Construction of a direct brain-machine interface @MI) for neuroprosthetic purposes is at the forefront of many current neural engineering thrusts. Due to recent breakthroughs in device technology and implantation techniques, a basic framework is now sufficiently developed to allow design of systems level interface strategies producing robust, scalable BMIs that adapt quickly to optimize informa...

ion of Continuous System to Discrete Event System Using Neural Network Sung Hoon Jung* and Tag Gon Kim, *School of Information and Computer Engineering, Hansung Univ., Seoul, Korea tDepartment of Electrical Engineering, KAIST, Taejon 305-701, Korea ABSTRACT A hybrid system consists of continuous systems and discrete event systems, which interact with each other. In such configuration, a continu...

Neural interfaces (NIs) for motor control have recently become increasingly advanced. This has been possible owing to substantial progress in our understanding of the cortical motor system as well as the development of appropriate decoding methods in both non-human primates and paralyzed patients. So far, neural interfaces have controlled mainly computer screens and robotic arms. An important a...

Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rat...

Brain machine interface (BMI) design can be achieved by training linear and nonlinear models with simultaneously recorded cortical neural activity and behavior (typically the hand position of a primate). We propose the use of optimized BMI models for analyzing neural activity to assess the role of individual neurons and cortical areas in generating the performed movement. Two models (linear-fee...