Neuromorphic Computing

A primary objective in artificial intelligence research is to create computers capable of learning and reasoning similar to human cognition. While there are various approaches to achieve this, the consensus within the engineering community is that the most effective approach involves creating computer models that replicate the human brain's architecture.

Neuromorphic computing is a process that mimics the human brain's structure and functionality, using artificial neurons and synapses to process information.

Using artificial neurons and synapses, neuromorphic models simulate how our brain processes information, allowing them to solve problems, recognize patterns, and make decisions more quickly and efficiently than conventional computing models. A Neuron, also called a node, is a basic computational unit that processes inputs and produces an output, using a weighted sum and an activation function. A Synapse is a connection between two neurons.

Unlike the von Neumann model, where processing units and memory are separate, the neuromorphic computing model integrates memory and processing units in the neurons and synapses. Neuromorphic algorithms are defined by the structure of the neural network and its parameters rather than by direct instructions, as in von Neumann architecture.

Another significant distinction is how input data is processed.

Instead of encoding information as numerical values in binary format, neuromorphic computing uses Spikes as inputs, where the timing, magnitude, and shape of these spikes encode numerical information.