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Extending bidirectional brain-machine interfaces (BMI) tailored for specific experiments with additional software and hardware tools can be very onerous, if not impossible. To overcome this problem, we developed a modular configurable system by modifying the architecture of an existing bidirectional BMI. This modular system enables the seamless and efficient inclusion of new features and the integration of new protocols without changing the native system's overall structure. By introducing a platform for the implementation of BMI algorithms on neuromorphic chips, this method represents a step towards the development of low-power, compact and computationally powerful tools for clinical applications.

To endow large scale VLSI networks of spiking neurons with learning abilities it is important to develop compact and low power circuits that implement synaptic plasticity mechanisms. In this paper we present an analog/digital Spike-Timing Dependent Plasticity (STDP) circuit that changes its internal state in a continuous analog way on short biologically plausible time scales and drives its weight to one of two possible bi-stable states on long time scales. We highlight the differences and improvements over previously proposed circuits and demonstrate the performance of the new circuit using data measured from a chip fabricated using a standard 180nm CMOS process. Finally we discuss the use of stochastic learning methods that can best exploit the properties of this circuit for implementing robust machine-learning algorithms.