High frequency neurons determine effective connectivity in neuronal networks

Pariz, Aref; Esfahani, Zahra G.; Parsi, Shervin S.; Valizadeh, Alireza; Canals, Santiago; Mirasso, Claudio R.

doi:10.1016/j.neuroimage.2017.11.014

Cited by 32 publications

(44 citation statements)

References 31 publications

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“…The diversity and the time-dependency of the phase relationships reported in experiments [5,[21][22][23] are supposed to underlie the rich variety of communication patterns in the nervous circuits. Several experimental and computational studies have shown that those regions that phase advance others act as leaders and can efficiently transmit information to the laggard regions [20,24,25]. It has been shown that the presence of frequency mismatch can give rise to a phase difference and a directional information transfer, that is, nodes with higher frequency transmit information to those with lower frequency when the connection delay is neglected [20,24].…”

Section: Introductionmentioning

confidence: 99%

Transmission delays and frequency detuning can regulate information flow between brain regions

Pariz

Fischer

Valizadeh

et al. 2020

Preprint

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Brain networks exhibit very variable and dynamical functional connectivity and flexible configurations of information exchange despite their overall fixed structure (connectome). 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 that the connection delay and the frequency detuning between different neural populations play 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 can be determined solely 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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Section: Introductionmentioning

confidence: 99%

Transmission delays and frequency detuning can regulate information flow between brain regions

Pariz

Fischer

Valizadeh

et al. 2020

Preprint

Self Cite

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“…At the neuronal level, microcircuits producing coherent oscillations can be viewed as building blocks of the effective network dynamics in the brain. This means that the structural connectivity of neuronal motifs and the intrinsic excitability of each neuron modulate the information flow in the network [2]. In particular, the presence of autaptic connections (or autapses), which could be synapses from the axon of a neuron to its own somato-dendritic domain, have been shown to influence both the firing rhythm of the neuron and the synchronization properties of the network [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Inhibitory autapse mediates anticipated synchronization between coupled neurons

2019

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Two identical autonomous dynamical systems unidirectionally coupled in a sender-receiver configuration can exhibit anticipated synchronization (AS) if the Receiver neuron (R) also receives a delayed negative self-feedback. Recently, AS was shown to occur in a three-neuron motif with standard chemical synapses where the delayed inhibition was provided by an interneuron. Here we show that a two-neuron model in the presence of an inhibitory autapse, which is a massive self-innervation present in the cortical architecture, may present AS. The GABAergic autapse regulates the internal dynamics of the Receiver neuron and acts as the negative delayed self-feedback required by dynamical systems in order to exhibit AS. In this biologically plausible scenario, a smooth transition from the usual delayed synchronization (DS) to AS typically occurs when the inhibitory conductance is increased. The phenomenon is shown to be robust when model parameters are varied within a physiological range. For extremely large values of the inhibitory autapse the system undergoes to a phase-drift regime in which the Receiver is faster than the Sender. Furthermore, we show that the inhibitory autapse promotes a faster internal dynamics of the free-running Receiver when the two neurons are uncoupled, which could be the mechanism underlying anticipated synchronization and the DS-AS transition.

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“…The transfer of information between interacting sys-tems has been addressed using different theoretical tools, examples of which include: chaos syncronization [14], self-organization [15], and resonance [16]. On the other hand, in a system as complex as the brain [17] there is experimental evidence for the existence of crucial events.…”

Section: Introductionmentioning

confidence: 99%

Complexity Matching and Requisite Variety

Mahmoodi

West

Grigolini

2018

Preprint

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Complexity matching emphasizes the condition necessary to efficiently transport information from one complex system to another and the mechanism can be traced back to the 1957 Introduction to Cybernetics by Ross Ashby. Unlike this earlier work we argue that complexity can be expressed in terms of crucial events, which are generated by the processes of spontaneous self-organization. Complex processes, ranging from biological to sociological, must satisfy the homeodynamic condition and host crucial events that have recently been shown to drive the information transport between complex systems. We adopt a phenomenological approach, based on the subordination to periodicity that makes it possible to combine homeodynamics and self-organization induced crucial events. The complexity of crucial events is defined by the waiting-time probability density function (PDF) of the intervals between consecutive crucial events, which have an inverse power law (IPL) PDF ψ(τ ) ∝ 1/(τ ) µ with 1 < µ < 3. We establish the coupling between two temporally complex systems using a phenomenological approach inspired by models of swarm cognition and prove that complexity matching, namely sharing the same IPL index µ, facilitates the transport of information, generating perfect synchronization, reminiscent of, but distinct from chaos synchronization. This advanced form of complexity matching is expected to contribute a significant progress in understanding and improving the bio-feedback therapies.Author Summary This paper is devoted to the control of complex dynamical systems, inspired to real processes of biological and sociological interest. The concept of complexity we adopt focuses on the assumption that the processes of self-organization generate intermittent fluctuations and that the time distance between two consecutive fluctuations is described by a distribution density with an inverse power law structure making the second moment of these time distances diverge. These fluctuations are called crucial events and are responsible for the ergodicity breaking that is widely revealed by the experimental observation of biological dynamics. We argue that the information transport from one to another complex system is ruled by these crucial events and we propose an efficient theoretical prescription leading to qualitative agreement with experimental results, shedding light into the processes of social learning. The theory of this paper is expected to have important medical applications, such as an improvement of the biofeedback techniques, the heart-brain communication and a significant progress on cognition and the contribution of emotions to cognition.

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High frequency neurons determine effective connectivity in neuronal networks

Cited by 32 publications

References 31 publications

Transmission delays and frequency detuning can regulate information flow between brain regions

Transmission delays and frequency detuning can regulate information flow between brain regions

Inhibitory autapse mediates anticipated synchronization between coupled neurons

Complexity Matching and Requisite Variety

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