Quantifying neural information content: a case study of the impact of hippocampal adult neurogenesis
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Neural networks have an intrinsic information content encapsulated by neural firing behaviors and constituted by their neural encoding. Shannon entropy is a fundamental method to quantize the amount of information in a variety of sources such as communication channels. Many approaches have been devised to apply this sort of information measure to neurons with mixed success. However, doing so typically requires knowledge of the firing behavior probability distributions for the neurons of interest (whether modeled or recordings), and furthermore often is only applicable for single neurons and not ensembles. We have observed that conventional compression methods may help overcome some of the limiting factors of standard techniques and allows us to approximate information in neural data. To do so we have used compressibility as a measure of complexity in order to estimate entropy to quantitatively assess information content of neural ensembles.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1561002
- Report Number(s):
- SAND--2016-1928R; 619783
- Country of Publication:
- United States
- Language:
- English
Similar Records
Quantifying neural information content: A case study of the impact of hippocampal adult neurogenesis through computational modeling.
Information transmission and recovery in neural communications channels