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BN4402 : To be offered in July 2003 for ECE undergraduate and graduate students. Pre-requisite NIL Scope: A study of Electrical Engineering tools for Neuro-electrophysiology! Tutorials: Based on NEURON Software Package This course allows students to familiarize with the evolving field of Neuroengineering and introduces the concepts of Neuronal modeling. Neuronal Modeling is a technique that Computational Neuroscientists use to explore the behavior of neurons. Typically invitro experiments are conducted on brain slices and cultured neurons to record specific aspects of neuronal behavior. This data is then applied to a simulation model of the neuron. Our Neuroengineering lab has facilities for doing such in vitro experiments. The data will be supplied by the medical and bioengineering students working on in vitro experiments. What has motivated me to introduce this topic to our ECE students is the massive requirement for computational neuroscientists both in industry and research. Recently, I have found many research groups in the US wanting to work with computational neuroscientists. The reason being that many companies are now plunging into the fanciful area of Neuroengineering. Some of the feats that has attracted me to this area are the needs to explore the working principle of implantable deep brain stimulators for epilepsy and Parkinson’s disease. Some of these are FDA approved products. Computational modeling of deep brain stimulation is one of the most recent and challenging topic for research in the area of Neuroengineering! In recent years the greater availability of workstations has resulted in significant increases in modeling in many scientific disciplines. In Computational Neuroscience, there has been an increase in the number, and complexity of models of single neurons, and neural networks (Bower and Koch 1992). Modeling is attractive because it provides a deeper understanding of what is still unknown about the system, and thus helps us to guide our experiments so that we avoid generating massive amounts of unconnected and uninterpretable data (Bower 1992). A neuron generally consists of a central cell body or soma connected to a number of input elements (called dendrites) and a single output element (called an axon). This course will enable us to devise new approaches for visualizing the enormous amounts of data generated from complex neuronal network simulations. We will be using a package called “NEURON” to solve problems in the class. The problems will lead us to perform Realistic Modeling of Brain Structures with Remote Interaction between Simulations of the different neuronal structures in the brain. Cable Theory Cable Theory and the application of its equations describe the flow of current in an electrical cable. Similarly, it characterizes the flow of heat in a rod and the diffusion of substances in a solute, but more profoundly to neuroscientists, it describes the passage of current in dendritic neurons. Although this theory has made exact quantitative formulations of neurophysiological events feasible, its use in modeling real neurons is limited because of its shear complexity when dealing with neurons with comprehensive branching structures. Compartmental Modeling Compartmental Modeling is a recent extension to cable theory which involves decomposing the dendritic branches of a neuron into individual compartments. Since each compartment is kept small enough, only simple equations are needed to express it. There are generally two types of compartments (passive and excitable compartments) both of which can be modeled as electrical circuits. Common to both types of compartments is the part of the circuit that describes current flow through the cell membrane and to other compartments over a cytoplasmic resistance Ri. The membrane assumes some capacitance CM, and there is a potential source E. The circuit for an excitable compartment contains additional elements to simulate Sodium (Na), Potassium (K) conductances as well as synaptic channels that impart the dendrite. References Bower, J. M. 1992. ``Modeling the Nervous System.'' Trends in Neuroscience 15, no. 11[173] (Nov.): 411-412. Bower, J. M. and C. Koch. 1992. ``Experimentalists and modelers: can we all just get along?'' Trends in Neuroscience 15, no. 11[173] (Nov.): 458-461.