Python implementation of biological Neurons (Spiking Neural Network) for information processing

SNN are artificial Neural Network models that mimics natural neural networks. SNN aims at bridging the gap between neuroscience and machine learning, using Biologically-realistic models of neurons to carry out computation. This talk is on how to build a simple Spiking Neural Network using python.

The aim of Artificial intelligence is to have machines act and think the way humans Do, but this can only be done, by giving machines the ability to learn (that is, machine learning). Machine learning has been a field of great interest among developers in the 21st century Right from implementation of predictive modelling, image/video processing to autonomous self-Driving cars. This still doesn’t mimic completely the human brain cells which exhibits stochastic Behavior. This therefore pushed scientist at IBM to create a randomly Spiking Neural Network (SNN) That imitates the functionality of biological neurons. This was efficiently able to capture the very essence Of what makes a brain tick in an artificial neuron. This breakthrough can lead to the development Of neuromorphic computers or brain inspired computing. SNN are artificial Neural Network models that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs also incorporate the concept of time into their Operating model. SNN aims at bridging the gap between neuroscience and machine learning, using Biologically-realistic models of neurons to carry out computation. SNN operates using spikes, Which are discrete events that takes place at points in time, rather than continuous values in A normal Neural network. This talk is therefore centered on how to build a simple Spiking Neural Network that can model neuronal systems for information processing using python libraries like PyNN, Neuron, Nest etc.

This is a talk that clarifies the future of artificial intelligence and how biological neurons could be modeled. It requires the understanding of how biological neurons works or simple neural network layers which will be clearly stated and simplified for easy understanding of how the Spiking Neural Network works.