then apply our findings to fMRI data from 820 human subjects scanned at wakeful rest. We show that the data can be quantitatively understood in terms of the random model, and that brain activity lies close to the phase transition when engaged in unconstrained, task-free cognition-supporting the brain criticality hypothesis in this context.
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Affiliation(s)
- Faheem Mosam
- Department of Physics, Ryerson University, M5B 2K3, Toronto, Canada
| | - Diego Vidaurre
- Department of Psychiatry, Oxford University, OX3 7JX, United Kingdom.,Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, 8000, Denmark
| | - Eric De Giuli
- Department of Physics, Ryerson University, M5B 2K3, Toronto, Canada
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39
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Primavera BA, Shainline JM. Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware. Front Neurosci 2021; 15:732368. [PMID: 34552465 PMCID: PMC8450355 DOI: 10.3389/fnins.2021.732368] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 08/09/2021] [Indexed: 11/24/2022] Open
Abstract
Any large-scale spiking neuromorphic system striving for complexity at the level of the human brain and beyond will need to be co-optimized for communication and computation. Such reasoning leads to the proposal for optoelectronic neuromorphic platforms that leverage the complementary properties of optics and electronics. Starting from the conjecture that future large-scale neuromorphic systems will utilize integrated photonics and fiber optics for communication in conjunction with analog electronics for computation, we consider two possible paths toward achieving this vision. The first is a semiconductor platform based on analog CMOS circuits and waveguide-integrated photodiodes. The second is a superconducting approach that utilizes Josephson junctions and waveguide-integrated superconducting single-photon detectors. We discuss available devices, assess scaling potential, and provide a list of key metrics and demonstrations for each platform. Both platforms hold potential, but their development will diverge in important respects. Semiconductor systems benefit from a robust fabrication ecosystem and can build on extensive progress made in purely electronic neuromorphic computing but will require III-V light source integration with electronics at an unprecedented scale, further advances in ultra-low capacitance photodiodes, and success from emerging memory technologies. Superconducting systems place near theoretically minimum burdens on light sources (a tremendous boon to one of the most speculative aspects of either platform) and provide new opportunities for integrated, high-endurance synaptic memory. However, superconducting optoelectronic systems will also contend with interfacing low-voltage electronic circuits to semiconductor light sources, the serial biasing of superconducting devices on an unprecedented scale, a less mature fabrication ecosystem, and cryogenic infrastructure.
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Affiliation(s)
- Bryce A. Primavera
- National Institute of Standards and Technology, Boulder, CO, United States
- Department of Physics, University of Colorado Boulder, Boulder, CO, United States
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40
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Thuwal K, Banerjee A, Roy D. Aperiodic and Periodic Components of Ongoing Oscillatory Brain Dynamics Link Distinct Functional Aspects of Cognition across Adult Lifespan. eNeuro 2021; 8:ENEURO.0224-21.2021. [PMID: 34544762 PMCID: PMC8547598 DOI: 10.1523/eneuro.0224-21.2021] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/19/2021] [Accepted: 08/31/2021] [Indexed: 12/04/2022] Open
Abstract
Signal transmission in the brain propagates via distinct oscillatory frequency bands but the aperiodic component, 1/f activity, almost always co-exists which most of the previous studies have not sufficiently taken into consideration. We used a recently proposed parameterization model that delimits the oscillatory and aperiodic components of neural dynamics on lifespan aging data collected from human participants using magnetoencephalography (MEG). Since healthy aging underlines an enormous change in local tissue properties, any systematic relationship of 1/f activity would highlight their impact on the self-organized critical functional states. Furthermore, we have used patterns of correlation between aperiodic background and metrics of behavior to understand the domain general effects of 1/f activity. We suggest that age-associated global change in 1/f baseline alters the functional critical states of the brain affecting the global information processing impacting critically all aspects of cognition, e.g., metacognitive awareness, speed of retrieval of memory, cognitive load, and accuracy of recall through adult lifespan. This alteration in 1/f crucially impacts the oscillatory features peak frequency (PF) and band power ratio, which relates to more local processing and selective functional aspects of cognitive processing during the visual short-term memory (VSTM) task. In summary, this study leveraging on big lifespan data for the first time tracks the cross-sectional lifespan-associated periodic and aperiodic dynamical changes in the resting state to demonstrate how normative patterns of 1/f activity, PF, and band ratio (BR) measures provide distinct functional insights about the cognitive decline through adult lifespan.
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Affiliation(s)
- Kusum Thuwal
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Gurgaon, Haryana 122052, India
| | - Arpan Banerjee
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Gurgaon, Haryana 122052, India
| | - Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Gurgaon, Haryana 122052, India
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41
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Pazzini R, Kinouchi O, Costa AA. Neuronal avalanches in Watts-Strogatz networks of stochastic spiking neurons. Phys Rev E 2021; 104:014137. [PMID: 34412363 DOI: 10.1103/physreve.104.014137] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 06/08/2021] [Indexed: 01/03/2023]
Abstract
Networks of stochastic leaky integrate-and-fire neurons, both at the mean-field level and in square lattices, present a continuous absorbing phase transition with power-law neuronal avalanches at the critical point. Here we complement these results showing that small-world Watts-Strogatz networks have mean-field critical exponents for any rewiring probability p>0. For the ring (p=0), the exponents are the same from the dimension d=1 of the directed-percolation class. In the model, firings are stochastic and occur in discrete time steps, based on a sigmoidal firing probability function. Each neuron has a membrane potential that integrates the signals received from its neighbors. The membrane potentials are subject to a leakage parameter. We study topologies with a varied number of neuron connections and different values of the leakage parameter. Results indicate that the dynamic range is larger for p=0. We also study a homeostatic synaptic depression mechanism to self-organize the network towards the critical region. These stochastic oscillations are characteristic of the so-called self-organized quasicriticality.
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Affiliation(s)
- Renata Pazzini
- Universidade de São Paulo, FFCLRP, Departamento de Física, Ribeirão Preto, São Paulo 14040-901, Brazil
| | - Osame Kinouchi
- Universidade de São Paulo, FFCLRP, Departamento de Física, Ribeirão Preto, São Paulo 14040-901, Brazil
| | - Ariadne A Costa
- Grupo de Redes Complexas Aplicadas de Jataí, Universidade Federal de Jataí, Jataí, GO 75801-615, Brazil
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42
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Bansal K, Garcia JO, Lauharatanahirun N, Muldoon SF, Sajda P, Vettel JM. Scale-specific dynamics of high-amplitude bursts in EEG capture behaviorally meaningful variability. Neuroimage 2021; 241:118425. [PMID: 34303795 DOI: 10.1016/j.neuroimage.2021.118425] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/25/2021] [Accepted: 07/21/2021] [Indexed: 10/20/2022] Open
Abstract
Cascading high-amplitude bursts in neural activity, termed avalanches, are thought to provide insight into the complex spatially distributed interactions in neural systems. In human neuroimaging, for example, avalanches occurring during resting-state show scale-invariant dynamics, supporting the hypothesis that the brain operates near a critical point that enables long range spatial communication. In fact, it has been suggested that such scale-invariant dynamics, characterized by a power-law distribution in these avalanches, are universal in neural systems and emerge through a common mechanism. While the analysis of avalanches and subsequent criticality is increasingly seen as a framework for using complex systems theory to understand brain function, it is unclear how the framework would account for the omnipresent cognitive variability, whether across individuals or tasks. To address this, we analyzed avalanches in the EEG activity of healthy humans during rest as well as two distinct task conditions that varied in cognitive demands and produced behavioral measures unique to each individual. In both rest and task conditions we observed that avalanche dynamics demonstrate scale-invariant characteristics, but differ in their specific features, demonstrating individual variability. Using a new metric we call normalized engagement, which estimates the likelihood for a brain region to produce high-amplitude bursts, we also investigated regional features of avalanche dynamics. Normalized engagement showed not only the expected individual and task dependent variability, but also scale-specificity that correlated with individual behavior. Our results suggest that the study of avalanches in human brain activity provides a tool to assess cognitive variability. Our findings expand our understanding of avalanche features and are supportive of the emerging theoretical idea that the dynamics of an active human brain operate close to a critical-like region and not a singular critical-state.
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Affiliation(s)
- Kanika Bansal
- Human Research and Engineering Directorate, US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA; Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
| | - Javier O Garcia
- Human Research and Engineering Directorate, US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
| | - Nina Lauharatanahirun
- Department of Biomedical Engineering and Department of Biobehavioral Health, Pennsylvania State University, State College, PA 16802, USA
| | - Sarah F Muldoon
- Mathematics Department, CDSE Program, and Neuroscience Program, University at Buffalo, SUNY, Buffalo, NY 14260, USA
| | - Paul Sajda
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA; Data Science Institute, Columbia University, New York, NY 10027, USA
| | - Jean M Vettel
- Human Research and Engineering Directorate, US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA 93106, USA
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43
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Argilaga A, Papachristos E. Bounding the Multi-Scale Domain in Numerical Modelling and Meta-Heuristics Optimization: Application to Poroelastic Media with Damageable Cracks. MATERIALS 2021; 14:ma14143974. [PMID: 34300893 PMCID: PMC8304150 DOI: 10.3390/ma14143974] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 07/08/2021] [Accepted: 07/13/2021] [Indexed: 11/16/2022]
Abstract
It is very common for natural or synthetic materials to be characterized by a periodic or quasi-periodic micro-structure. This micro-structure, under the different loading conditions may play an important role on the apparent, macroscopic behaviour of the material. Although, fine, detailed information can be implemented at the micro-structure level, it still remains a challenging task to obtain experimental metrics at this scale. In this work, a constitutive law obtained by the asymptotic homogenization of a cracked, damageable, poroelastic medium is first evaluated for multi-scale use. For a given range of micro-scale parameters, due to the complex mechanical behaviour at micro-scale, such multi-scale approaches are needed to describe the (macro) material's behaviour. To overcome possible limitations regarding input data, meta-heuristics are used to calibrate the micro-scale parameters targeted on a synthetic failure envelope. Results show the validity of the approach to model micro-fractured materials such as coal or crystalline rocks.
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Affiliation(s)
- Albert Argilaga
- MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, China
- Correspondence: ; Tel.: +86-1776-709-0154
| | - Efthymios Papachristos
- Ecole Centrale de Nantes, Institut de Recherche en Génie Civil (GeM), UMR6183, 44321 Nantes, France;
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44
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Lotfi N, Fontenele AJ, Feliciano T, Aguiar LAA, de Vasconcelos NAP, Soares-Cunha C, Coimbra B, Rodrigues AJ, Sousa N, Copelli M, Carelli PV. Signatures of brain criticality unveiled by maximum entropy analysis across cortical states. Phys Rev E 2021; 102:012408. [PMID: 32795006 DOI: 10.1103/physreve.102.012408] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 07/01/2020] [Indexed: 11/07/2022]
Abstract
It has recently been reported that statistical signatures of brain criticality, obtained from distributions of neuronal avalanches, can depend on the cortical state. We revisit these claims with a completely different and independent approach, employing a maximum entropy model to test whether signatures of criticality appear in urethane-anesthetized rats. To account for the spontaneous variation of cortical states, we parse the time series and perform the maximum entropy analysis as a function of the variability of the population spiking activity. To compare data sets with different numbers of neurons, we define a normalized distance to criticality that takes into account the peak and width of the specific heat curve. We found a universal collapse of the normalized distance to criticality dependence on the cortical state, on an animal by animal basis. This indicates a universal dynamics and a critical point at an intermediate value of spiking variability.
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Affiliation(s)
- Nastaran Lotfi
- Departamento de Física, Universidade Federal de Pernambuco, Recife, PE 50670-901, Brazil
| | - Antonio J Fontenele
- Departamento de Física, Universidade Federal de Pernambuco, Recife, PE 50670-901, Brazil
| | - Thaís Feliciano
- Departamento de Física, Universidade Federal de Pernambuco, Recife, PE 50670-901, Brazil
| | - Leandro A A Aguiar
- Departamento de Física, Universidade Federal de Pernambuco, Recife, PE 50670-901, Brazil
| | - Nivaldo A P de Vasconcelos
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, 4710-057, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, 4806-909, Portugal
| | - Carina Soares-Cunha
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, 4710-057, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, 4806-909, Portugal
| | - Bárbara Coimbra
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, 4710-057, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, 4806-909, Portugal
| | - Ana João Rodrigues
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, 4710-057, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, 4806-909, Portugal
| | - Nuno Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, 4710-057, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, 4806-909, Portugal
| | - Mauro Copelli
- Departamento de Física, Universidade Federal de Pernambuco, Recife, PE 50670-901, Brazil
| | - Pedro V Carelli
- Departamento de Física, Universidade Federal de Pernambuco, Recife, PE 50670-901, Brazil
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45
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Spitzner FP, Dehning J, Wilting J, Hagemann A, P. Neto J, Zierenberg J, Priesemann V. MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity. PLoS One 2021; 16:e0249447. [PMID: 33914774 PMCID: PMC8084202 DOI: 10.1371/journal.pone.0249447] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 03/18/2021] [Indexed: 11/23/2022] Open
Abstract
Here we present our Python toolbox "MR. Estimator" to reliably estimate the intrinsic timescale from electrophysiologal recordings of heavily subsampled systems. Originally intended for the analysis of time series from neuronal spiking activity, our toolbox is applicable to a wide range of systems where subsampling-the difficulty to observe the whole system in full detail-limits our capability to record. Applications range from epidemic spreading to any system that can be represented by an autoregressive process. In the context of neuroscience, the intrinsic timescale can be thought of as the duration over which any perturbation reverberates within the network; it has been used as a key observable to investigate a functional hierarchy across the primate cortex and serves as a measure of working memory. It is also a proxy for the distance to criticality and quantifies a system's dynamic working point.
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Affiliation(s)
- F. P. Spitzner
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - J. Dehning
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - J. Wilting
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - A. Hagemann
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - J. P. Neto
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - J. Zierenberg
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - V. Priesemann
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
- Bernstein-Center for Computational Neuroscience (BCCN) Göttingen, Göttingen, Germany
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46
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Xenakis MN, Kapetis D, Yang Y, Gerrits MM, Heijman J, Waxman SG, Lauria G, Faber CG, Westra RL, Lindsey PJ, Smeets HJ. Hydropathicity-based prediction of pain-causing NaV1.7 variants. BMC Bioinformatics 2021; 22:212. [PMID: 33892629 PMCID: PMC8063372 DOI: 10.1186/s12859-021-04119-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 04/01/2021] [Indexed: 11/10/2022] Open
Abstract
Background Mutation-induced variations in the functional architecture of the NaV1.7 channel protein are causally related to a broad spectrum of human pain disorders. Predicting in silico the phenotype of NaV1.7 variant is of major clinical importance; it can aid in reducing costs of in vitro pathophysiological characterization of NaV1.7 variants, as well as, in the design of drug agents for counteracting pain-disease symptoms. Results In this work, we utilize spatial complexity of hydropathic effects toward predicting which NaV1.7 variants cause pain (and which are neutral) based on the location of corresponding mutation sites within the NaV1.7 structure. For that, we analyze topological and scaling hydropathic characteristics of the atomic environment around NaV1.7’s pore and probe their spatial correlation with mutation sites. We show that pain-related mutation sites occupy structural locations in proximity to a hydrophobic patch lining the pore while clustering at a critical hydropathic-interactions distance from the selectivity filter (SF). Taken together, these observations can differentiate pain-related NaV1.7 variants from neutral ones, i.e., NaV1.7 variants not causing pain disease, with 80.5\documentclass[12pt]{minimal}
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\begin{document}$$\%$$\end{document}% specificity [area under the receiver operating characteristics curve = 0.872]. Conclusions Our findings suggest that maintaining hydrophobic NaV1.7 interior intact, as well as, a finely-tuned (dictated by hydropathic interactions) distance from the SF might be necessary molecular conditions for physiological NaV1.7 functioning. The main advantage for using the presented predictive scheme is its negligible computational cost, as well as, hydropathicity-based biophysical rationalization. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04119-2.
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Affiliation(s)
- Makros N Xenakis
- Department of Toxicogenomics, Section Clinical Genomics, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands. .,Research School for Mental Health and Neuroscience (MHeNS), Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands.
| | - Dimos Kapetis
- Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico "Carlo Besta", Via Celoria 11, 20133, Milan, Italy
| | - Yang Yang
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University College of Pharmacy, West Lafayette, IN, 47907, USA.,Purdue Institute for Integrative Neuroscience, West Lafayette, IN, 47907, USA
| | - Monique M Gerrits
- Department of Clinical Genetics, Maastricht University Medical Center, PO box 5800, 6202 AZ, Maastricht, The Netherlands
| | - Jordi Heijman
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands
| | - Stephen G Waxman
- Department of Neurology and Center for Neuroscience and Regeneration Research, Yale University School of Medicine, New Haven, CT, 06510, USA.,Rehabilitation Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Giuseppe Lauria
- Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico "Carlo Besta", Via Celoria 11, 20133, Milan, Italy.,Department of Biomedical and Clinical Sciences "Luigi Sacco", University of Milan, Via G.B. Grassi 74, 20157, Milan, Italy
| | - Catharina G Faber
- Department of Neurology, Maastricht University Medical Center, PO Box 5800, 6202 AZ, Maastricht, The Netherlands
| | - Ronald L Westra
- Department of Data Science and Knowledge Engineering, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands
| | - Patrick J Lindsey
- Department of Toxicogenomics, Section Clinical Genomics, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands.,Research School for Oncology and Developmental Biology (GROW), Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands
| | - Hubert J Smeets
- Department of Toxicogenomics, Section Clinical Genomics, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands.,Research School for Mental Health and Neuroscience (MHeNS), Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands
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47
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Is There Sufficient Evidence for Criticality in Cortical Systems? eNeuro 2021; 8:ENEURO.0551-20.2021. [PMID: 33811087 PMCID: PMC8059881 DOI: 10.1523/eneuro.0551-20.2021] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/09/2021] [Accepted: 03/09/2021] [Indexed: 11/21/2022] Open
Abstract
Numerous studies have proposed that specific brain activity statistics provide evidence that the brain operates at a critical point, which could have implications for the brain’s information processing capabilities. A recent paper reported that identical scalings and criticality signatures arise in a variety of different neural systems (neural cultures, cortical slices, anesthetized or awake brains, across both reptiles and mammals). The diversity of these states calls into question the claimed role of criticality in information processing. We analyze the methodology used to assess criticality and replicate this analysis for spike trains of two non-critical systems. These two non-critical systems pass all the tests used to assess criticality in the aforementioned recent paper. This analysis provides a crucial control (which is absent from the original study) and suggests that the methodology used may not be sufficient to establish that a system operates at criticality. Hence whether the brain operates at criticality or not remains an open question and it is of evident interest to develop more robust methods to address these questions.
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48
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Heiney K, Huse Ramstad O, Fiskum V, Christiansen N, Sandvig A, Nichele S, Sandvig I. Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation. Front Comput Neurosci 2021; 15:611183. [PMID: 33643017 PMCID: PMC7902700 DOI: 10.3389/fncom.2021.611183] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 01/18/2021] [Indexed: 01/03/2023] Open
Abstract
It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed "neuronal avalanches." The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.
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Affiliation(s)
- Kristine Heiney
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Ola Huse Ramstad
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Vegard Fiskum
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Nicholas Christiansen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Axel Sandvig
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Clinical Neuroscience, Umeå University Hospital, Umeå, Sweden
- Department of Neurology, St. Olav's Hospital, Trondheim, Norway
| | - Stefano Nichele
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
- Department of Holistic Systems, Simula Metropolitan, Oslo, Norway
| | - Ioanna Sandvig
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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49
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Kim H, Valentini G, Hanson J, Walker SI. Informational architecture across non-living and living collectives. Theory Biosci 2021; 140:325-341. [PMID: 33532895 PMCID: PMC8629804 DOI: 10.1007/s12064-020-00331-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 11/12/2020] [Indexed: 11/24/2022]
Abstract
Collective behavior is widely regarded as a hallmark property of living and intelligent systems. Yet, many examples are known of simple physical systems that are not alive, which nonetheless display collective behavior too, prompting simple physical models to often be adopted to explain living collective behaviors. To understand collective behavior as it occurs in living examples, it is important to determine whether or not there exist fundamental differences in how non-living and living systems act collectively, as well as the limits of the intuition that can be built from simpler, physical examples in explaining biological phenomenon. Here, we propose a framework for comparing non-living and living collectives as a continuum based on their information architecture: that is, how information is stored and processed across different degrees of freedom. We review diverse examples of collective phenomena, characterized from an information-theoretic perspective, and offer views on future directions for quantifying living collective behaviors based on their informational structure.
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Affiliation(s)
- Hyunju Kim
- Beyond Center for Fundamental Concepts in Science, Arizona State University, Tempe, AZ, USA
- School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA
- ASU-SFI Center for Biosocial Complex Systems, Arizona State University and Santa Fe Institute, Tempe, USA
| | - Gabriele Valentini
- School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Jake Hanson
- Beyond Center for Fundamental Concepts in Science, Arizona State University, Tempe, AZ, USA
- School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA
| | - Sara Imari Walker
- Beyond Center for Fundamental Concepts in Science, Arizona State University, Tempe, AZ, USA.
- School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA.
- ASU-SFI Center for Biosocial Complex Systems, Arizona State University and Santa Fe Institute, Tempe, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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50
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Aguilera M, Di Paolo EA. Critical integration in neural and cognitive systems: Beyond power-law scaling as the hallmark of soft assembly. Neurosci Biobehav Rev 2021; 123:230-237. [PMID: 33485887 DOI: 10.1016/j.neubiorev.2021.01.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 12/10/2020] [Accepted: 01/07/2021] [Indexed: 11/20/2022]
Abstract
Inspired by models of self-organized criticality, a family of measures quantifies long-range correlations in neural and behavioral activity in the form of self-similar (e.g., power-law scaled) patterns across a range of scales. Long-range correlations are often taken as evidence that a system is near a critical transition, suggesting interaction-dominant, softly assembled relations between its parts. Psychologists and neuroscientists frequently use power-law scaling as evidence of critical regimes and soft assembly in neural and cognitive activity. Critics, however, argue that this methodology operates at most at the level of an analogy between cognitive and other natural phenomena. This is because power-laws do not provide information about a particular system's organization or what makes it specifically cognitive. We respond to this criticism using recent work in Integrated Information Theory. We propose a more principled understanding of criticality as a system's susceptibility to changes in its own integration, a property cognitive agents are expected to manifest. We contrast critical integration with power-law measures and find the former more informative about the underlying processes.
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Affiliation(s)
- Miguel Aguilera
- IAS-Research Center for Life, Mind and Society, Department of Logic and Philosophy of Science, University of the Basque Country, Donostia, Spain; Department of Informatics & Sussex Neuroscience, University of Sussex, Falmer, Brighton, UK; ISAAC Lab, Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
| | - Ezequiel A Di Paolo
- IAS-Research Center for Life, Mind and Society, Department of Logic and Philosophy of Science, University of the Basque Country, Donostia, Spain; Ikerbasque, Basque Foundation for Science, Bizkaia, Spain; Centre for Computational Neuroscience and Robotics, Department of Informatics, University of Sussex, Falmer, Brighton, UK
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