Computational simulation of dentate gyrus granule cell—The role of metaplasticity

Hananeia, Nicholas; Benuskova, Lubica

doi:10.1016/j.neucom.2015.10.063

Cited by 5 publications

(7 citation statements)

References 33 publications

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“…The resulting current (Fig. 9b,c) is then passed to a non-dimensional regular spiking Izhikevich neuron, an acceptable model for GCs 84 . We used the following parameters: a = 0.02, b = 0.2, c = −65 mV, d = 6, time resolution = 0.1 ms, max voltage = 30 mV, initial values V(0) = −70 mV and x(0) = b.V(0).…”

Section: Methodsmentioning

confidence: 99%

Pattern separation of spiketrains in hippocampal neurons

Madar

Ewell

Jones

2019

Sci Rep

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Pattern separation is a process that minimizes overlap between patterns of neuronal activity representing similar experiences. Theoretical work suggests that the dentate gyrus (DG) performs this role for memory processing but a direct demonstration is lacking. One limitation is the difficulty to measure DG inputs and outputs simultaneously. To rigorously assess pattern separation by DG circuitry, we used mouse brain slices to stimulate DG afferents and simultaneously record DG granule cells (GCs) and interneurons. Output spiketrains of GCs are more dissimilar than their input spiketrains, demonstrating for the first time temporal pattern separation at the level of single neurons in the DG. Pattern separation is larger in GCs than in fast-spiking interneurons and hilar mossy cells, and is amplified in CA3 pyramidal cells. Analysis of the neural noise and computational modelling suggest that this form of pattern separation is not explained by simple randomness and arises from specific presynaptic dynamics. Overall, by reframing the concept of pattern separation in dynamic terms and by connecting it to the physiology of different types of neurons, our study offers a new window of understanding in how hippocampal networks might support episodic memory.

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Section: Methodsmentioning

confidence: 99%

Pattern separation of spiketrains in hippocampal neurons

Madar

Ewell

Jones

2019

Sci Rep

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“…In addition, a synaptic delay of 4 ms was introduced. The resulting current 729(Figure 9B-C)is then passed to a non-dimensional regular spiking Izhikevich neuron, an 730 acceptable model for GCs(Hananeia & Benuskova, 2016). We used the following parameters: a731 = 0.02, b = 0.2, c = -65 mV, d = 6, time resolution = 0.1 ms, max voltage = 30 mV, initial values 732 V(0) = -70 mV and x(0) = b.V(0).…”

mentioning

confidence: 99%

Pattern separation of spiketrains in hippocampal neurons

Madar

Ewell

Jones

2017

Preprint

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“…For all the remaining granule cells, V j = 0 (subthreshold). By strengthening the pattern separation, the hippocampus will store new information under new or unpredictable conditions [61]. Based on the process described above, each granule cells' activity can be summarized as follows [53]:…”

Section: Pattern Separation In the Hippocampusmentioning

confidence: 99%

Pattern Separation Network Based on the Hippocampus Activity for Handwritten Recognition

et al. 2020

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Reaching high accuracy in handwritten character recognition is an essential challenge since it is widely used in many fields such as signature analysis and forgery detection. Recently, deep learning has demonstrated efficiency in this field. The problem with deep learning is that it uses a vast number of parameters that require a large dataset for training. To overcome this problem, an intelligent network is proposed in this study, based on the computational function of the dentate gyrus of the brain's hippocampus. The ability to separate patterns with high overlapping is a task that is referred to as the dentate gyrus. Handwritten character images have high overlapping due to various writers' styles, or even one writer's style under different conditions. Therefore, proposing a network based on the dentate gyrus' functional computation can be useful in this field. One of the prominent features of the proposed network is employing two excitation steps and two inhibition steps, augmenting the accuracy of recognizing handwritten characters. The proposed network was evaluated with six datasets of digits and characters from five languages. Experiments on all of the used datasets showed promising results. Moreover, a comparative and detailed analysis of the proposed network with other SOM-based and deep learning methods is provided. Experimental results show a significant boost in accuracy. While the character error rate (CER) was smaller than 1.85% for all the experiments, the smallest CER of 0.6% was achieved by the MNIST dataset. Moreover, in recognizing patterns with high noise, the proposed network showed satisfactory results.

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Computational simulation of dentate gyrus granule cell—The role of metaplasticity

Cited by 5 publications

References 33 publications

Pattern separation of spiketrains in hippocampal neurons

Pattern separation of spiketrains in hippocampal neurons

Pattern separation of spiketrains in hippocampal neurons

Pattern Separation Network Based on the Hippocampus Activity for Handwritten Recognition

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