Influence of Delayed Conductance on Neuronal Synchronization

Protachevicz, Paulo R.; Borges, Fernando S.; Iarosz, Kelly C.; Baptista, Murilo S.; Lameu, Ewandson L.; Hansen, Matheus; Caldas, Iberê L.; Szezech, José D.; Batista, Antônio M.; Kurths, Jürgen

doi:10.3389/fphys.2020.01053

Cited by 15 publications

(6 citation statements)

References 51 publications

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“…The transition of neuronal single-cell firing patterns can emerge as a sum of different sources. In this framework, topology properties, such as the number [ 18 , 19 ], the type [ 20 ], the intensity of neuronal interactions [ 21 , 22 , 23 , 24 , 25 ], and the time delay of chemical transmission [ 26 , 27 ], can contribute to the firing patterns transitions. Besides that, noise input currents, as well as external perturbations, can also play a role in the spike and burst transitions [ 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

The Roles of Potassium and Calcium Currents in the Bistable Firing Transition

Borges,

Protachevicz,

Souza

et al. 2023

Brain Sciences

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Healthy brains display a wide range of firing patterns, from synchronized oscillations during slow-wave sleep to desynchronized firing during movement. These physiological activities coexist with periods of pathological hyperactivity in the epileptic brain, where neurons can fire in synchronized bursts. Most cortical neurons are pyramidal regular spiking (RS) cells with frequency adaptation and do not exhibit bursts in current-clamp experiments (in vitro). In this work, we investigate the transition mechanism of spike-to-burst patterns due to slow potassium and calcium currents, considering a conductance-based model of a cortical RS cell. The joint influence of potassium and calcium ion channels on high synchronous patterns is investigated for different synaptic couplings (gsyn) and external current inputs (I). Our results suggest that slow potassium currents play an important role in the emergence of high-synchronous activities, as well as in the spike-to-burst firing pattern transitions. This transition is related to the bistable dynamics of the neuronal network, where physiological asynchronous states coexist with pathological burst synchronization. The hysteresis curve of the coefficient of variation of the inter-spike interval demonstrates that a burst can be initiated by firing states with neuronal synchronization. Furthermore, we notice that high-threshold (IL) and low-threshold (IT) ion channels play a role in increasing and decreasing the parameter conditions (gsyn and I) in which bistable dynamics occur, respectively. For high values of IL conductance, a synchronous burst appears when neurons are weakly coupled and receive more external input. On the other hand, when the conductance IT increases, higher coupling and lower I are necessary to produce burst synchronization. In light of our results, we suggest that channel subtype-specific pharmacological interactions can be useful to induce transitions from pathological high bursting states to healthy states.

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Section: Introductionmentioning

confidence: 99%

The Roles of Potassium and Calcium Currents in the Bistable Firing Transition

Borges,

Protachevicz,

Souza

et al. 2023

Brain Sciences

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“…In doing so, they facilitate the degree and direction of information transmission throughout the brain. However, one must not forget that despite having a system of efficient connectivity among neurons, conduction delays along their axonal branches remain their major drawback in terms of computational speed [114]. Therefore, the network of inhibitory neurons will need to work cooperatively in order to compel targeted populations of excitatory neurons to do that as well.…”

Section: Insights From Evoked Oscillations For Emotion Recognitionmentioning

confidence: 99%

Review of EEG Affective Recognition with a Neuroscience Perspective

Lim,

Lew,

Ang

2024

Brain Sciences

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Emotions are a series of subconscious, fleeting, and sometimes elusive manifestations of the human innate system. They play crucial roles in everyday life—influencing the way we evaluate ourselves, our surroundings, and how we interact with our world. To date, there has been an abundance of research on the domains of neuroscience and affective computing, with experimental evidence and neural network models, respectively, to elucidate the neural circuitry involved in and neural correlates for emotion recognition. Recent advances in affective computing neural network models often relate closely to evidence and perspectives gathered from neuroscience to explain the models. Specifically, there has been growing interest in the area of EEG-based emotion recognition to adopt models based on the neural underpinnings of the processing, generation, and subsequent collection of EEG data. In this respect, our review focuses on providing neuroscientific evidence and perspectives to discuss how emotions potentially come forth as the product of neural activities occurring at the level of subcortical structures within the brain’s emotional circuitry and the association with current affective computing models in recognizing emotions. Furthermore, we discuss whether such biologically inspired modeling is the solution to advance the field in EEG-based emotion recognition and beyond.

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“…The time delay in the conductance of the pre-synaptic neuron k (g k ) assumes d exc = 1.5 ms for excitatory and d inh = 0.8 ms for inhibitory connections (Borges et al, 2020). The influence of delayed conductance on neural synchronization was studied in Protachevicz et al (2020). There are no spike activities in the time interval…”

Section: The Aeif Model With Neural Autapses and Network Configurationsmentioning

confidence: 99%

“…A number of studies reported that excitatory synapses facilitate neural synchronization (Borges et al, 2017;Breakspear et al, 2003), while inhibitory synapses have an opposite effect (Kada et al, 2016;Ostojic, 2014;Protachevicz et al, 2019). The time delay related to excitatory and inhibitory synapses influences the neural synchronization (Gu and Zhou, 2015;Protachevicz et al, 2020). Further on, there is a strong research interest in the investigation of how excitatory and inhibitory synapses influence synchronization in neural networks (Ge and Cao, 2019).…”

Section: Introductionmentioning

confidence: 99%

Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses

Protachevicz

Iarosz

Caldas

et al. 2020

Front. Syst. Neurosci.

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A great deal of research has been devoted on the investigation of neural dynamics in various network topologies. However, only a few studies have focused on the influence of autapses, synapses from a neuron onto itself via closed loops, on neural synchronization. Here, we build a random network with adaptive exponential integrate-and-fire neurons coupled with chemical synapses, equipped with autapses, to study the effect of the latter on synchronous behavior. We consider time delay in the conductance of the pre-synaptic neuron for excitatory and inhibitory connections. Interestingly, in neural networks consisting of both excitatory and inhibitory neurons, we uncover that synchronous behavior depends on their synapse type. Our results provide evidence on the synchronous and desynchronous activities that emerge in random neural networks with chemical, inhibitory and excitatory synapses where neurons are equipped with autapses.

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Influence of Delayed Conductance on Neuronal Synchronization

Cited by 15 publications

References 51 publications

The Roles of Potassium and Calcium Currents in the Bistable Firing Transition

The Roles of Potassium and Calcium Currents in the Bistable Firing Transition

Review of EEG Affective Recognition with a Neuroscience Perspective

Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses

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