Dynamics of neural motifs realized with a minimal memristive neurosynaptic unit

Implementing artificial neural networks in electronic hardware has been pursued for decades, but suffers drawbacks such as device mismatch and circuit complexity, hence requiring sophisticated fabrication facilities. This study exploits the concept of memristors to implement a neuromorphic circuit of extreme simplicity. Both the neuron and synaptic circuits quantitatively realize conventional mathematical models of theoretical neuroscience and are implemented with off-the-shelf analog electronic components. This hardware model provides an affordable and easily available platform to implement spiking neural networks for basic research and practical applications.