Computational intelligence and neuroscience
Jun 20, 2016
Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware pe...
Emulation of biological synapses is an important step toward construction of large-scale brain-inspired electronics. Despite remarkable progress in emulating synaptic functions, current synaptic devices still consume energy that is orders of magnitud...
International journal of neural systems
Jun 9, 2016
Data-driven model-based analysis of electrophysiological data is an emerging technique for understanding the mechanisms of seizures. Model-based analysis enables tracking of hidden brain states that are represented by the dynamics of neural mass mode...
Self-organized structures in networks with spike-timing dependent synaptic plasticity (STDP) are likely to play a central role for information processing in the brain. In the present study we derive a reaction-diffusion-like formalism for plastic fee...
Computational intelligence and neuroscience
Jun 5, 2016
A model of an Equivalent Artificial Neural Net (EANN) describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN). The EANN helps to estimate the NN gains or ...
In contrast to other large-scale network models for propagation of electrical activity in neural tissue that have no analytical solutions for their dynamics, we show that for a specific class of integrate and fire neural networks the acceleration dep...
The human hand's versatility allows for robust and flexible grasping. To obtain such efficiency, many robotic hands include human biomechanical features such as fingers having their two last joints mechanically coupled. Although such coupling enables...
IEEE transactions on biomedical circuits and systems
May 18, 2016
Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuro...
IEEE transactions on biomedical circuits and systems
May 18, 2016
Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) have been successfully used in applications ranging from speech recognition to image classification, and are particularly interesting because of their potential for generative ta...
International journal of neural systems
May 16, 2016
In this paper, we use the neural property known as intrinsic plasticity to develop neural network models that resemble the koniocortex, the fourth layer of sensory cortices. These models evolved from a very basic two-layered neural network to a compl...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.