Rapid development of memristive elements emulating biological neurons creates new opportunities for brain-like computation at low energy consumption. A first step toward mimicking complex neural computations is the analysis of single neurons and their characteristics. Here we measure and model spiking activity in artificial neurons built using diffusive memristors. We compare activity of these artificial neurons with the spiking activity of biological neurons measured in sensory, pre-motor, and motor cortical areas of the monkey (male) brain. We find that artificial neurons can operate in diverse self-sustained and noise-induced spiking regimes that correspond to the activity of different types of cortical neurons with distinct functions. We demonstrate that artificial neurons can function as trans-functional devices (transneurons) that reconfigure their behaviour to attain instantaneous computational needs, each capable of emulating several biological neurons.<p></p>
Funding
Neuromorphic memristive circuits to simulate inhibitory and excitatory dynamics of neuron networks: from physiological similarities to deep learning
Engineering and Physical Sciences Research Council
National Eye Institute (NEI) Core Grant for Vision Research P30 EY019005
NIBIB + NIMH grant 1R01EB028154
NINDS R01 NS123435
X-Grants Program of the President’s Excellence Fund at Texas A&M University, and Air Force Office
of Scientific Research grant under contract no. FA9550-19-1-0213
NEI grants 5R01EY012135, R01 EY018613, R01EY029117
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