PaCNeS 2016 - Special Session on Parallel Computing for Neural System (PaCNeS)

The special session on Parallel Computing for Neural-based Systems provides an international forum for reporting progress and recent advances in parallel computing techniques, hardware and software tools for speeding-up functioning and training of traditional and bio-inspired neural-based systems. On top of that we are seeking for new type of neural-based computing devices that significantly improve the existing computing capabilities. Topics of interest include, but are not limited to:
Specialized computing hardware, transputers and FPGA-implementations for neuron-based systems
Parallel training algorithms for feed-forward, recurrent, RBF, recirculation and other neuron-based systems
Parallel supervised and unsupervised training and reinforcement learning algorithms
Parallelization of neural network algorithms on many-core systems, clusters, grids and clouds
GPU-based implementations of neural networks
Coarse-grain parallelization of neuron-based systems
Parallel training algorithms for deep-belief networks
Parallelization of cognitive neural models
Parallel implementations and training algorithms for spiking neuron-based systems
Grid-based frameworks for neural networks execution and parallelization
Modeling of large-scale neural models using parallel computing techniques
Neural simulators in neuroscience using parallel computing
Computational neuroscience using parallel architectures
Neural network simulation tools and libraries using parallel computing
High-performance machine intelligence