Spiking neural networks (SNNs) can be considered as a type of artificial neural networks (ANN), in which models of neurons communicate through "sequences of spikes." They are different in several ways from other forms of deep neural networks (DNNs). SNNs are believed to be the next generation of DNNs because it tries to bridge the gap between machine learning and neuroscience however it has some ground to cover before getting there
WHAT ARE SPIKING NEURAL NETWORKS?

SNNs can process a large amount of data using some spikes. Spiking neural networks provide "powerful tools for analysis" of processes in the brain to include analyzing the process behind:

-Learning,

-Understanding information and

-Plasticity (the ability of a living thing to adapt to the changes in its environment or the differences between its various habitats.) An example of SNN are seen in this graph, and another can be seen in figure.

SNNs offer solutions to problems in fields such as:

-Fast signal-processing,

-Event detection,

-Classification,

-Speech recognition,

-Spatial navigation or motor control.

DIFFERENCES BETWEEN SPATIAL NEUTRAL NETWORKS AND OTHER NETWORKS

1. SNNs apply to all problems considered to be "solvable by non-spiking neural networks." However, the difference is that the spiking models are more powerful than perceptrons (a basic part of a neuron) and sigmoidal gates (a digital circuit — for example 'AND,' 'OR,' and 'NOT'),

2. Non-spiking deep Neural network (DNNs) communicate using continuous values while SNNs communicate using spikes,

3. SNN are "intrinsically sensitive" to the processing and transmission of information that takes place in the brain,

4. SNN have has many advantages over traditional neural networks as it relates to "implementation of special purpose hardware." Presently, the training of traditional DNNs requires the use of high-end energy intensive graphics cards. However, SNNs have the built-in property that the output spikes, which are called 'trains' are sparse in time. The advantage this offers in biological networks is that the spiking consumes energy and that use few spikes which have high information content by extension serves the purpose of reducing energy consumption,

5. SNNs allow for the development of a brain like representation in the form of spike-based DNNs,

6. SNNs allow for a type of biology-inspired learning which is called (weight modification).

NEXT GENERATION OF MACHINE LEARNING

SNN are considered to be the next generation of neural networks. This is so because it tries to bridge the gap between machine learning and neuroscience by using biologically-realistic models of neurons that carry out computations. However, SNN is not more widespread because of the issue of training. It is believed that to use SNNs for "real-world tasks," then there is need to develop an "effective supervised learning method," which is a difficult task since it would involve determining how the human brain learns.

Secondly, SNNs are very "computationally-intensive" and requires "simulating differential equations." However, hardware such as IBM’s 'TrueNorth' tries to solve this by simulating neurons using special tools which can explore the "discrete and sparse nature" of the spiking behavior of neurons. Therefore, by addressing this issue, we are one step closer to SNNs becoming the next generation of DNN.

However, the point must be made that although they are the next generation on the one hand, on the other, SNNs by the issues to be dealt with, are far from being "practical tools for most tasks." That being said, there are some "current real-world applications" known to be associated with SNNs such as audio processing and images. However, there is sparse literature on its practical applications. Papers written on the subject are usually theoretical, or highlight performance under a simple "2nd generation network". At the same time, it is believed that many teams are working on various SNN supervised learning rules.

CONCLUSION

To wrap up, we reviewed what spiking neural networks are a type of artificial neural networks (ANN), in which models of neurons communicate through "sequences of spikes." They are different in several ways from other forms of deep neural networks (DNNs). SNNs are believed to be the next generation of DNNs because it tries to bridge the gap between machine learning and neuroscience however it has some ground to cover before getting there.