A neural network model of reliably optimized spike transmission

Samura, Toshikazu; Ikegaya, Yuji; Sato, Yasuomi D.

doi:10.1007/s11571-015-9329-1

Cited by 10 publications

(12 citation statements)

References 46 publications

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“…6(b)]. It is known that local interactions between excitatory and inhibitory neurons induce neural oscillations with high coherence between excitatory and inhibitory neural populations [72]- [76], and the size of this synchronized neural population is expanded under strong EPSP connections [29]. Therefore, it can be interpreted that the activated state of one module corresponded to the oscillation induced by intramodule connections between excitatory and inhibitory neural populations and the existence of strong EPSP connections, whereas the deactivated state of the other module represented subserviently spiking activity driven by the external input spikes and spikes from other modules.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…5) Surrogate Data Analysis: Recent findings regarding temporal fluctuations of neural activity imply that the structural network characteristics induce complex temporal neural fluctuation [20], [29]- [33]. Moreover, we previously reported that this characteristic appears as the deterministic dynamical activity, instead of stochastic temporal behavior [33].…”

Section: B Evaluation Index 1) Spiking Ratementioning

confidence: 99%

“…Moreover, studies of structural connectivity at the synaptic-level show that the excitatory postsynaptic potential (EPSP) of most cerebral synapses exhibits sub-mV values, while a small number of synapses exhibit large EPSPs ( 1.0 [mV]), i.e., the distribution of EPSP fits a log-normal distribution [25], [26]. Modeling studies focusing on the log-normal distribution of EPSPs have described spontaneous activity [27], [28], which is fluctuated spiking activity that is sustained even in the absence of external stimulation [20], [29], [30]. Moreover, Kriener et al [31] reported that this spontaneous activity that was induced by the log-normal distribution of EPSPs exhibited a complex behavior with slow temporal transitions between bistable activity states.…”

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confidence: 99%

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Long-Tailed Characteristic of Spiking Pattern Alternation Induced by Log-Normal Excitatory Synaptic Distribution

Nobukawa

Nishimura

Wagatsuma

et al. 2021

IEEE Trans. Neural Netw. Learning Syst.

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Studies of structural connectivity at the synaptic level show that in synaptic connections of the cerebral cortex, the excitatory postsynaptic potential (EPSP) in most synapses exhibits sub-mV values, while a small number of synapses exhibit large EPSPs ( 1.0 [mV]). This means that the distribution of EPSP fits a log-normal distribution. While not restricting structural connectivity, skewed and long-tailed distributions have been widely observed in neural activities, such as the occurrences of spiking rates and the size of a synchronously spiking population. Many studies have been modeled this long-tailed EPSP neural activity distribution; however, its causal factors remain controversial. This study focused on the long-tailed EPSP distributions and interlateral synaptic connections primarily observed in the cortical network structures, thereby having constructed a spiking neural network consistent with these features. Especially, we constructed two coupled modules of spiking neural networks with excitatory and inhibitory neural populations with a log-normal EPSP distribution. We evaluated the spiking activities for different input frequencies and with/without strong synaptic connections. These coupled modules exhibited intermittent intermodule-alternative behavior, given moderate input frequency and the existence of strong synaptic and intermodule connections. Moreover, the power analysis, multiscale entropy analysis, and surrogate data analysis revealed that the long-tailed EPSP distribution and intermodule connections enhanced the complexity of spiking activity at large temporal scales and induced nonlinear dynamics and neural activity that followed the long-tailed distribution.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: B Evaluation Index 1) Spiking Ratementioning

confidence: 99%

mentioning

confidence: 99%

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Long-Tailed Characteristic of Spiking Pattern Alternation Induced by Log-Normal Excitatory Synaptic Distribution

Nobukawa

Nishimura

Wagatsuma

et al. 2021

IEEE Trans. Neural Netw. Learning Syst.

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“…(6, 7, 8). Izhikevich's neuron model is widely used in many studies (Qu et al 2014;Samura et al 2015;Li et al 2016;Zhao et al 2016. Three spiking patterns were used: regular spiking (RS), chattering (CH), and low-threshold spiking (LTS), as shown in Fig.…”

Section: Neuron Modelmentioning

confidence: 99%

A decision-making model based on a spiking neural circuit and synaptic plasticity

Wei

Dai

2017

Cogn Neurodyn

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To adapt to the environment and survive, most animals can control their behaviors by making decisions. The process of decision-making and responding according to cues in the environment is stable, sustainable, and learnable. Understanding how behaviors are regulated by neural circuits and the encoding and decoding mechanisms from stimuli to responses are important goals in neuroscience. From results observed in Drosophila experiments, the underlying decision-making process is discussed, and a neural circuit that implements a two-choice decision-making model is proposed to explain and reproduce the observations. Compared with previous two-choice decision making models, our model uses synaptic plasticity to explain changes in decision output given the same environment. Moreover, biological meanings of parameters of our decision-making model are discussed. In this paper, we explain at the micro-level (i.e., neurons and synapses) how observable decision-making behavior at the macro-level is acquired and achieved.

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“…To address these issues, Hodgkin and Huxley (1952) proposed the Hodgkin-Huxley model. Not only did the model parameters have biological significance and scalability (Samura et al 2015), but the model also provided a foundation from which to explore synaptic integration and interactions between ion currents. However, its computational efficiency was poor, and it could only simulate a few neurons in real-time (Izhikevich 2003).…”

Section: Related Workmentioning

confidence: 99%

A plausible neural circuit for decision making and its formation based on reinforcement learning

Wei

Dai

2017

Cogn Neurodyn

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A human's, or lower insects', behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit's constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit's output layer, which generates a stable spike firing rate to encode flight commands, controls the insect's angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit's information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee's behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit's generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control. Finally, this study also helps establish a transitional bridge between the microscopic activity of the nervous system and macroscopic animal behavior.

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A neural network model of reliably optimized spike transmission

Cited by 10 publications

References 46 publications

Long-Tailed Characteristic of Spiking Pattern Alternation Induced by Log-Normal Excitatory Synaptic Distribution

Long-Tailed Characteristic of Spiking Pattern Alternation Induced by Log-Normal Excitatory Synaptic Distribution

A decision-making model based on a spiking neural circuit and synaptic plasticity

A plausible neural circuit for decision making and its formation based on reinforcement learning

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