Models of Dynamical Synapses and Cortical Development

Khalil, R; Moftah, Marie; Landry, Marc; Moustafa, Ahmed A.

doi:10.1002/9781119159193.ch23

Cited by 4 publications

(8 citation statements)

References 34 publications

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“…At present, SNNs have been well implemented in some brain regions modeling and cognitive functions simulation, like image classification (Zhang et al, 2018b), working memory maintenance (Zhang et al, 2016), decision-making tasks (Héricé et al, 2016;Zhao et al, 2018), cortical development (Khalil et al, 2017a), contingency perception (Pitti et al, 2009) etc. However, even though the training methods proposed by Zhang et al (2018a) and Shrestha and Orchard (2018) make SNNs performance comparable to ANNs, they are at the cost of a large amount of time.…”

Section: Introductionmentioning

confidence: 99%

A Curiosity-Based Learning Method for Spiking Neural Networks

Shi

Zhang

Zeng

2020

Front. Comput. Neurosci.

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Spiking Neural Networks (SNNs) have shown favorable performance recently. Nonetheless, the time-consuming computation on neuron level and complex optimization limit their real-time application. Curiosity has shown great performance in brain learning, which helps biological brains grasp new knowledge efficiently and actively. Inspired by this leaning mechanism, we propose a curiosity-based SNN (CBSNN) model, which contains four main learning processes. Firstly, the network is trained with biologically plausible plasticity principles to get the novelty estimations of all samples in only one epoch; secondly, the CBSNN begins to repeatedly learn the samples whose novelty estimations exceed the novelty threshold and dynamically update the novelty estimations of samples according to the learning results in five epochs; thirdly, in order to avoid the overfitting of the novel samples and forgetting of the learned samples, CBSNN retrains all samples in one epoch; finally, step two and step three are periodically taken until network convergence. Compared with the state-of-the-art Voltage-driven Plasticity-centric SNN (VPSNN) under standard architecture, our model achieves a higher accuracy of 98.55% with only 54.95% of its computation cost on the MNIST hand-written digit recognition dataset. Similar conclusion can also be found out in other datasets, i.e., Iris, NETtalk, Fashion-MNIST, and CIFAR-10, respectively. More experiments and analysis further prove that such curiosity-based learning theory is helpful in improving the efficiency of SNNs. As far as we know, this is the first practical combination of the curiosity mechanism and SNN, and these improvements will make the realistic application of SNNs possible on more specific tasks within the von Neumann framework.

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Section: Introductionmentioning

confidence: 99%

A Curiosity-Based Learning Method for Spiking Neural Networks

Shi

Zhang

Zeng

2020

Front. Comput. Neurosci.

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“…Here, we designed a Spike Neural Network (SNN) model to monitor the neural firing activity responses within the two physiological states of GABA A . Following fine-tuning and optimization of our model (see Supplementary Materials: Figures S.1.1, S.1.2 in Khalil et al, 2017a,b), we systematically segregated it into several scenarios (Figure 1; Appendix A, C.2: Network Scenario). For each network scenario, we relied on biophysical parameters that had been intensively used in various experiments and biophysical studies (Appendix A, E: Model Parameters, see also Khalil et al, 2017a,b).…”

Section: Methodsmentioning

confidence: 99%

“…This has been shown to be one of the principal relevant parameters for macroscopic quantities (Yger et al, 2011). Finally, we used proportions for a lateral spread length between neighbor neurons {1, 2, …,10%} and we selected reciprocal values for both parameters, which resulted in eliciting a reasonable firing activity response (Figure 1 in Khalil et al, 2017a,b, see also Appendix A, C: Connectivity). The design of the network scenarios relies on the variations in the percentages of (ϵ) and (σ c ).…”

Section: Methodsmentioning

confidence: 99%

“…Based on our previous work on dynamical synapses (Khalil et al, 2017a,b) and to better explain the effects of dynamical synapses on cortical network development, we extended our modeling study to observe firing activity in response to the excitatory action of GABA A . Accordingly, we considered the reversal potential of GABA A to be excitatory in the immature condition and inhibitory in the mature condition (Ben-Ari, 2002, 2007a; Ben-Ari et al, 2007b, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have shown that during early cortical development, the reversal potential of GABA A shifts from depolarizing to hyperpolarizing. Here we provide the first integrative computational model to simulate the combined effects of these factors in a unified framework (building on our prior work: Khalil et al, 2017a,b). In the current study, we extend our model to monitor firing activity in response to the excitatory action of GABA A .…”

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confidence: 99%

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Dynamic Communications Between GABAA Switch, Local Connectivity, and Synapses During Cortical Development: A Computational Study

Khalil

Karim

Khedr

et al. 2018

Front. Cell. Neurosci.

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Several factors regulate cortical development, such as changes in local connectivity and the influences of dynamical synapses. In this study, we simulated various factors affecting the regulation of neural network activity during cortical development. Previous studies have shown that during early cortical development, the reversal potential of GABAA shifts from depolarizing to hyperpolarizing. Here we provide the first integrative computational model to simulate the combined effects of these factors in a unified framework (building on our prior work: Khalil et al., 2017a,b). In the current study, we extend our model to monitor firing activity in response to the excitatory action of GABAA. Precisely, we created a Spiking Neural Network model that included certain biophysical parameters for lateral connectivity (distance between adjacent neurons) and nearby local connectivity (complex connections involving those between neuronal groups). We simulated different network scenarios (for immature and mature conditions) based on these biophysical parameters. Then, we implemented two forms of Short-term synaptic plasticity (depression and facilitation). Each form has two distinct kinds according to its synaptic time constant value. Finally, in both sets of networks, we compared firing rate activity responses before and after simulating dynamical synapses. Based on simulation results, we found that the modulation effect of dynamical synapses for evaluating and shaping the firing activity of the neural network is strongly dependent on the physiological state of GABAA. Moreover, the STP mechanism acts differently in every network scenario, mirroring the crucial modulating roles of these critical parameters during cortical development. Clinical implications for pathological alterations of GABAergic signaling in neurological and psychiatric disorders are discussed.

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GLSNN: A Multi-Layer Spiking Neural Network Based on Global Feedback Alignment and Local STDP Plasticity

Zhao

Zeng

Zhang

et al. 2020

Front. Comput. Neurosci.

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Spiking Neural Networks (SNNs) are considered as the third generation of artificial neural networks, which are more closely with information processing in biological brains. However, it is still a challenge for how to train the non-differential SNN efficiently and robustly with the form of spikes. Here we give an alternative method to train SNNs by biologically-plausible structural and functional inspirations from the brain. Firstly, inspired by the significant top-down structural connections, a global random feedback alignment is designed to help the SNN propagate the error target from the output layer directly to the previous few layers. Then inspired by the local plasticity of the biological system in which the synapses are more tuned by the neighborhood neurons, a differential STDP is used to optimize local plasticity. Extensive experimental results on the benchmark MNIST (98.62%) and Fashion MNIST (89.05%) have shown that the proposed algorithm performs favorably against several state-of-the-art SNNs trained with backpropagation.

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Models of Dynamical Synapses and Cortical Development

Cited by 4 publications

References 34 publications

A Curiosity-Based Learning Method for Spiking Neural Networks

A Curiosity-Based Learning Method for Spiking Neural Networks

Dynamic Communications Between GABAA Switch, Local Connectivity, and Synapses During Cortical Development: A Computational Study

GLSNN: A Multi-Layer Spiking Neural Network Based on Global Feedback Alignment and Local STDP Plasticity

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