Neuromorphic hardware based on memristive nanodevices for seizure detection and recovery

Abstract During the last decades, neuromorphic engineers have developed specific hardware designed to build efficient computing systems inspired by the structure of the human brain. The emergence of nanoscale memristors provided these systems with a new component which can approximately emulate the behavior of synaptic connections, improving the capability to implement in-situ learning algorithms like spike-timing-dependent plasticity. Meanwhile, neuro-inspired biomimetic platforms have been developed to directly interface with biological neurons, allowing to record and process neural signals like local field potentials (LFP). Combining both technologies, it would be possible to implant intracraneal electroencephalography electrodes with a neuromorphic chip which could sense signals from epileptic tissues and provide stimulation to prevent seizures in a closed-loop setup. In this work, we use a neuromorphic hardware platform with memristors to process LFP activity generated by an artificial neural mass model (ANMM) of the hippocampal loop implemented on a microcontroller for real-time operation, showing that the memristor system can learn correlations between neurons to detect seizures and eventually prevent them. This closed-loop ANMM-memristor crossbar interaction demonstration paves the way for trying a similar setup, replacing the ANMM with biological epileptic tissues.