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Yang H, Han F, Wang Q. A large-scale neuronal network modelling study: Stimulus size modulates gamma oscillations in the primary visual cortex by long-range connections. Eur J Neurosci 2024. [PMID: 38812400 DOI: 10.1111/ejn.16429] [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: 10/06/2023] [Revised: 05/04/2024] [Accepted: 05/13/2024] [Indexed: 05/31/2024]
Abstract
Stimulus size modulation of neuronal firing activity is a fundamental property of the primary visual cortex. Numerous biological experiments have shown that stimulus size modulation is affected by multiple factors at different spatiotemporal scales, but the exact pathways and mechanisms remain incompletely understood. In this paper, we establish a large-scale neuronal network model of primary visual cortex with layer 2/3 to study how gamma oscillation properties are modulated by stimulus size and especially how long-range connections affect the modulation as realistic neuronal properties and spatial distributions of synaptic connections are considered. It is shown that long-range horizontal synaptic connections are sufficient to produce dimensional modulation of firing rates and gamma oscillations. In particular, with increasing grating stimulus size, the firing rate increases and then decreases, the peak frequency of gamma oscillations decreases and the spectral power increases. These are consistent with biological experimental observations. Furthermore, we explain in detail how the number and spatial distribution of long-range connections affect the size modulation of gamma oscillations by using the analysis of neuronal firing activity and synaptic current fluctuations. Our results provide a mechanism explanation for size modulation of gamma oscillations in the primary visual cortex and reveal the important and unique role played by long-range connections, which contributes to a deeper understanding of the cognitive function of gamma oscillations in visual cortex.
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Affiliation(s)
- Hao Yang
- College of Information Science and Technology, Donghua University, Shanghai, China
| | - Fang Han
- College of Information Science and Technology, Donghua University, Shanghai, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing, China
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2
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Khatri R, Machart P, Bonn S. DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation. Genome Biol 2024; 25:112. [PMID: 38689377 PMCID: PMC11061925 DOI: 10.1186/s13059-024-03251-5] [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: 07/10/2023] [Accepted: 04/17/2024] [Indexed: 05/02/2024] Open
Abstract
Cell deconvolution is the estimation of cell type fractions and cell type-specific gene expression from mixed data. An unmet challenge in cell deconvolution is the scarcity of realistic training data and the domain shift often observed in synthetic training data. Here, we show that two novel deep neural networks with simultaneous consistency regularization of the target and training domains significantly improve deconvolution performance. Our algorithm, DISSECT, outperforms competing algorithms in cell fraction and gene expression estimation by up to 14 percentage points. DISSECT can be easily adapted to other biomedical data types, as exemplified by our proteomic deconvolution experiments.
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Affiliation(s)
- Robin Khatri
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Pierre Machart
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Bonn
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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3
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Borzou A, Miller SN, Hommel JD, Schwarz JM. Cocaine diminishes functional network robustness and destabilizes the energy landscape of neuronal activity in the medial prefrontal cortex. PNAS NEXUS 2024; 3:pgae092. [PMID: 38476665 PMCID: PMC10929585 DOI: 10.1093/pnasnexus/pgae092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 02/13/2024] [Indexed: 03/14/2024]
Abstract
We present analysis of neuronal activity recordings from a subset of neurons in the medial prefrontal cortex of rats before and after the administration of cocaine. Using an underlying modern Hopfield model as a description for the neuronal network, combined with a machine learning approach, we compute the underlying functional connectivity of the neuronal network. We find that the functional connectivity changes after the administration of cocaine with both functional-excitatory and functional-inhibitory neurons being affected. Using conventional network analysis, we find that the diameter of the graph, or the shortest length between the two most distant nodes, increases with cocaine, suggesting that the neuronal network is less robust. We also find that the betweenness centrality scores for several of the functional-excitatory and functional-inhibitory neurons decrease significantly, while other scores remain essentially unchanged, to also suggest that the neuronal network is less robust. Finally, we study the distribution of neuronal activity and relate it to energy to find that cocaine drives the neuronal network towards destabilization in the energy landscape of neuronal activation. While this destabilization is presumably temporary given one administration of cocaine, perhaps this initial destabilization indicates a transition towards a new stable state with repeated cocaine administration. However, such analyses are useful more generally to understand how neuronal networks respond to perturbations.
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Affiliation(s)
- Ahmad Borzou
- Department of Physics and BioInspired Institute, Syracuse University, Syracuse, NY 13244, USA
- CompuFlair, Houston, TX 77064, USA
| | - Sierra N Miller
- Department of Pharmacology and Toxicology, Center for Addiction Sciences and Therapeutics, University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Jonathan D Hommel
- Department of Pharmacology and Toxicology, Center for Addiction Sciences and Therapeutics, University of Texas Medical Branch, Galveston, TX 77555, USA
| | - J M Schwarz
- Department of Physics and BioInspired Institute, Syracuse University, Syracuse, NY 13244, USA
- Indian Creek Farm, Ithaca, NY 14850, USA
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4
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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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5
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Hwang S, Jun SB. Ultrasound neuromodulation of cultured hippocampal neurons. Biomed Eng Lett 2024; 14:79-89. [PMID: 38186947 PMCID: PMC10769976 DOI: 10.1007/s13534-023-00314-7] [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: 05/25/2023] [Revised: 07/24/2023] [Accepted: 08/14/2023] [Indexed: 01/09/2024] Open
Abstract
Ultrasound is becoming an emerging and promising method for neuromodulation due to its advantage of noninvasiveness and its high spatial resolution. However, the underlying principles of ultrasound neuromodulation have not yet been elucidated. We have herein developed a new in vitro setup to study the ultrasonic neuromodulation, and examined various parameters of ultrasound to verify the effective conditions to evoke the neural activity. Neurons were stimulated with 0.5 MHz center frequency ultrasound, and the action potentials were recorded from rat hippocampal neural cells cultured on microelectrode arrays. As the intensity of ultrasound increased, the neuronal activity also increased. There was a notable and significant increase in both the spike rate and the number of bursts at 50% duty cycle, 1 kHz pulse repetition frequency, and the acoustic intensities of 7.6 W/cm2 and 3.8 W/cm2 in terms of spatial-peak pulse-average intensity and spatial-peak temporal-average intensity, respectively. In addition, the impact of ultrasonic neuromodulation was assessed in the presence of a gamma-aminobutyric acid A (GABAA) receptor antagonist to exclude the effect of activated inhibitory neurons. Interestingly, it is noteworthy that the predominant neuromodulatory effects of ultrasound disappeared when the GABAA blocker was introduced, suggesting the potential of ultrasonic stimulation specifically targeting inhibitory neurons. The experimental setup proposed herein could serve as a useful tool for the clarification of the mechanisms underlying the electrophysiological effects of ultrasound.
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Affiliation(s)
- Seoyoung Hwang
- Department of Electronic and Electrical Engineering, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760 Republic of Korea
- Seoul National University Hospital Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sang Beom Jun
- Department of Electronic and Electrical Engineering, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760 Republic of Korea
- Graduate Program in Smart Factory, Ewha Womans University, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, Republic of Korea
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Chen L, Yu C, Zhai J. How network structure affects the dynamics of a network of stochastic spiking neurons. CHAOS (WOODBURY, N.Y.) 2023; 33:093101. [PMID: 37656915 DOI: 10.1063/5.0164207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/14/2023] [Indexed: 09/03/2023]
Abstract
Up to now, it still remains an open question about the relation between the structure of brain networks and their functions. The effects of structure on the dynamics of neural networks are usually investigated via extensive numerical simulations, while analytical analysis is always very difficult and thus rare. In this work, we explored the effects of a random regular graph on the dynamics of a neural network of stochastic spiking neurons, which has a bistable region when fully connected. We showed by numerical simulations that as the number of each neuron's neighbors decreases, the bistable region shrinks and eventually seems to disappear, and a critical-like transition appears. In the meantime, we made analytical analysis that explains numerical results. We hope this would give some insights into how structure affects the dynamics of neural networks from a theoretical perspective, rather than merely by numerical simulations.
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Affiliation(s)
- Lei Chen
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Chaojun Yu
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jian Zhai
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
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Wang M, Yu X. Experience-dependent structural plasticity of pyramidal neurons in the developing sensory cortices. Curr Opin Neurobiol 2023; 81:102724. [PMID: 37068383 DOI: 10.1016/j.conb.2023.102724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 03/16/2023] [Accepted: 03/17/2023] [Indexed: 04/19/2023]
Abstract
Sensory experience regulates the structural and functional wiring of neuronal circuits, during development and throughout adulthood. Here, we review current knowledge of how experience affects structural plasticity of pyramidal neurons in the sensory cortices. We discuss the pros and cons of existing labeling approaches, as well as what structural parameters are most plastic. We further discuss how recent advances in sparse labeling of specific neuronal subtypes, as well as development of techniques that allow fast, high resolution imaging in large fields, would enable future studies to address currently unanswered questions in the field of structural plasticity.
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Affiliation(s)
- Miao Wang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, and PKU-IDG/McGovern Institute, Peking University, Beijing 100871, China.
| | - Xiang Yu
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, and PKU-IDG/McGovern Institute, Peking University, Beijing 100871, China; Autism Research Center of Peking University Health Science Center, Beijing 100191, China; Chinese Institute for Brain Research, Beijing 102206, China.
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Stasenko SV, Kazantsev VB. Information Encoding in Bursting Spiking Neural Network Modulated by Astrocytes. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25050745. [PMID: 37238500 DOI: 10.3390/e25050745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/28/2023] [Accepted: 04/28/2023] [Indexed: 05/28/2023]
Abstract
We investigated a mathematical model composed of a spiking neural network (SNN) interacting with astrocytes. We analysed how information content in the form of two-dimensional images can be represented by an SNN in the form of a spatiotemporal spiking pattern. The SNN includes excitatory and inhibitory neurons in some proportion, sustaining the excitation-inhibition balance of autonomous firing. The astrocytes accompanying each excitatory synapse provide a slow modulation of synaptic transmission strength. An information image was uploaded to the network in the form of excitatory stimulation pulses distributed in time reproducing the shape of the image. We found that astrocytic modulation prevented stimulation-induced SNN hyperexcitation and non-periodic bursting activity. Such homeostatic astrocytic regulation of neuronal activity makes it possible to restore the image supplied during stimulation and lost in the raster diagram of neuronal activity due to non-periodic neuronal firing. At a biological point, our model shows that astrocytes can act as an additional adaptive mechanism for regulating neural activity, which is crucial for sensory cortical representations.
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Affiliation(s)
- Sergey V Stasenko
- Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia
| | - Victor B Kazantsev
- Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia
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Chen L, Yu C, Zhai J. Self-organized collective oscillations in networks of stochastic spiking neurons. CHAOS (WOODBURY, N.Y.) 2023; 33:023119. [PMID: 36859226 DOI: 10.1063/5.0130075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
The theory of self-organized bistability (SOB) is the counterpart of self-organized criticality for systems tuning themselves to the edge of bistability of a discontinuous phase transition, rather than to the critical point of a continuous one. As far as we are concerned, there are currently few neural network models that display SOB or rather its non-conservative version, self-organized collective oscillations (SOCO). We show that by slightly modifying the firing function, a stochastic excitatory/inhibitory network model can display SOCO behaviors, thus providing some insights into how SOCO behaviors can be generated in neural network models.
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Affiliation(s)
- Lei Chen
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Chaojun Yu
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jian Zhai
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
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