NextGen Artificial Neural Networks
How is information sent and received by a neuron? Neurons need to transmit information for communicating among themselves. Transmission of the information is done both within the neuron or from one neuron to another. In the human brain, the dendrites usually get information from the sensory receptors. The information received is passed to the axon through the cell body. As soon as the information reaches the axon, it moves down the axon’s entire length as an electric signal known as the action potential. On reaching the end of the axon, information needs to be transmitted to the next neuron’s dendrites, if required. There is a synaptic gap present between the axon and the dendrites of the next neuron. This gap can be filled on its own or with the help of neurotransmitters.
On similar lines, with rapidly changing industry needs multiple applications that generate huge amounts of data in the form of fast streams are becoming increasingly prevalent. These conditions usually impose memory and processing time restrictions, and they often turn into evolving environments where a change may affect the input data distribution. Such a change causes that predictive models trained over these stream data become obsolete and do not adapt suitably to new distributions. Especially in these non-stationary scenarios, there is a pressing need for new algorithms that adapt to these changes as fast as possible, while maintaining good performance scores. Unfortunately, most off-the-shelf classification models need to be retrained if they are used in changing environments and fail to scale properly. Spiking Neural Networks have revealed themselves as one of the most successful approaches to model the behaviour and learning potential of the brain, and exploit them to undertake practical online learning tasks.
A spiking neural network(SNN) is different from a traditional artificial neural network(ANN) known in the machine learning community. Spiking neural network operates on spikes. Spikes are discrete events taking place at specific points in time. Thus, it is different from Artificial Neural Networks that use continuous values. Differential equations represent various biological processes in the event of a spike. One of the most critical processes is the membrane capacity of the neuron. A neuron spikes when it reaches a specific potential. After a neuron spike, the potential is again re-established for that neuron. It takes some time for a neuron to return to its stable state after firing an action potential. The time interval after reaching membrane potential is known as the refractory period.
In the spiking neural network, neurons are not discharged at every propagation cycle. The firing of neurons is only when the membrane potential reaches a certain value. As soon as a neuron is discharged, it produces a signal. This signal reaches other neurons and changes their membrane potential. Spike train provides us with increased potential to process Spatio-temporal data. Though spiking neural network is in a pretty nascent stage currently but it promises a great future in the field of Machine learning.