Contemporary AI systems still lack high-level cognitive and learning skills. With the help of digital neuromorphic chips, scientists now aim to accurately model how humans can quickly and easily learn a cognitive task, such as a new board game.

In digital neuromorphic chips, silicon ‘neurons’ replicate the way information flows in biological neurons but with different physics. Digital chips maintain their neuro­morphic status in the way they capture the brain’s architecture, while spikes come in the form of packets of information rather than analog voltage signals. Hence, they now facilitate the ability to better emulate learning and model chemical processes, such as the effects of dopamine on learning. The researchers also envision modelling various kinds of neurons, dendrites, and ion channels, as well as structural plasticity properties such as the loss and growth of synapses.

Digital neuromorphic chips can also facilitate high connectivity between their artificial ‘neurons’, similar to what is seen in the mammalian brain.

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