Description
This text is about neural modeling, i.e., about neurons and biological neural networks (BNNs) and how their dynamic behavior can be quantitatively described. It was written for graduate students in biomedical engineering, but will also be of interest to neurophysiologists, computational neurobiologists, and biophysicists who are concerned with how neural systems process information and how these processes can be modeled. What sort of academic background does the reader need to get the most out of this text? The author has assumed that readers are familiar with the formulation and solution of ordinary differential equations, also introductory probability theory, basic EE circuit theory, and that they have had an introductory course in neurobiology. This interdisciplinary background is not unusual for graduate students in biomedical engineering and biophysics. For the reader who wants to pursue any topic in greater depth, there are many references, some from the “classic period” in neurobiology (the 1960s and 1970s) and others from contemporary work.
Neural modeling as a discipline (now known as computational neurobiology or computational neuroscience) has a long history, back at least to the 1952 groundbreaking kinetic model of Hodgkin and Huxley for the generation of the nerve action potential. The Hodgkin–Huxley model dealt with events at the molecular and ionic levels on a unit area of axon membrane. Other models have examined neural behavior on a more macroscopic level, preserving neural components such as synapses, dendrites, soma, axon, etc. In another approach, the bulk behavior of large sensory networks such as the vertebrate retina or the arthropod compound eye has been modeled using the linear mathematics of engineering systems analysis. Each approach is valid in the proper context.
This text discusses tools and methods that can describe and predict the dynamic behavior of single neurons, small assemblies of neurons devoted to a single task (e.g., central pattern generators), larger sensory arrays and their underlying neuropile (e.g., arthropod compound eyes, vertebrate retinas, olfactory systems, etc.), and, finally, very large assemblies of neurons (e.g., central nervous system structures). Neural modeling is now performed by solving large sets of nonlinear, ordinary differential equations (ODEs) on a digital computer. There is considerable art and science in setting up a computational model that can be validated from existing neurophysiological data. There are special, free computer programs available at Web sites that allow the user to set up and test neural models. There are also “component libraries” available on the Internet that supply the modeler with the parameters of different types of neurons.
Table of Contents
- Chapter 1 Introduction to Neurons
- Chapter 2 Selected Examples of Sensory Receptors and Small Receptor Arrays
- Chapter 3 Electronic Models of Neurons: A Historical Perspective
- Chapter 4 Simulation of the Behavior of Small Assemblies of Neurons
- Chaper 5 Large Arrays of Interacting Receptors: The Compound Eye
- Chapter 6 Large Arrays of Interacting Receptors: The Vertebrate Retina
- Chapter 7 Theoretical Models of Information Processing and Feature Extraction in Visual Sensory Arrays
- Chapter 8 Characterization of Neuro-Sensory Systems Review of Characterization and Identification Means for Linear Systems
- Chapter 9 Software for Simulation of Neural Systems