There are about 100 billion neurons in the human brain, the same number as stars in our galaxy. Neurons are the basic building block of the human nervous system. They are specially designed to transmit information throughout the body.
On an average, each neuron is connected to a thousand other neurons. Can you imagine the complexity of a neural network in which 100 billion neurons each have a thousand connections.
Also consider this. The brain needs a constant supply of blood. Located inside the human brain is an extensive network of arteries and other small blood vessels that carry life-giving blood from the heart to the brain.
Blood vessels in the brain traverse some 100,000 miles, or 4 times more than the earth’s circumference.
The brain is a complex part of the human anatomy; its complexity baffling and challenging the best research scientists world-wide to uncover its secrets.
Now, a new Amrita book will help shed light on some of the brain’s functions.
The book is titled Computational Neuroscience of Granule Neurons: Biophysical modeling of single neuron and network functions of the cerebellum granular layer.
Published by Lambert Academic Publishing, Germany, it is priced at $101.00 (or 68.00 €).
The book is available for purchase at amazon.com and other online as well as brick-and-mortar bookstores around the world.
Authored by Dr. Shyam Diwakar, the book uses computational modelling to analyze neuronal processing to determine the possible role and function of a neuron in a particular neural microcircuit.
“Using mathematical biophysics, studies of neurons and neuronal hypotheses have become easier, thanks to increased computational resources,” stated the author.
“The initial chapters of the book serve as a quick reference for biophysics of neural computation,” he explained. “This book should be useful as a user manual for making biophysically detailed computational models of neurons and learning how these models can be used to understand the role of neurons in population coding.”
“Some properties of neurons have been noted to play a role in population coding and in network function. This book discusses a new algorithm, ‘ReConv’ for reconstructing local field potentials (LFPs) from detailed models of neurons. The algorithm has the ability to predict the relationship between cellular processes and their manifestation during circuit activity in vivo.”
May 11, 2011
School of Biotechnology, Amritapuri