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 emulate the behavior of synaptic connections, improving the capability to implement in-situ learning algorithms like spike-timing-dependent plasticity (STDP). 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 [1], as illustrated in Fig. 1. In this work, we implemented a neuromorphic computing system (NCS) hardware platform with a memristive crossbar [2] to process LFP activity generated by an artificial neural mass model (ANMM) of the hippocampal loop [3] implemented on a microcontroller for real-time operation, with a feed-back loop which controls the model activity. The diagram of this platform is shown in Fig. 2, where the four EEG signals generated by the ANMM correspond to four populations of neurons. A photograph of the hardware platform is presented in Fig. 3. The implemented system demonstrated that the memristor system can learn correlations between neurons using a stochastic STDP algorithm [4] to detect seizures and eventually prevent them, as illustrated in Fig. 4. This closed-loop ANMM-NCS-memristive crossbar interaction demonstration paves the way for trying a similar setup, replacing the ANMM with biological epileptic tissues.