Teaching quantitative neurobiology to engineers and biologists

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 engineer-oriented 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 human-designed circuitry. Math in 440 is at the level of complex analysis which allows rigorous treatment of filters, sub-threshold 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 cross-listed 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.



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 cross-listed in NBB (and perhaps ECE) and would pull most of its students from engineering. The course could be team-taught, 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.


Modified syllabus from BioNB222-2006

Current Lectures

Possible engineering topics

Introduction: Neuron Hypothesis

How are cells visualized? Technology.
Electrical synapses/close coupling

Introduction to Electrical Signaling in the Nervous System

The nervous system is an electrochemical machine. Neural nets and computation.
Comparison to a digital computer.

Resting Potential

Electrochemical equilibrium, Goldman eqn
Chemistry behind channel specificity
Equivalent circuits, compare to Goldman eqn.

Action Potentials and Propagation I

The transatlantic cable and cable equation.
Need for nonlinear amplifiers.
Equivalent circuits and electronic implementation.

Action Potentials and Propagation II

HH model and simulations
Spiketrain analysis, information theory

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
Equivalent circuits/current flows

Inhibition and Neuronal Integration

Automatic gain control
Nonlinear effects, equvalent circuits

Release of Neurotransmitter

Chemistry of vesicle fusion, diffusion equation, coupled reaction/diffusion


Multiple modes of systems of nonlinear oscillators.



Just say "know": Drugs and the Brain

Chemical kinetics, enzyme variability

Building Blocks of the Nervous System:
Part 1,2


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.


Fourier Transform. Impedance matching. Traveling wave on a inhomogeneous membrane.

Sensory Motor Integration

Converting from Cartesian to body-joint 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



Neurotropic Factors


Learning & Memory I: non-associative learning

Integrate and fire schemes/neural nets
Pulsed networks vs. Hopfield networks
Equivalent circuit of variable synapse

Learning & Memory II: classical conditioning

Synchrony, stability,

Learning & Memory III: operant conditioning


Complex Learning


Hebbian Learning and LTP
Is LTP involved in learning?

Comparison to back-prop networks
Local normalization requirements