Probabilistic Spike Propagation for Efficient Hardware Implementation of Spiking Neural Networks.

Spiking neural networks (SNNs) have gained considerable attention in recent years due to their ability to model temporal event streams, be trained using unsupervised learning rules, and be realized on low-power event-driven hardware. Notwithstanding the intrinsic desirable attributes of SNNs, there is a need to further optimize their computational efficiency to enable their deployment in highly resource-constrained systems. The complexity of evaluating an SNN is strongly correlated to the spiking activity in the network, and can be measured in terms of a fundamental unit of computation, .

An inertial sensor-based system to develop motor capacity in children with cerebral palsy.

Learning to communicate with alternative augmentative communication devices can be difficult because of the difficulty of achieving controlled interaction while simultaneously learning to communicate. What is needed is a device that harnesses a child's natural motor capabilities and provides the means to reinforce them. We present a kinematic sensor-based system that learns a child's natural gestural capability and allows him/her to practice those capabilities in the context of a game.