Mining regular behaviors based on multidimensional trajectories

2016 ◽  
Vol 66 ◽  
pp. 106-113 ◽  
Author(s):  
Xinlong Pan ◽  
You He ◽  
Haipeng Wang ◽  
Wei Xiong ◽  
Xuan Peng
2017 ◽  
Vol 11 (7) ◽  
pp. 1147-1154 ◽  
Author(s):  
Xinlong Pan ◽  
Haipeng Wang ◽  
You He ◽  
Wei Xiong ◽  
Tao Jian

2021 ◽  
Author(s):  
Catherine Eberbach ◽  
Cindy E. Hmelo‐Silver ◽  
Rebecca Jordan ◽  
Joseph Taylor ◽  
Roberta Hunter

2019 ◽  
Vol 2 ◽  
Author(s):  
Annabelle Frazier ◽  
Patricia A. Ferreira ◽  
Joseph E. Gonzales

Abstract Across a significant body of research, psychopathy has often been conceptualized as a biologically based malady. In this research, genetic and neurobiological differences have been conceptualized to underlie psychopathy, while affected individuals’ life experiences only influence expressed psychopathic features and their severity. Psychopathy research has largely ignored developmental evidence demonstrating significant influences of environment on both biological and behavioral processes, resulting in several prominent criticisms (Edens & Vincent, 2008; Loeber, Byrd, & Farrington, 2015). The current review was conducted with two main aims: (a) to collect and consider etiological evidence from the extant body of research on genetic and neurobiological factors in psychopathy; and (b) to evaluate findings from genetic, neurotransmitter, brain structure, and brain function studies in the context of relevant evidence from developmental research. Examples from research on adversity and traumatic stress, a common correlate of psychopathy, were used to highlight current research gaps and future directions to aid in the integration of developmental and neurobiological research agendas. While some promising evidence exists regarding possible underlying neurobiological processes of psychopathic traits, this evidence is insufficient to suggest a largely biological etiology for the disorder. Further, information from developmental and epigenetic research may suggest complex, multidimensional trajectories for individuals experiencing psychopathy. Based on these observations, the authors make several recommendations for future research, as well as for current clinical application and practice.


2020 ◽  
Vol 14 ◽  
Author(s):  
Carlo Michaelis ◽  
Andrew B. Lehr ◽  
Christian Tetzlaff

Neuromorphic hardware has several promising advantages compared to von Neumann architectures and is highly interesting for robot control. However, despite the high speed and energy efficiency of neuromorphic computing, algorithms utilizing this hardware in control scenarios are still rare. One problem is the transition from fast spiking activity on the hardware, which acts on a timescale of a few milliseconds, to a control-relevant timescale on the order of hundreds of milliseconds. Another problem is the execution of complex trajectories, which requires spiking activity to contain sufficient variability, while at the same time, for reliable performance, network dynamics must be adequately robust against noise. In this study we exploit a recently developed biologically-inspired spiking neural network model, the so-called anisotropic network. We identified and transferred the core principles of the anisotropic network to neuromorphic hardware using Intel's neuromorphic research chip Loihi and validated the system on trajectories from a motor-control task performed by a robot arm. We developed a network architecture including the anisotropic network and a pooling layer which allows fast spike read-out from the chip and performs an inherent regularization. With this, we show that the anisotropic network on Loihi reliably encodes sequential patterns of neural activity, each representing a robotic action, and that the patterns allow the generation of multidimensional trajectories on control-relevant timescales. Taken together, our study presents a new algorithm that allows the generation of complex robotic movements as a building block for robotic control using state of the art neuromorphic hardware.


2020 ◽  
Vol 58 ◽  
pp. 40-51
Author(s):  
Xinlong Pan ◽  
Haipeng Wang ◽  
Xueqi Cheng ◽  
Xuan Peng ◽  
You He

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