Overall quantified fingerprints combined with super-information characteristics digitized parameters to monitor the quality consistency of Rong'e Yishen oral liquid

2021 ◽  
pp. 106958
Author(s):  
Xiang Li ◽  
Hong Yang ◽  
Wanyang Sun ◽  
Guoxiang Sun
Keyword(s):  
Oral Liquid ◽  
Information Characteristics

Oral liquid thyroxine assumed at breakfast: preliminary results of the TICO study

2015 ◽  
Author(s):  
Ilenia Pirola ◽  
Annamaria Formenti ◽  
Linda Daffini ◽  
Alessandra Cristiano ◽  
Elena Gandossi ◽  
...  
Keyword(s):  
Preliminary Results ◽  
Oral Liquid

scholarly journals Spatial Memory in a Spiking Neural Network with Robot Embodiment

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2678
Author(s):  
Sergey A. Lobov ◽  
Alexey I. Zharinov ◽  
Valeri A. Makarov ◽  
Victor B. Kazantsev
Keyword(s):  
Neural Network ◽  
Spatial Memory ◽  
Internal Representation ◽  
Learning Curves ◽  
Global Network ◽  
Spiking Neural Network ◽  
Interference Phenomenon ◽  
Brain Functioning ◽  
Information Characteristics ◽  
Catastrophic Interference

Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity. The network receives stimuli at specific loci, while the memory retrieval operates as a functional SNN response in the form of population bursts. The SNN function is explored through its embodiment in a robot moving in an arena with safe and dangerous zones. We propose a measure of the global network memory using the synaptic vector field approach to validate results and calculate information characteristics, including learning curves. We show that after training, the SNN can effectively control the robot’s cognitive behavior, allowing it to avoid dangerous regions in the arena. However, the learning is not perfect. The robot eventually visits dangerous areas. Such behavior, also observed in animals, enables relearning in time-evolving environments. If a dangerous zone moves into another place, the SNN remaps positive and negative areas, allowing escaping the catastrophic interference phenomenon known for some AI architectures. Thus, the robot adapts to changing world.

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