| Brain–machine interface (BMI) systems convert neural signals from motor regions of the brain into control signals to guide prosthetic devices. The ultimate goal of BMIs is to improve the quality of life for people with paralysis by providing direct neural control of prosthetic arms or computer cursors. While considerable research over the past 15 years has led to compelling BMI demonstrations...

The duty of securing networks is very difficult due to their size, complexity, diversity and dynamic situation. Currently applying neural networks in intrusion detection is a robust approach to ensure security in the network system. Further, neural networks are alternatives to other approaches in the area of intrusion detection. The main objective of this research is to present an adaptive, fle...

Why and how that deep learning works well on different tasks remains a mystery from a theoretical perspective. In this paper we draw a geometric picture of the deep learning system by finding its analogies with two existing geometric structures, the geometry of quantum computations and the geometry of the diffeomorphic template matching. In this framework, we give the geometric structures of di...

Abnormal decision making is a central feature of neuropsychiatric disorders. Recent investigations of the neural substrates underlying decision making have involved qualitative assessment of the cognition of decision making in clinical lesion studies (in patients with frontal lobe dementia) and neuropsychiatric disorders such as mania, substance abuse and personality disorders. A neural network...

Although the study of time has been central to physics and philosophy for millennia, questions of how time is represented in the brain and how this representation is related to time perception have only recently started to be addressed. Emerging evidence subtly yet profoundly challenges our intuitive notions of time over short scales, offering insight into the nature of the brain's representati...

This paper deals with artificial neural network (ANN) architecture, the multilayer Feed-forward (MLFF) network with back propagation learning. The training of an artificial neural network involves two passes. In the forward pass, the input signals propagate from the network input to the output. In the reverse pass the calculated error signals propagate backwards through the network where they a...

Animals seek out relevant information by moving through a dynamic world, but sensory systems are usually studied under highly constrained and passive conditions that may not probe important dimensions of the neural code. Here, we explored neural coding in the barrel cortex of head-fixed mice that tracked walls with their whiskers in tactile virtual reality. Optogenetic manipulations revealed th...

Machine translation is a natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to improve. Yet, current neural machine translation training focuses on expensive human-generated reference translations. We describe a reinforcement learning algorithm that improves neural machine translation systems f...

Quality improvement provides organizations with significant opportunities to reduce costs, increase sales, provide on time deliveries and foster better customer relationships. The design and manufacturing are among the critical processes for continuous quality improvement. Time series data collected from these processes are the useful source. While there are various techniques to explore these ...