Reasons for a new course
I believe it is time to propose a new neurobiology course aimed at teaching introductory, quantitative neurobiology. This consideration occurs now because of the successful initiation of the Biomedical Engineering (BME) department and the changing needs of the graduate students in Neurobiology and Behavior (NBB) have created a pool of students who are interested in quantitative approaches to neurobiology. In addition, I believe that a new engineeroriented introductory neurobiology course would strengthen the connections between BME, ECE and NBB.
There is clearly a neurobiological interest from BME students, as evidenced by the number of BME students in BioNB 440 and BioNB441 (cross listed in BME). Also, about 17 engineering students/year take BioNB222. BioNB440,441 were initiated in 1998 to address the needs of NBB grad students with regard to electronics and computer technology. The courses have been aimed at the molecular/cellular biology graduate students. The content of 440 includes basic electronics, with applications to cellular mechanisms and to humandesigned circuitry. Math in 440 is at the level of complex analysis which allows rigorous treatment of filters, subthreshold cell membranes and other linear circuits. The content of 441 is introductory computer programming using Matlab language. The topics include nonlinear modeling of cell membranes, pattern formation, animation, and data analysis.
Two years ago, BioNB440,441 were crosslisted in BME and currently most of the students in the classes are from BME and BEE. I believe that the influx of engineering students into these two introductory technical courses is not optimal for the targeted NBB audience, nor for the engineering students. The neurobiology students need basic engineering, and the engineers need to hear about the neurobiological applications of their technical background. The solution is a neurobiology course targeted at students with an engineering skill set and interests. By having a quantitative, introductory neurobiology course we could address the needs of engineering students to understand the electronic, chemical, statistical, and organizational aspects of the nervous system.
Summary:
Proposal
I propose to introduce a new course (call it BME222 for now) which would cover much the same material as the current BioNB222 course, but would assume an engineering background for the students, and would cover the topics in much more quantitative detail. The course would be crosslisted in NBB (and perhaps ECE) and would pull most of its students from engineering. The course could be teamtaught, but I am willing to take lead on it to get it started. See below for a tentative syllabus. I believe there enough staff interest to form a teaching team, based on preliminary discussions with faculty.
The syllabus for BME222 essentially follows the 222 topics, but with more simulation and substantial programming and problem sets. For instance, in talking about ion channels we could have students simulate the distribution of channel open times and how those relate the kinetic models used to fit the channel kinetics. When talking about movement disorders we could explain some oscillatory conditions using feedback control theory. When talking about development we can ask the students to perform computer experiments in nonlinear pattern formation.
The current lectures given in BioNB222 are listed below on the left. On the right are engineering aspects which could be emphasized in BME222. Some of the existing 222 material might have to be compressed, but the goal would be to produce a broad course which would prepare BME and NBB students for further study of the nervous system.
Summary:
Modified syllabus from BioNB2222006
Current Lectures 
Possible engineering topics 
Introduction: Neuron Hypothesis 
How are cells visualized? Technology. 
Introduction to Electrical Signaling in the Nervous System 
The nervous system is an electrochemical machine. Neural nets and computation. 
Resting Potential 
Electrochemical equilibrium, Goldman eqn 
Action Potentials and Propagation I 
The transatlantic cable and cable equation. 
Action Potentials and Propagation II 
HH model and simulations 
Ion Channel Structure, Function, and Diversity 
Exponential duration distributions implied from chemistry. Simulation of relation between chemical equations and channel times. 
Diseases of Ion Channels (channelopathies) 
Quantitative estimates of ionic disturbances, rates, amplitudes, number of channels. 
Ionic Mechanisms of Synaptic Excitation 
Electrical synapses/chemical synapses 
Inhibition and Neuronal Integration 
Automatic gain control 
Release of Neurotransmitter 
Chemistry of vesicle fusion, diffusion equation, coupled reaction/diffusion 
Neuromodulation 
Multiple modes of systems of nonlinear oscillators. 
Neurochemistry 

Just say "know": Drugs and the Brain 
Chemical kinetics, enzyme variability 
Building Blocks of the Nervous System: 

Control of Movement 
Central pattern generation by nonlinear oscillators 
Parkinson’s Disease: A Molecular & Personal Perspective 
Feedback control systems, stability, lag, delays. 
Neural Systems for Sensory Maps 
Spike train analysis and information theory 
Language and Hemispheric Dominance 
Probabilistic inference from sensory data 
Sleeping, Dreaming and Waking 
Wake/sleep chemical nonlinear oscillator 
Emotional Centers of the Brain 

Principles of Sensory Function 
Statistical problems that sensory systems must solve. 
Sensory Transduction 
Tunneling? Quantum efficiency. Gain. Cool mechanisms. 
Visual Periphery 
Image compression, gradient emphasis, lateral inhibition. Spatial aliasing. 
Vision in the CNS 
Wavelet transform. Redundancy of the average visual scene. Compression in the visual system. 
Hearing 
Fourier Transform. Impedance matching. Traveling wave on a inhomogeneous membrane. 
Sensory Motor Integration 
Converting from Cartesian to bodyjoint coordinates, inverse kinematics 
Initial Formation of the Vertebrate CNS 
Pattern generation through local rules. Forces and topology 
Specification of Neural Tissue I 

Specification of Neural Tissue II 

The Directed Movement of Neurons and Axons 
Chemical kinetics and gradient following 
Apoptosis 

Neurotropic Factors 

Learning & Memory I: nonassociative learning 
Integrate and fire schemes/neural nets 
Learning & Memory II: classical conditioning 
Synchrony, stability, 
Learning & Memory III: operant conditioning 

Complex Learning 

Hebbian Learning and LTP 
Comparison to backprop networks 