Electrical engineering

Neuromorphic engineering

Neuroscience

Transistor

Physical neural network

Economic growth

Biology

Operating system

Hebbian theory

Recurrent neural network

Memristor

Engineering

Voltage

Types of artificial neural networks

Emulation

Artificial intelligence

Economics

Capacitive sensing

Electronic engineering

Soma

Computer science

Artificial neural network

Nature Communications

Experimental demonstration of resistive neural networks has been the recent focus of hardware implementation of neuromorphic computing. Capacitive neural networks, which call for novel building blocks, provide an alternative physical embodiment of neural networks featuring a lower static power and a better emulation of neural functionalities. Here, we develop neuro-transistors by integrating dynamic pseudo-memcapacitors as the gates of transistors to produce electronic analogs of the soma and axon of a neuron, with "leaky integrate-and-fire" dynamics augmented by a signal gain on the output. Paired with non-volatile pseudo-memcapacitive synapses, a Hebbian-like learning mechanism is implemented in a capacitive switching network, leading to the observed associative learning. A prototypical fully integrated capacitive neural network is built and used to classify inputs of signals.

Capacitive neural network with neuro-transistors