1
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Kaboodvand N, Karimi H, Iravani B. Preparatory activity of anterior insula predicts conflict errors: integrating convolutional neural networks and neural mass models. Sci Rep 2024; 14:16682. [PMID: 39030222 PMCID: PMC11271609 DOI: 10.1038/s41598-024-67034-5] [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: 01/16/2024] [Accepted: 07/08/2024] [Indexed: 07/21/2024] Open
Abstract
Preparatory brain activity is a cornerstone of proactive cognitive control, a top-down process optimizing attention, perception, and inhibition, fostering cognitive flexibility and adaptive attention control in the human brain. In this study, we proposed a neuroimaging-informed convolutional neural network model to predict cognitive control performance from the baseline pre-stimulus preparatory electrophysiological activity of core cognitive control regions. Particularly, combined with perturbation-based occlusion sensitivity analysis, we pinpointed regions with the most predictive preparatory activity for proactive cognitive control. We found that preparatory arrhythmic broadband neural dynamics in the right anterior insula, right precentral gyrus, and the right opercular part of inferior frontal gyrus (posterior ventrolateral prefrontal cortex), are highly predictive of prospective cognitive control performance. The pre-stimulus preparatory activity in these regions corresponds to readiness for conflict detection, inhibitory control, and overall elaborate attentional processing. We integrated the convolutional neural network with biologically inspired Jansen-Rit neural mass model to investigate neurostimulation effects on cognitive control. High-frequency stimulation (130 Hz) of the left anterior insula provides significant cognitive enhancement, especially in reducing conflict errors, despite the right anterior insula's higher predictive value for prospective cognitive control performance. Thus, effective neurostimulation targets may differ from regions showing biomarker activity. Finally, we validated our theoretical finding by evaluating intrinsic neuromodulation through neurofeedback-guided volitional control in an independent dataset. We found that left anterior insula was intrinsically modulated in real-time by volitional control of emotional valence, but not arousal. Our findings further highlight central role of anterior insula in orchestrating proactive cognitive control processes, positioning it at the top of hierarchy for cognitive control.
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Affiliation(s)
- Neda Kaboodvand
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Hanie Karimi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Behzad Iravani
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA.
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2
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Shavikloo M, Esmaeili A, Valizadeh A, Madadi Asl M. Synchronization of delayed coupled neurons with multiple synaptic connections. Cogn Neurodyn 2024; 18:631-643. [PMID: 38699603 PMCID: PMC11061096 DOI: 10.1007/s11571-023-10013-9] [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: 04/17/2023] [Revised: 08/16/2023] [Accepted: 09/16/2023] [Indexed: 05/05/2024] Open
Abstract
Synchronization is a key feature of the brain dynamics and is necessary for information transmission across brain regions and in higher brain functions like cognition, learning and memory. Experimental findings demonstrated that in cortical microcircuits there are multiple synapses between pairs of connected neurons. Synchronization of neurons in the presence of multiple synaptic connections may be relevant for optimal learning and memory, however, its effect on the dynamics of the neurons is not adequately studied. Here, we address the question that how changes in the strength of the synaptic connections and transmission delays between neurons impact synchronization in a two-neuron system with multiple synapses. To this end, we analytically and computationally investigated synchronization dynamics by considering both phase oscillator model and conductance-based Hodgkin-Huxley (HH) model. Our results show that symmetry/asymmetry of feedforward and feedback connections crucially determines stability of the phase locking of the system based on the strength of connections and delays. In both models, the two-neuron system with multiple synapses achieves in-phase synchrony in the presence of small and large delays, whereas an anti-phase synchronization state is favored for median delays. Our findings can expand the understanding of the functional role of multisynaptic contacts in neuronal synchronization and may shed light on the dynamical consequences of pathological multisynaptic connectivity in a number of brain disorders.
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Affiliation(s)
- Masoumeh Shavikloo
- Department of Physics, Faculty of Science, Urmia University, Urmia, Iran
| | - Asghar Esmaeili
- Department of Physics, Faculty of Science, Urmia University, Urmia, Iran
| | - Alireza Valizadeh
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
- Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran
| | - Mojtaba Madadi Asl
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran
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3
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Madadi Asl M, Ramezani Akbarabadi S. Delay-dependent transitions of phase synchronization and coupling symmetry between neurons shaped by spike-timing-dependent plasticity. Cogn Neurodyn 2023; 17:523-536. [PMID: 37007192 PMCID: PMC10050303 DOI: 10.1007/s11571-022-09850-x] [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: 10/16/2021] [Revised: 05/24/2022] [Accepted: 07/06/2022] [Indexed: 11/03/2022] Open
Abstract
Synchronization plays a key role in learning and memory by facilitating the communication between neurons promoted by synaptic plasticity. Spike-timing-dependent plasticity (STDP) is a form of synaptic plasticity that modifies the strength of synaptic connections between neurons based on the coincidence of pre- and postsynaptic spikes. In this way, STDP simultaneously shapes the neuronal activity and synaptic connectivity in a feedback loop. However, transmission delays due to the physical distance between neurons affect neuronal synchronization and the symmetry of synaptic coupling. To address the question that how transmission delays and STDP can jointly determine the emergent pairwise activity-connectivity patterns, we studied phase synchronization properties and coupling symmetry between two bidirectionally coupled neurons using both phase oscillator and conductance-based neuron models. We show that depending on the range of transmission delays, the activity of the two-neuron motif can achieve an in-phase/anti-phase synchronized state and its connectivity can attain a symmetric/asymmetric coupling regime. The coevolutionary dynamics of the neuronal system and the synaptic weights due to STDP stabilizes the motif in either one of these states by transitions between in-phase/anti-phase synchronization states and symmetric/asymmetric coupling regimes at particular transmission delays. These transitions crucially depend on the phase response curve (PRC) of the neurons, but they are relatively robust to the heterogeneity of transmission delays and potentiation-depression imbalance of the STDP profile.
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Affiliation(s)
- Mojtaba Madadi Asl
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, 19395-5531 Iran
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4
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Roohi N, Valizadeh A. Role of Interaction Delays in the Synchronization of Inhibitory Networks. Neural Comput 2022; 34:1425-1447. [PMID: 35534004 DOI: 10.1162/neco_a_01500] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 01/25/2022] [Indexed: 11/04/2022]
Abstract
Neural oscillations provide a means for efficient and flexible communication among different brain areas. Understanding the mechanisms of the generation of brain oscillations is crucial to determine principles of communication and information transfer in the brain circuits. It is well known that the inhibitory neurons play a major role in the generation of oscillations in the gamma range, in pure inhibitory networks, or in the networks composed of excitatory and inhibitory neurons. In this study, we explore the impact of different parameters and, in particular, the delay in the transmission of the signals between the neurons, on the dynamics of inhibitory networks. We show that increasing delay in a reasonable range increases the synchrony and stabilizes the oscillations. Unstable gamma oscillations characterized by a highly variable amplitude of oscillations can be observed in an intermediate range of delays. We show that in this range of delays, other experimentally observed phenomena such as sparse firing, variable amplitude and period, and the correlation between the instantaneous amplitude and period could be observed. The results broaden our understanding of the mechanism of the generation of the gamma oscillations in the inhibitory networks, known as the ING (interneuron-gamma) mechanism.
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Affiliation(s)
- Nariman Roohi
- Department of Physics, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
| | - Alireza Valizadeh
- Department of Physics, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran.,School of Biological Sciences, Institute for Research in Fundamental Sciences, Niavaran, Tehran, Iran
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5
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Sánchez-Claros J, Pariz A, Valizadeh A, Canals S, Mirasso CR. Information Transmission in Delay-Coupled Neuronal Circuits in the Presence of a Relay Population. Front Syst Neurosci 2021; 15:705371. [PMID: 34393731 PMCID: PMC8357994 DOI: 10.3389/fnsys.2021.705371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 06/30/2021] [Indexed: 12/04/2022] Open
Abstract
Synchronization between neuronal populations is hypothesized to play a crucial role in the communication between brain networks. The binding of features, or the association of computations occurring in spatially segregated areas, is supposed to take place when a stable synchronization between cortical areas occurs. While a direct cortico-cortical connection typically fails to support this mechanism, the participation of a third area, a relay element, mediating in the communication was proposed to overcome this limitation. Among the different structures that could play the role of coordination during the binding process, the thalamus is the best placed region to carry out this task. In this paper we study how information flows in a canonical motif that mimics a cortico-thalamo-cortical circuit composed by three mutually coupled neuronal populations (also called the V-motif). Through extensive numerical simulations, we found that the amount of information transferred between the oscillating neuronal populations is determined by the delay in their connections and the mismatch in their oscillation frequencies (detuning). While the transmission from a cortical population is mostly restricted to positive detuning, transmission from the relay (thalamic) population to the cortical populations is robust for a broad range of detuning values, including negative values, while permitting feedback communication from the cortex at high frequencies, thus supporting robust bottom up and top down interaction. In this case, a strong feedback transmission between the cortex to thalamus supports the possibility of robust bottom-up and top-down interactions in this motif. Interestingly, adding a cortico-cortical bidirectional connection to the V-motif (C-motif) expands the dynamics of the system with distinct operation modes. While overall transmission efficiency is decreased, new communication channels establish cortico-thalamo-cortical association loops. Switching between operation modes depends on the synaptic strength of the cortico-cortical connections. Our results support a role of the transthalamic V-motif in the binding of spatially segregated cortical computations, and suggest an important regulatory role of the direct cortico-cortical connection.
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Affiliation(s)
- Jaime Sánchez-Claros
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, UIB-CSIC), Campus UIB, Palma de Mallorca, Spain
| | - Aref Pariz
- Institute for Advanced Studies in Basic Sciences, Zanjan, Iran.,Department of Biology, University of Ottawa, Ottawa, ON, Canada
| | | | - Santiago Canals
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, Sant Joan d'Alacant, Spain
| | - Claudio R Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, UIB-CSIC), Campus UIB, Palma de Mallorca, Spain
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6
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Pariz A, Fischer I, Valizadeh A, Mirasso C. Transmission delays and frequency detuning can regulate information flow between brain regions. PLoS Comput Biol 2021; 17:e1008129. [PMID: 33857135 PMCID: PMC8049288 DOI: 10.1371/journal.pcbi.1008129] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 02/16/2021] [Indexed: 12/28/2022] Open
Abstract
Brain networks exhibit very variable and dynamical functional connectivity and flexible configurations of information exchange despite their overall fixed structure. Brain oscillations are hypothesized to underlie time-dependent functional connectivity by periodically changing the excitability of neural populations. In this paper, we investigate the role of the connection delay and the detuning between the natural frequencies of neural populations in the transmission of signals. Based on numerical simulations and analytical arguments, we show that the amount of information transfer between two oscillating neural populations could be determined by their connection delay and the mismatch in their oscillation frequencies. Our results highlight the role of the collective phase response curve of the oscillating neural populations for the efficacy of signal transmission and the quality of the information transfer in brain networks.
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Affiliation(s)
- Aref Pariz
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, Palma de Mallorca, Spain
| | - Ingo Fischer
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, Palma de Mallorca, Spain
| | - Alireza Valizadeh
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
- School of biological sciences, Institute for research in fundamental sciences (IPM), Tehran, Iran
- * E-mail: (AV); (CM)
| | - Claudio Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, Palma de Mallorca, Spain
- * E-mail: (AV); (CM)
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7
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Ziaeemehr A, Valizadeh A. Frequency-Resolved Functional Connectivity: Role of Delay and the Strength of Connections. Front Neural Circuits 2021; 15:608655. [PMID: 33841105 PMCID: PMC8024621 DOI: 10.3389/fncir.2021.608655] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 02/26/2021] [Indexed: 12/04/2022] Open
Abstract
The brain functional network extracted from the BOLD signals reveals the correlated activity of the different brain regions, which is hypothesized to underlie the integration of the information across functionally specialized areas. Functional networks are not static and change over time and in different brain states, enabling the nervous system to engage and disengage different local areas in specific tasks on demand. Due to the low temporal resolution, however, BOLD signals do not allow the exploration of spectral properties of the brain dynamics over different frequency bands which are known to be important in cognitive processes. Recent studies using imaging tools with a high temporal resolution has made it possible to explore the correlation between the regions at multiple frequency bands. These studies introduce the frequency as a new dimension over which the functional networks change, enabling brain networks to transmit multiplex of information at any time. In this computational study, we explore the functional connectivity at different frequency ranges and highlight the role of the distance between the nodes in their correlation. We run the generalized Kuramoto model with delayed interactions on top of the brain's connectome and show that how the transmission delay and the strength of the connections, affect the correlation between the pair of nodes over different frequency bands.
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Affiliation(s)
- Abolfazl Ziaeemehr
- Department of Physics, Institute of Advanced Studies in Basic Sciences, Zanjan, Iran
| | - Alireza Valizadeh
- Department of Physics, Institute of Advanced Studies in Basic Sciences, Zanjan, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran
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8
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Shunting Inhibition Improves Synchronization in Heterogeneous Inhibitory Interneuronal Networks with Type 1 Excitability Whereas Hyperpolarizing Inhibition Is Better for Type 2 Excitability. eNeuro 2020; 7:ENEURO.0464-19.2020. [PMID: 32198159 PMCID: PMC7210489 DOI: 10.1523/eneuro.0464-19.2020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 03/01/2020] [Accepted: 03/10/2020] [Indexed: 11/27/2022] Open
Abstract
All-to-all homogeneous networks of inhibitory neurons synchronize completely under the right conditions; however, many modeling studies have shown that biological levels of heterogeneity disrupt synchrony. Our fundamental scientific question is “how can neurons maintain partial synchrony in the presence of heterogeneity and noise?” A particular subset of strongly interconnected interneurons, the PV+ fast-spiking (FS) basket neurons, are strongly implicated in γ oscillations and in phase locking of nested γ oscillations to theta. Their excitability type apparently varies between brain regions: in CA1 and the dentate gyrus they have type 1 excitability, meaning that they can fire arbitrarily slowly, whereas in the striatum and cortex they have type 2 excitability, meaning that there is a frequency thresh old below which they cannot sustain repetitive firing. We constrained the models to study the effect of excitability type (more precisely bifurcation type) in isolation from all other factors. We use sparsely connected, heterogeneous, noisy networks with synaptic delays to show that synchronization properties, namely the resistance to suppression and the strength of theta phase to γ amplitude coupling, are strongly dependent on the pairing of excitability type with the type of inhibition. Shunting inhibition performs better for type 1 and hyperpolarizing inhibition for type 2. γ Oscillations and their nesting within theta oscillations are thought to subserve cognitive functions like memory encoding and recall; therefore, it is important to understand the contribution of intrinsic properties to these rhythms.
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9
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Moosavi SA, Montakhab A, Valizadeh A. Coexistence of scale-invariant and rhythmic behavior in self-organized criticality. Phys Rev E 2018; 98:022304. [PMID: 30253485 DOI: 10.1103/physreve.98.022304] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Indexed: 11/07/2022]
Abstract
Scale-free behavior as well as oscillations are frequently observed in the activity of many natural systems. One important example is the cortical tissues of mammalian brain where both phenomena are simultaneously observed. Rhythmic oscillations as well as critical (scale-free) dynamics are thought to be important, but theoretically incompatible, features of a healthy brain. Motivated by the above, we study the possibility of the coexistence of scale-free avalanches along with rhythmic behavior within the framework of self-organized criticality. In particular, we add an oscillatory perturbation to local threshold condition of the continuous Zhang model and characterize the subsequent activity of the system. We observe regular oscillations embedded in well-defined avalanches which exhibit scale-free size and duration in line with observed neuronal avalanches. The average amplitude of such oscillations are shown to decrease with increasing frequency consistent with real brain oscillations. Furthermore, it is shown that optimal amplification of oscillations occur at the critical point, further providing evidence for functional advantages of criticality.
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Affiliation(s)
- S Amin Moosavi
- Department of Physics, Institute for Advanced Studies in Basic Sciences, Zanjan 45137-66731, Iran.,Department of Physics, College of Sciences, Shiraz University, Shiraz 71946-84795, Iran
| | - Afshin Montakhab
- Department of Physics, College of Sciences, Shiraz University, Shiraz 71946-84795, Iran
| | - Alireza Valizadeh
- Department of Physics, Institute for Advanced Studies in Basic Sciences, Zanjan 45137-66731, Iran
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10
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Khoshkhou M, Montakhab A. Beta-Rhythm Oscillations and Synchronization Transition in Network Models of Izhikevich Neurons: Effect of Topology and Synaptic Type. Front Comput Neurosci 2018; 12:59. [PMID: 30154708 PMCID: PMC6103382 DOI: 10.3389/fncom.2018.00059] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 07/09/2018] [Indexed: 11/13/2022] Open
Abstract
Despite their significant functional roles, beta-band oscillations are least understood. Synchronization in neuronal networks have attracted much attention in recent years with the main focus on transition type. Whether one obtains explosive transition or a continuous transition is an important feature of the neuronal network which can depend on network structure as well as synaptic types. In this study we consider the effect of synaptic interaction (electrical and chemical) as well as structural connectivity on synchronization transition in network models of Izhikevich neurons which spike regularly with beta rhythms. We find a wide range of behavior including continuous transition, explosive transition, as well as lack of global order. The stronger electrical synapses are more conducive to synchronization and can even lead to explosive synchronization. The key network element which determines the order of transition is found to be the clustering coefficient and not the small world effect, or the existence of hubs in a network. These results are in contrast to previous results which use phase oscillator models such as the Kuramoto model. Furthermore, we show that the patterns of synchronization changes when one goes to the gamma band. We attribute such a change to the change in the refractory period of Izhikevich neurons which changes significantly with frequency.
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Affiliation(s)
- Mahsa Khoshkhou
- Department of Physics, College of Sciences, Shiraz University, Shiraz, Iran
| | - Afshin Montakhab
- Department of Physics, College of Sciences, Shiraz University, Shiraz, Iran
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11
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Delay-Induced Multistability and Loop Formation in Neuronal Networks with Spike-Timing-Dependent Plasticity. Sci Rep 2018; 8:12068. [PMID: 30104713 PMCID: PMC6089910 DOI: 10.1038/s41598-018-30565-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 08/02/2018] [Indexed: 12/16/2022] Open
Abstract
Spike-timing-dependent plasticity (STDP) adjusts synaptic strengths according to the precise timing of pre- and postsynaptic spike pairs. Theoretical and computational studies have revealed that STDP may contribute to the emergence of a variety of structural and dynamical states in plastic neuronal populations. In this manuscript, we show that by incorporating dendritic and axonal propagation delays in recurrent networks of oscillatory neurons, the asymptotic connectivity displays multistability, where different structures emerge depending on the initial distribution of the synaptic strengths. In particular, we show that the standard deviation of the initial distribution of synaptic weights, besides its mean, determines the main properties of the emergent structural connectivity such as the mean final synaptic weight, the number of two-neuron loops and the symmetry of the final structure. We also show that the firing rates of the neurons affect the evolution of the network, and a more symmetric configuration of the synapses emerges at higher firing rates. We justify the network results based on a two-neuron framework and show how the results translate to large recurrent networks.
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12
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Tomka T, Iber D, Boareto M. Travelling waves in somitogenesis: Collective cellular properties emerge from time-delayed juxtacrine oscillation coupling. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2018; 137:76-87. [PMID: 29702125 DOI: 10.1016/j.pbiomolbio.2018.04.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2018] [Revised: 04/09/2018] [Accepted: 04/12/2018] [Indexed: 11/18/2022]
Abstract
The sculpturing of the vertebrate body plan into segments begins with the sequential formation of somites in the presomitic mesoderm (PSM). The rhythmicity of this process is controlled by travelling waves of gene expression. These kinetic waves emerge from coupled cellular oscillators and sweep across the PSM. In zebrafish, the oscillations are driven by autorepression of her genes and are synchronized via Notch signalling. Mathematical modelling has played an important role in explaining how collective properties emerge from the molecular interactions. Increasingly more quantitative experimental data permits the validation of those mathematical models, yet leads to increasingly more complex model formulations that hamper an intuitive understanding of the underlying mechanisms. Here, we review previous efforts, and design a mechanistic model of the her1 oscillator, which represents the experimentally viable her7;hes6 double mutant. This genetically simplified system is ideally suited to conceptually recapitulate oscillatory entrainment and travelling wave formation, and to highlight open questions. It shows that three key parameters, the autorepression delay, the juxtacrine coupling delay, and the coupling strength, are sufficient to understand the emergence of the collective period, the collective amplitude, and the synchronization of neighbouring Her1 oscillators. Moreover, two spatiotemporal time delay gradients, in the autorepression and in the juxtacrine signalling, are required to explain the collective oscillatory dynamics and synchrony of PSM cells. The highlighted developmental principles likely apply more generally to other developmental processes, including neurogenesis and angiogenesis.
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Affiliation(s)
- Tomas Tomka
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Dagmar Iber
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland; Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058 Basel, Switzerland.
| | - Marcelo Boareto
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland; Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058 Basel, Switzerland.
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13
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Bolhasani E, Azizi Y, Valizadeh A, Perc M. Synchronization of oscillators through time-shifted common inputs. Phys Rev E 2017; 95:032207. [PMID: 28415346 DOI: 10.1103/physreve.95.032207] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Indexed: 06/07/2023]
Abstract
Shared upstream dynamical processes are frequently the source of common inputs in various physical and biological systems. However, due to finite signal transmission speeds and differences in the distance to the source, time shifts between otherwise common inputs are unavoidable. Since common inputs can be a source of correlation between the elements of multi-unit dynamical systems, regardless of whether these elements are directly connected with one another or not, it is of importance to understand their impact on synchronization. As a canonical model that is representative for a variety of different dynamical systems, we study limit-cycle oscillators that are driven by stochastic time-shifted common inputs. We show that if the oscillators are coupled, time shifts in stochastic common inputs do not simply shift the distribution of the phase differences, but rather the distribution actually changes as a result. The best synchronization is therefore achieved at a precise intermediate value of the time shift, which is due to a resonance-like effect with the most probable phase difference that is determined by the deterministic dynamics.
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Affiliation(s)
- Ehsan Bolhasani
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran
- School of Cognitive Science, Institute for Research in Fundamental Sciences (IPM), P.O. Box 1954851167, Tehran, Iran
| | - Yousef Azizi
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran
| | - Alireza Valizadeh
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran
- School of Cognitive Science, Institute for Research in Fundamental Sciences (IPM), P.O. Box 1954851167, Tehran, Iran
| | - Matjaž Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, SI-2000 Maribor, Slovenia
- CAMTP-Center for Applied Mathematics and Theoretical Physics, University of Maribor, Krekova 2, SI-2000 Maribor, Slovenia
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14
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Madadi Asl M, Valizadeh A, Tass PA. Dendritic and Axonal Propagation Delays Determine Emergent Structures of Neuronal Networks with Plastic Synapses. Sci Rep 2017; 7:39682. [PMID: 28045109 PMCID: PMC5206725 DOI: 10.1038/srep39682] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Accepted: 11/25/2016] [Indexed: 11/09/2022] Open
Abstract
Spike-timing-dependent plasticity (STDP) modifies synaptic strengths based on the relative timing of pre- and postsynaptic spikes. The temporal order of spikes turned out to be crucial. We here take into account how propagation delays, composed of dendritic and axonal delay times, may affect the temporal order of spikes. In a minimal setting, characterized by neglecting dendritic and axonal propagation delays, STDP eliminates bidirectional connections between two coupled neurons and turns them into unidirectional connections. In this paper, however, we show that depending on the dendritic and axonal propagation delays, the temporal order of spikes at the synapses can be different from those in the cell bodies and, consequently, qualitatively different connectivity patterns emerge. In particular, we show that for a system of two coupled oscillatory neurons, bidirectional synapses can be preserved and potentiated. Intriguingly, this finding also translates to large networks of type-II phase oscillators and, hence, crucially impacts on the overall hierarchical connectivity patterns of oscillatory neuronal networks.
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Affiliation(s)
- Mojtaba Madadi Asl
- Institute for Advanced Studies in Basic Sciences (IASBS), Department of Physics, Zanjan, 45195-1159, Iran
| | - Alireza Valizadeh
- Institute for Advanced Studies in Basic Sciences (IASBS), Department of Physics, Zanjan, 45195-1159, Iran.,Institute for Research in Fundamental Sciences (IPM), School of Cognitive Sciences, Tehran, 19395-5746, Iran
| | - Peter A Tass
- Institute of Neuroscience and Medicine - Neuromodulation (INM-7), Research Center Jülich, Jülich, 52425, Germany.,Stanford University, Department of Neurosurgery, Stanford, CA, 94305, USA.,University of Cologne, Department of Neuromodulation, Cologne, 50937, Germany
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15
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Esfahani ZG, Gollo LL, Valizadeh A. Stimulus-dependent synchronization in delayed-coupled neuronal networks. Sci Rep 2016; 6:23471. [PMID: 27001428 PMCID: PMC4802300 DOI: 10.1038/srep23471] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Accepted: 03/07/2016] [Indexed: 02/04/2023] Open
Abstract
Time delay is a general feature of all interactions. Although the effects of delayed interaction are often neglected when the intrinsic dynamics is much slower than the coupling delay, they can be crucial otherwise. We show that delayed coupled neuronal networks support transitions between synchronous and asynchronous states when the level of input to the network changes. The level of input determines the oscillation period of neurons and hence whether time-delayed connections are synchronizing or desynchronizing. We find that synchronizing connections lead to synchronous dynamics, whereas desynchronizing connections lead to out-of-phase oscillations in network motifs and to frustrated states with asynchronous dynamics in large networks. Since the impact of a neuronal network to downstream neurons increases when spikes are synchronous, networks with delayed connections can serve as gatekeeper layers mediating the firing transfer to other regions. This mechanism can regulate the opening and closing of communicating channels between cortical layers on demand.
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Affiliation(s)
- Zahra G Esfahani
- Department of physics, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
| | - Leonardo L Gollo
- Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Alireza Valizadeh
- Department of physics, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran.,School of Cognitive Sciences, IPM, Niavaran, Tehran, Iran
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16
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Esfahani ZG, Valizadeh A. Transient synchrony in delayed coupled neuronal networks. BMC Neurosci 2015. [PMCID: PMC4699146 DOI: 10.1186/1471-2202-16-s1-p269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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17
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Bolhasani E, Valizadeh A. Stabilizing synchrony by inhomogeneity. Sci Rep 2015; 5:13854. [PMID: 26338691 PMCID: PMC4559804 DOI: 10.1038/srep13854] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2015] [Accepted: 08/07/2015] [Indexed: 11/28/2022] Open
Abstract
We show that for two weakly coupled identical neuronal oscillators with strictly positive phase resetting curve, isochronous synchrony can only be seen in the absence of noise and an arbitrarily weak noise can destroy entrainment and generate intermittent phase slips. Small inhomogeneity–mismatch in the intrinsic firing rate of the neurons–can stabilize the phase locking and lead to more precise relative spike timing of the two neurons. The results can explain how for a class of neuronal models, including leaky integrate-fire model, inhomogeneity can increase correlation of spike trains when the neurons are synaptically connected.
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Affiliation(s)
- Ehsan Bolhasani
- Institute for Advanced Studies in Basic Sciences, Department of physics, Zanjan, 45137-66731, Iran.,Institute for Research in Fundamental Sciences, School of Cognitive Sciences, Niavaran, Tehran, 19857, Iran
| | - Alireza Valizadeh
- Institute for Advanced Studies in Basic Sciences, Department of physics, Zanjan, 45137-66731, Iran.,Institute for Research in Fundamental Sciences, School of Cognitive Sciences, Niavaran, Tehran, 19857, Iran
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18
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Bayati M, Valizadeh A, Abbassian A, Cheng S. Self-organization of synchronous activity propagation in neuronal networks driven by local excitation. Front Comput Neurosci 2015; 9:69. [PMID: 26089794 PMCID: PMC4454885 DOI: 10.3389/fncom.2015.00069] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Accepted: 05/20/2015] [Indexed: 12/30/2022] Open
Abstract
Many experimental and theoretical studies have suggested that the reliable propagation of synchronous neural activity is crucial for neural information processing. The propagation of synchronous firing activity in so-called synfire chains has been studied extensively in feed-forward networks of spiking neurons. However, it remains unclear how such neural activity could emerge in recurrent neuronal networks through synaptic plasticity. In this study, we investigate whether local excitation, i.e., neurons that fire at a higher frequency than the other, spontaneously active neurons in the network, can shape a network to allow for synchronous activity propagation. We use two-dimensional, locally connected and heterogeneous neuronal networks with spike-timing dependent plasticity (STDP). We find that, in our model, local excitation drives profound network changes within seconds. In the emergent network, neural activity propagates synchronously through the network. This activity originates from the site of the local excitation and propagates through the network. The synchronous activity propagation persists, even when the local excitation is removed, since it derives from the synaptic weight matrix. Importantly, once this connectivity is established it remains stable even in the presence of spontaneous activity. Our results suggest that synfire-chain-like activity can emerge in a relatively simple way in realistic neural networks by locally exciting the desired origin of the neuronal sequence.
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Affiliation(s)
- Mehdi Bayati
- Mercator Research Group "Structure of Memory", Ruhr-Universität Bochum Bochum, Germany
| | - Alireza Valizadeh
- Department of Physics, Institute for Advanced Studies in Basic Sciences Zanjan, Iran ; School of Cognitive Sciences, Institute for Research in Fundamental Sciences Tehran, Iran
| | | | - Sen Cheng
- Mercator Research Group "Structure of Memory", Ruhr-Universität Bochum Bochum, Germany ; Department of Psychology, Ruhr-Universität Bochum Bochum, Germany
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19
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Belykh I, Reimbayev R, Zhao K. Synergistic effect of repulsive inhibition in synchronization of excitatory networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2015; 91:062919. [PMID: 26172784 DOI: 10.1103/physreve.91.062919] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2014] [Indexed: 06/04/2023]
Abstract
We show that the addition of pairwise repulsive inhibition to excitatory networks of bursting neurons induces synchrony, in contrast to one's expectations. Through stability analysis, we reveal the mechanism underlying this purely synergistic phenomenon and demonstrate that it originates from the transition between different types of bursting, caused by excitatory-inhibitory synaptic coupling. This effect is generic and observed in different models of bursting neurons and fast synaptic interactions. We also find a universal scaling law for the synchronization stability condition for large networks in terms of the number of excitatory and inhibitory inputs each neuron receives, regardless of the network size and topology. This general law is in sharp contrast with linearly coupled networks with positive (attractive) and negative (repulsive) coupling where the placement and structure of negative connections heavily affect synchronization.
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Affiliation(s)
- Igor Belykh
- Department of Mathematics and Statistics and Neuroscience Institute, Georgia State University, 30 Pryor Street, Atlanta, Georgia 30303, USA
| | - Reimbay Reimbayev
- Department of Mathematics and Statistics and Neuroscience Institute, Georgia State University, 30 Pryor Street, Atlanta, Georgia 30303, USA
| | - Kun Zhao
- Department of Mathematics and Statistics and Neuroscience Institute, Georgia State University, 30 Pryor Street, Atlanta, Georgia 30303, USA
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20
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Horvath V, Kutner DJ, Chavis III JT, Epstein IR. Pulse-coupled BZ oscillators with unequal coupling strengths. Phys Chem Chem Phys 2015; 17:4664-76. [DOI: 10.1039/c4cp05416d] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A host of asymptotically stable, temporally periodic patterns are found when chemical oscillators are pulse coupled,e.g., the 1 : 2 and 1 : D–N type patterns shown here.
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21
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Ghasemi Esfahani Z, Valizadeh A. Zero-lag synchronization despite inhomogeneities in a relay system. PLoS One 2014; 9:e112688. [PMID: 25486522 PMCID: PMC4259331 DOI: 10.1371/journal.pone.0112688] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2014] [Accepted: 10/10/2014] [Indexed: 11/18/2022] Open
Abstract
A novel proposal for the zero-lag synchronization of the delayed coupled neurons, is to connect them indirectly via a third relay neuron. In this study, we develop a Poincaré map to investigate the robustness of the synchrony in such a relay system against inhomogeneity in the neurons and synaptic parameters. We show that when the inhomogeneity does not violate the symmetry of the system, synchrony is maintained and in some cases inhomogeneity enhances synchrony. On the other hand if the inhomogeneity breaks the symmetry of the system, zero lag synchrony can not be preserved. In this case we give analytical results for the phase lag of the spiking of the neurons in the stable state.
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Bolhasani E, Azizi Y, Valizadeh A. Direct connections assist neurons to detect correlation in small amplitude noises. Front Comput Neurosci 2013; 7:108. [PMID: 23966940 PMCID: PMC3743174 DOI: 10.3389/fncom.2013.00108] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Accepted: 07/24/2013] [Indexed: 11/13/2022] Open
Abstract
We address a question on the effect of common stochastic inputs on the correlation of the spike trains of two neurons when they are coupled through direct connections. We show that the change in the correlation of small amplitude stochastic inputs can be better detected when the neurons are connected by direct excitatory couplings. Depending on whether intrinsic firing rate of the neurons is identical or slightly different, symmetric or asymmetric connections can increase the sensitivity of the system to the input correlation by changing the mean slope of the correlation transfer function over a given range of input correlation. In either case, there is also an optimum value for synaptic strength which maximizes the sensitivity of the system to the changes in input correlation.
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Affiliation(s)
- E Bolhasani
- Department of Physics, Institute for Advanced Studies in Basic Sciences Zanjan, Iran
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