Generations of Neural Networks: From Perceptrons to SNNs

paper (Supervised learning in spiking neural networks: A review of algorithms and evaluations)

1. First generation: These are the earliest neural networks, which were developed in the 1950s and 1960s. They are also known as “perceptrons” and are based on the idea of a single neuron that receives input and produces an output. These networks are limited in their capabilities, as they can only classify input into one of two categories.
2. Second generation: These neural networks, also known as “multi-layer perceptrons,” were developed in the 1980s and 1990s. They consist of multiple layers of artificial neurons, allowing them to process complex input and perform tasks such as pattern recognition and clustering.
3. Third generation: Spiking neural networks (SNNs) are a type of third generation neural network that is inspired by the structure and function of biological neurons. In the brain, neurons communicate with each other through electrical pulses called “spikes,” and the timing of these spikes carries information. SNNs attempt to replicate this process by using mathematical models of neurons that communicate with each other through spikes. One advantage of SNNs is that they are able to process input in a more spatiotemporal manner, meaning that they can take into account the timing of the input as well as the spatial relationships between different inputs. This makes them well-suited for tasks that require temporal processing, such as speech recognition or video analysis. Another advantage of SNNs is that they are more energy-efficient than other types of neural networks, as they only transmit spikes when they are necessary and can operate using lower voltage levels. This makes them a good candidate for use in mobile and edge computing devices where power consumption is a concern. Overall, SNNs are a promising area of research in the field of neural networks and have the potential to be used for a wide range of applications. These networks are even more powerful and are able to perform tasks such as image and speech recognition, machine translation, and natural language processing.

One key difference between the generations of neural networks is the level of complexity and the types of tasks they are able to perform. First generation neural networks are relatively simple and are only able to perform basic classification tasks, while third generation neural networks are much more complex and are able to perform a wide range of tasks.