# Spiking Neural Networks for Computational Intelligence: An Overview

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## Abstract

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## 1. Introduction

## 2. Fundamentals of a Spiking Neuron

#### 2.1. Leaky Integrate-and-Fire Neuron

#### 2.2. Izhikevich Neuron Model

## 3. Architectures of Spiking Neural Networks

## 4. Learning in Spiking Neural Networks

#### 4.1. Unsupervised Learning

#### 4.2. Supervized Learning

#### 4.2.1. Gradient-Based Learning

#### 4.2.2. Bio-Inspired Learning

#### 4.2.3. Other Learning Algorithms

#### 4.3. Reinforcement Learning

## 5. Generic Applications of SNN in Computational Intelligence

## 6. SNNs on Neuromorphic Chips

## 7. Future Trends: Brain-Inspired SNN Architectures

#### 7.1. The NeuCube Architecture

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- Input information encoding module;
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- 3D SNN reservoir/cube module (SNNc), or also neurogenetic brain cube (NBC), for unsupervised learning;
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- Output classification/regression module for supervised learning;
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- Gene regulatory network module (optional).

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- Short-term memory, represented as changes of the membrane potential level and temporary changes of synaptic efficacy;
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- Long-term memory, represented as a stable establishment of synaptic efficacy—LTP and LTD;
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- Genetic memory, represented as a genetic code.

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- Predicting brain re-wiring through mindfulness [63];
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- Modelling neuroimaging data such as EEG and fMRI [62];
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- Personalized brain data modelling [64];
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- Emotion recognition [65];
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- Speech, sound and music recognition [66];
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- Moving object recognition [67];
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- Prediction of events from temporal climate data (stroke) [64];
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- Brain–computer interfaces (BCI) [68].

#### 7.2. Integration of Multimodal Data in a BI-SNN Architectures

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- Integrating time, space and orientation data, such as fMRI and DTI [66,69]: An extension of the STDP learning rule was proposed in [69], called oiSTDP, where if two or more postsynaptic neurons spike after a pre-synaptic neuron, the closer a postsynaptic neuron is to the orientation vector, the higher the increase is in the connection weight of that postsynaptic neuron. The proposed rules are utilized for integrating MRI and DTI data to create a personalized model for predicting the response of schizophrenic patient to clozapine. Based on the proposed approach, it has been shown that higher prediction accuracy is achieved using the integrated data;
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- Integrating genetic data into a neurogenetic SNN architecture [70]: In [70], a gene interaction network model was suggested as part of a spiking neuron model based on the neuroreceptors AMPAR and NMDAR. For a given problem, such as modelling AD, genes can be connected to these neuroreceptors in a gene regulatory network, thereby influencing the performance of the SNN as a whole.

## 8. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Relationship between the change in weight of a synapse and the difference between spike times of the pre- and postsynaptic neurons. $\Delta t={t}_{post}-{t}_{pre}$ where ${t}_{post}$ and ${t}_{pre}$ represent the spike times for the pre- and postsynaptic neurons, respectively. Figure was generated using the following values: ${A}_{+}=1$, ${A}_{-}=-0.4$, ${\tau}_{+}=60$ and ${\tau}_{-}=100$.

**Figure 2.**A schematic diagram of the NeuCube architecture (adopted from [15]).

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Dora, S.; Kasabov, N.
Spiking Neural Networks for Computational Intelligence: An Overview. *Big Data Cogn. Comput.* **2021**, *5*, 67.
https://doi.org/10.3390/bdcc5040067

**AMA Style**

Dora S, Kasabov N.
Spiking Neural Networks for Computational Intelligence: An Overview. *Big Data and Cognitive Computing*. 2021; 5(4):67.
https://doi.org/10.3390/bdcc5040067

**Chicago/Turabian Style**

Dora, Shirin, and Nikola Kasabov.
2021. "Spiking Neural Networks for Computational Intelligence: An Overview" *Big Data and Cognitive Computing* 5, no. 4: 67.
https://doi.org/10.3390/bdcc5040067