Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Investigating the existence of periodicity in activity of neural network by novel neural signal processing technique - quantifying induced learning in cell culture

Sayan Biswas
doi: https://doi.org/10.1101/177360
Sayan Biswas
1Department of Electrical Engineering Jadavpur University, Kolkata, India
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

The network forming ability of neurons are huge for their sparking ability to form new connections and break existing ones. This sheer ability allows dynamic nature of the network for which this network are ever changing. The neurons being cells that are chemically and electrically excitable, electrical excitation of these cells cause variation of voltage in vicinity of the active neurons. These variation captured through electrical recording device records to activity points in the network. Cultured neuron cells on Multi electrode array dish is used to study disassociated cultures. A novel integrative model of neural signal processing termed as Activity Index is applied. AI variation is plotted graphically to show the evidence in periodicity of network analysis. The finding on periodicity are discussed along with how could it be used as a potential parameter to quantify learning ability of a cell culture.

Index Terms neurons, dynamic, variation of voltage, Multi electrode array, Activity Index train, Periodicity, learning

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted August 17, 2017.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Investigating the existence of periodicity in activity of neural network by novel neural signal processing technique - quantifying induced learning in cell culture
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Investigating the existence of periodicity in activity of neural network by novel neural signal processing technique - quantifying induced learning in cell culture
Sayan Biswas
bioRxiv 177360; doi: https://doi.org/10.1101/177360
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Investigating the existence of periodicity in activity of neural network by novel neural signal processing technique - quantifying induced learning in cell culture
Sayan Biswas
bioRxiv 177360; doi: https://doi.org/10.1101/177360

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Systems Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4384)
  • Biochemistry (9602)
  • Bioengineering (7100)
  • Bioinformatics (24885)
  • Biophysics (12625)
  • Cancer Biology (9968)
  • Cell Biology (14364)
  • Clinical Trials (138)
  • Developmental Biology (7966)
  • Ecology (12115)
  • Epidemiology (2067)
  • Evolutionary Biology (15997)
  • Genetics (10932)
  • Genomics (14746)
  • Immunology (9875)
  • Microbiology (23683)
  • Molecular Biology (9486)
  • Neuroscience (50907)
  • Paleontology (370)
  • Pathology (1540)
  • Pharmacology and Toxicology (2684)
  • Physiology (4022)
  • Plant Biology (8664)
  • Scientific Communication and Education (1510)
  • Synthetic Biology (2397)
  • Systems Biology (6442)
  • Zoology (1346)