Information Encoding and Reconstruction by Phase Coding of Spikes

Author(s):  
Zoltan Nadasdy
2021 ◽  
Vol 11 (7) ◽  
pp. 885
Author(s):  
Maher Abujelala ◽  
Rohith Karthikeyan ◽  
Oshin Tyagi ◽  
Jing Du ◽  
Ranjana K. Mehta

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Miguel Angel Lastras-Montaño ◽  
Osvaldo Del Pozo-Zamudio ◽  
Lev Glebsky ◽  
Meiran Zhao ◽  
Huaqiang Wu ◽  
...  

AbstractRatio-based encoding has recently been proposed for single-level resistive memory cells, in which the resistance ratio of a pair of resistance-switching devices, rather than the resistance of a single device (i.e. resistance-based encoding), is used for encoding single-bit information, which significantly reduces the bit error probability. Generalizing this concept for multi-level cells, we propose a ratio-based information encoding mechanism and demonstrate its advantages over the resistance-based encoding for designing multi-level memory systems. We derive a closed-form expression for the bit error probability of ratio-based and resistance-based encodings as a function of the number of levels of the memory cell, the variance of the distribution of the resistive states, and the ON/OFF ratio of the resistive device, from which we prove that for a multi-level memory system using resistance-based encoding with bit error probability x, its corresponding bit error probability using ratio-based encoding will be reduced to $$x^2$$ x 2 at the best case and $$x^{\sqrt{2}}$$ x 2 at the worst case. We experimentally validated these findings on multiple resistance-switching devices and show that, compared to the resistance-based encoding on the same resistive devices, our approach achieves up to 3 orders of magnitude lower bit error probability, or alternatively it could reduce the cell’s programming time and programming energy by up 5–10$$\times$$ × , while achieving the same bit error probability.


2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Fang Han ◽  
Zhijie Wang ◽  
Hong Fan ◽  
Yaopeng Zhang

High-frequency synchronization has been found in many real neural systems and is confirmed by excitatory/inhibitory (E/I) network models. However, the functional role played by it remains elusive. In this paper, it is found that high-frequency synchronization in E/I neuronal networks could improve the firing rate contrast of the whole network, no matter if the network is fully connected or randomly connected, with noise or without noise. It is also found that the global firing rate contrast enhancement can prevent the number of spikes of the neurons measured within the limited time window from being confused by noise, thereby enhancing the information encoding efficiency (quantified by entropy theory here) of the neuronal system. The mechanism of firing rate contrast enhancement is also investigated. Our work implies a possible functional role in information transmission of high-frequency synchronization in neuronal systems.


1987 ◽  
Author(s):  
C L Verweij ◽  
M Hart ◽  
H Pannekoek

The von Willebrand factor (vWF) is a multimeric plasma glycoprotein synthesized in vascular endothelial cells as a pre-pro-polypeptide with a highly repetitive domain structure, symbolized by the formula:(H)-D1-D2-D'-D3-A1-A2-A3-D4-B1-B2-B3-C1-C2-(0H).A heterologous expression system, consisting of a monkey kidney cell line (C0S-1), transfected with full-length vWF cDNA, is shown to mimic the constitutively, secretory pathway of vWF in endothelial cells. The assembly of pro-vWF into multimers and the proteolytic processing of these structures is found to oro-ceed along the following, consecutive steps. Pro-vWF subunits associate to form dimers, a process that does not involve the pro-polypeptide of pro-vWF. This observation is derived from transfection of C0S-1 cells with vWF cDNA, lacking the genetic information encoding the pro-polypeptide, composed of the domains D1 and D2. Pro-vWF dimers are linked intracellularly to form a regular series of multimeric structures that are secreted and cannot be distinguished from those released constitutively by endothelial cells. The presence of the pro-polypeptide, embedded in pro-vWF, is obligatory for multimerization since the deletion mutant lacking the D1 and D2 domains fails to assemble beyond the dimer stage. It is argued that the D domains are involved in interchain interactions.


2021 ◽  
pp. 2107809
Author(s):  
Xuerui Gong ◽  
Zhen Qiao ◽  
Yikai Liao ◽  
Song Zhu ◽  
Lei Shi ◽  
...  
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