Intro to Neural Engineering - The Last Assignment

The Neural Engineering course has come to an end and I'm ready to declare it a success. The students seemed to enjoy it and I got a lot out of the course too. The key element to making the course succeed was the mix of students: we had a great mix of biologists and engineers which allowed everyone to exceed their comfort zone and learn something new. I would go so far as to say that in the future I'll need to cross-list the course with the Biology and Neuroscience programs in order to ensure sufficient enrollment from those areas.

The students had to hand in three assignments this term. The first was a report describing research projects that use neural decoding, beyond the scope of the applications we'd discussed in class. The second assignment was a computational biology assignment: students could either code up the Hodgkin-Huxley model (which also required them to generate the strength-duration curve) or they could attempt to replicate the basic neural model of Hansel 1998 which was discussed earlier in the semester.

The final assignment, which I just graded today, was to write the first two pages of an NIH proposal for the next great neural engineering experiment. The two pages had to encompass Specific Aims and Significance as outlined in the NIH PHS 398 application guidelines. I encouraged students to view the entire scope of neural engineering which we'd covered in the course, and to think about what would constitute a viable new research angle. The results were marvelous - reading these papers this afternoon was a real treat. Here are some of the topics:

• Brain-controlled articulatory speech
• Multi-electrode arrays that can receive "biological" input such as vision and audio
• Enhanced visual prosthetics (I got a couple of these... One student had the neat idea to create virtual retinal electrodes by co-stimulating pairs of adjacent "real" electrodes, as if sometimes done with auditory prostheses)
• Optogenetics: using light to facilitate brain-derived neurotrophic factor release in order to ameliorate symptoms of Parkinsons
• Spine-Machine Interface
• Magnetogenetics: like optogenetics but using a magnetic field to activate ion channels instead of light
• Bionic Sphincter: controlled autonomously via nerves of the viscera.

Clearly we have lots of future Nobel Laureates at Temple. Thanks to everyone for a great semester!

Lecture 14 - Optogenetics

This week was the 14th and final installment of my graduate Neural Engineering course. This week we discussed Optogenetics - the new breakthrough field that introduces light-sensitive components into cells. These can be used in at least two ways

1. A light-sensitive membrane protein can be added to a cell. When a specific frequency of light is shined on that cell, it will depolarize and fire.
2. By adding light sensitivity to gene promotors, it is/will be possible to up- or down- regulate the production of specific genes under the control a light source.

The genetic material needed to affect these changes is introduced via a standard (or so I'm told) technique called viral transfection in which RNA vectors are placed in a virus (i.e. Lentivirus) which infects cells with the RNA. The cell then uses the foreign RNA to manufacture the desired protein. There are genes which are apparently unique to each individual cell type; by modifying the RNA to only work in conjunction with specific genes, it becomes possible to functionally introduce the desired changes to very specific cell types.

The two papers we read were

• Boyden 2005
• Han 2007

The class was excited about this technique and we worked our way through some of the finer grained technical issues such as how complicated is it to put a fiber optic cable into someone's brain, how large of a light beam would one want (i.e. would you like to turn on one neuron at a time, or entire sections of the brain), and so on.

One interesting question that arose was whether it might be possible to genetically modify the RNA vectors to produce proteins that were sensitive to specific frequencies: i.e. can you program the light sensitivity of a protein? If that were possible, it might be useful to stimulate neurons using wavelengths outside of the visible band, perhaps even radio waves. By using wavelengths of the electromagnetic spectrum that can penetrate the brain, it might be possible to use spatial surface electrodes to target specific brain regions non-invasively.

I'll submit a final report on the class after the student's final projects are due: the first two pages of a grant proposal in which students pitch their idea for the next great neural engineering research project. Students are to submit Specific Aims and Significance sections ala NIH PHS 398 guidelines.

Lecture 13 - Vagus Nerve Stimulation

This week was devoted entirely to Vagus Nerve Stimulation (VNS), which is an electrophysiology technique in which pulses of electrical activity are supplied to the vagal nerve (via implanted system) in order to address a growing number of medical problems.

VNS technology has been in place for about 20 years but its medical applicability has been accelerating in recent years. It was originally designed to treat pharmacologically intractable epilepsy and was shown to reduce both the frequency and severity of seizures.

In the process of studying VNS in epilepsy, patients reported that the VNS was improving their moods. It was therefore discovered that VNS can also be used to treat depression in certain intractable cases. From there the list goes on and on: migraines, chronic pain, eating disorders, multiple sclerosis, and inflammatory diseases such as rheumatoid arthritis. The literature is generally positive and not much in the way of side-effects are reported.

The vagus nerves "wonder" throughout the body, innervating the stomach and heart, as well as any number of structures in the head and neck. It strongly connects to the structures of the limbic system (largely responsible for emotion, memory, and smell), which provides a strong candidate for mechanism of action; despite widespread use, mechanisms of VNS functionality are not well understood.

The articles we read were Beekwilder 2010 and Zitnik 2011. 

Lecture 12 - Neuron Modeling

In Week 12 we focussed on two papers. The first was "Quadratic Leaky Integrate-and-Fire Neural Network tuned with an Evolution-Strategy for a Simulated 3D Biped Walking Controller" (by Wiklendt) which can be found here. This paper developed a modified leaky integrate and fire neural network model to control a simulated biped. The model effectively used a three-layer neural model to control ten degrees of freedom in the simulated biped. Although the model was effectively an integrate and fire model, the authors made two nice simplifications that cleaned up the math enough that spike times could be calculated exactly, as opposed to being simulated numerically. The network learned using a (mu plus lambda) evolution strategy (ES) which was beyond the scope of our reading; I think next year this would make for a good reading. We were able to understand that essentially the evolution model is a cellular automaton that uses a fitness strategy to assess which neurons are weighted optimally for the desired outcome.

Overall the class seemed pretty down on this article since it used a virtual neural network to control a virtual biped: since neither the controller nor the network was "real", the class wondered what the practical application of this knowledge was.

The second article we read was the highly creative "Adaptive Flight Control with Living Neuronal Networks on Microelectrode Arrays" by DeMarse and Dockendorf at the University of Florida. The paper describes a setup in which a population of neurons is grown in a specially instrumented petri dish which can record electrical activity from the cells as they fire. The neural population is used to provide pitch and roll control for a virtual aircraft in a flight simulator game. Network training is achieved by providing the network with trains of high frequency pulses that force the network connectivity to change with respect to the stimulating electrode. What was neat about this paper was that the authors used the fact that the network could simultaneously encode two different control signals (pitch and roll) and that these could be assessed simultaneously by providing a single stimulus pulse to each of the control electrodes and then measuring the speed and extent of neural activations in response. The size and speed of the response was taken to encode the controls to the flight simulator. The class was generally positive on this paper (especially in comparison to the previous one, since the control signals were coming from a real source.

We also discussed whether this technology would make sense to bring to Temple University. There is some interest in my lab about growing neurons in vitro and measuring and manipulating their connectivity.

Lecture 11 - The Izhikevitch Brain

Lecture 11 covered Izhikevitch (2004) which discusses a gigantic simulation encompassing 100,000 neurons. The neurons themselves are modeled using the Izhikevich neuron model, which is a hybrid Hodgkin-Huxley/IAF neuron. The neurons were spatially distributed in a single layer across the surface of a virtual sphere and interconnected using local and projecting connections with synapses exhibiting potentiation and depression. The paper's key finding is that, as long as you provide random noise to stimulate the neurons occasionally, the neurons will cluster themselves into fairly stable functional groups that tend to fire together in sequence. Furthermore, coherent input stimuli can cause virtual receptive fields to change over time. These results are all reasonably biologically plausible and seem to bolster the theory of neural darwinism favored by co-author (and 1972 Nobel laureate) Gerald Edelman.

Amazingly, even using the lowly forward Euler with a timestep of 0.5 ms, their simulations worked at 60x realtime, meaning one second of simulated time required 60 seconds of real time to compute. They ran their simulation for 24 simulated hours, meaning the program actually required 60 days to run! Not bad for a 1GHz pentium. I wonder how long that simulation would take on a new top of the line work station these days...

Lecture 10 - The Hodgepodge Lecture

Lecture 10 was an interesting mix of a number of different topics. First, I presented work from my 2004-2005 postdoc in the optic nerve visual prosthesis lab of Professor Claude Veraart in Brussels, Belgium. The basic literature relevant to this area is:

• Delbeke (2003) Vision Res. 43(9)
• Verarart (2004) Artif Organs 27(11)
• Brelen (2006) J Neurosurg. 104(4)

Following that, we went back to Computational Neuroscience, looking at Numerical Integration methods for neural modeling. Specifically, we looked at Hansel (1998), which is a really interesting paper that we've replicated in our lab. The paper puts together a population of interconnected integrate and fire neurons with variable synaptic strength and network 'cohesion' factor. Then they simulate the network using (a) exact integration and (b) numerical methods. By comparing the exact results against the numerical methods, it is possible to deduce which numerical methods produce acceptable results. Having run this simulation in the lab, I was able to show the students the code and discuss specific results. We found that switching from floating point to double precision floats didn't improve precision at all, but the value of "dt" made a huge difference.

Hopefully, at least a few students will elect to replicate Hansel for their next assignment (due 4/21). Its a real challenge but its cool once you get it to work...

Lecture 9 - Computational Neuroscience

This week we continued last week's discussion by focussing in on some of the details of modeling neuronal spiking using a computer. The gold-standard model is Hodgkin Huxley - although we'd talked about it last week, we worked in more detail this week to understand how the Markov model of subunit state leads to a first order differential equation describing the dynamics of opening and closing membrane proteins. For example, suppose we are discussing the "n" gates comprising the potassium channel. Suppose "n" percent of them are open and (1-n) percent are closed. The probability of transitioning from closed to open in any instant is "alpha" and the probability of transitioning from open back to closed is "beta". The subunit dynamics can be described by dn/dt = (1-n)*alpha - n*beta. This states that the change in percentage of open "n" subunits is the number of closed subunits times the probability that each one will open, minus the number of open subunits time the probability that each one will close. If we solve dn/dt numerically, we will come up with a function n(t) that describes the proportion of n subunits open as a function of time. The potassium conductance is then given by gk_max * n^4, where the n^4 indicates that each K+ ion channel has four n subunits and all four must be open for the channel to conduct. It turns out that the dn/dt equation is just a first order differential equation with time constant 1/(alpha + beta) and n_infinity given by alpha / (alpha + beta). What makes Hodgkin Huxley a real challenge is that alpha and beta are both functions of membrane voltage!

Afterwards, we discussed simpler models that are easier and faster to implement that don't sacrifice too much precision. The basic model is integrate-and-fire. We looked at how to model synaptic input into an IAF neuron: essentially we extend our ion channel model to include an ion channel that opens and closes according to some post-synaptic conductance model, of which there are several. Using this configuration, it is easy to construct arbitrarily large networks of neurons that synapse onto each other and exhibit any number of behaviors.

Next we talked about the Izhikevitch neuron model, which is an integrate-and-fire type neuron but that includes two differential equations. The first models membrane voltage and the 2nd models membrane inactivation; the two equations work in opposition so that the more the membrane voltage increases, the more the inactivation increases to counter it. The dv/dt equation includes a quadratic equation that has been fitted based on empirical measurements from live tissue. The beauty of Izhikevitch is that it includes four parameters that can be varied to produce all sorts of different neural spiking patterns that actually occur in real life. In this way it is possible to build a network of neurons with an impressively rich palate of properties; the synaptic models (previous paragraph) can be used to interconnect these neurons.

Finally, we had an impromptu mini-lecture from one of our Neuroscience grad students who taught us about how G-protein modulated ion channel up-regulation is responsible for late phase synapse potentiation. This is something I know very little about and was very interested to learn! The Wikipedia entry on Long Term Potentiation covers this subject nicely.

Lecture 8

This week we moved on to some new material, taking a look at how we can write accurate mathematical descriptions of the electrical properties of neurons. In particular, what goes into modeling membrane dynamics? What can we infer about membrane structure from the models? And can those models be simplified without sacrificing too much accuracy?

Our reading was Chapter 5 of Dayan and Abbott, which does an excellent job discussing the physical underpinnings of membrane capacitance and resistance. The chapter also discusses the Hodgkin-Huxley model of electrically excitable cells, which is perhaps one of the finest basic science experiments of the 20th century. Hodgkin and Huxley did a series of painstaking experiments from which they deduced the microscopic structure and function of the membranes of electrically excitable cells. They determined how ions such as sodium and potassium flow across cell membranes in order to effect flow of current, and how this leads to action potential formation. Although there are a number of details to keep track of, the essential mechanics of membrane behavior are not more sophisticated that first-order differential equations. The Hodgkin-Huxley membrane model simplifies to four interconnected first-order diff-eqs that can be easily solved by any number crunching software (we prefer Matlab, although C/C++ will work in a pinch ;). My Matlab implementation of the Hodgkin-Huxley code can be found here. Next week we'll discuss some of the simplified models in detail, such as Integrate and Fire, as well as the notorious Izhikevitch model.

There was an interesting question about whether alternative computing platforms can be used to accelerate neural modeling. It turns out some at Georgia Tech have been trying to use FPGAs for neural modeling. Interesting papers can be found here and here.

Someone brought up the really fun question of why even bother writing mathematical models of neural activation. Fair question! Answering this question led to a lively discussion on improving medical devices such as pacemakers by trying to predict how changes to the device (or its placement) would affect functionality. Cardiac pacemakers are a great example actually: high quality electrical models of cardiac tissue are routinely used to make predictions about how to optimize performance by changing either the properties of the electrical stimuli or perhaps the locations in the cardiac muscle where those stimuli are delivered. Along those same lines, we discussed deep brain stimulation, which is commonly used to treat movement disorders such as Parkinsons and Essential Tremor. I brought up the point that despite its efficacy, there is much debate on how exactly DBS works. Neural models are being used to study DBS and to make predictions about how stimulating various parts of the brain can curb the effects of these diseases. Some incredible before-and-after video of DBS patients can be found here.

Lecture 7

This week we really swung for the fences! We read Sections 4.2 and 4.3 from Spikes (by Rieke) which cover neural coding of hyper-acuity in the bat and the fly. The fly visual system is pretty amazing - the fly can detect very small changes in its visual field far faster than can be explained by the raw speed of the visual system. It does this by integrating over a large number of broad receptive fields, thereby obviating the need for spatial oversampling. Pretty neat stuff!

The experiment described in the book goes like this: a fly is shown a random pattern and then that pattern is shifted by some small amount. The fly's H1 neuron is then monitored by dividing time from t=15-40ms post stimulus into 2ms bins and assigning a "one" (spike) or "zero" (no spike) to each of the 13 bins. (it takes 15ms for the signal to reach H1, and after 40ms, the fly has already reacted to the stimulus, meaning the signal has already been interpreted). There are therefore 2^13 possible outcomes of this experiment. The trick is to examine the contents of those 13 bits and determine whether the fly observed a pattern shift of x degrees or y degrees.

Spiking is, of course, a stochastic event - spike trains are probabilistic, not deterministic. We learned how to use signal-to-noise ratio to quantify the separability between two populations of spiking patterns, spiking patterns contributed to signal to noise ratio, and, most interestingly, that most of the information separating two populations comes in the timing of the first spike.

In general, this was a good conversation and a very tough piece of reading. I think next year this lecture could stand to be better developed, perhaps with some external readings and some simulated Matlab code.

Lecture 6

In Week 6, we focussed on specific methods of spike decoding, spending most of our time on linear decoding methods as spelled out in Kim 2006. We started by discussing the general concept of linear decoding, specifically that the firing rate of each neuron (at a specific time lag) is linearly related to the kinematic parameter of interest. Mathematically speaking we might say:

x(t) = Sum{wij * nij(t)}

where x(t) is the kinematic parameter being predicted and nij(t) is the number of times neuron "i" fired at the "j"th time-lag. The "wij" terms are the linear coefficients; these weights can be any real number, positive or negative. In reality, no known neuron actually fires linearly with respect to a kinematic parameter, but if we include enough lags and enough neurons in the model, we should be able to approximate reasonable kinematic behavior.

The trouble becomes finding acceptable combinations of weights, "wij". If our experiment includes 300 neurons at ten lags each, then there are 3000 weights altogether that must be approximated. This problem is poorly formed numerically and lends itself to non-singular matrices and non-optimal solutions.

The Kim paper essentially discusses methods of solving for the filter weights. The most basic is the optimal Wiener filter, and its closely related cousin the normalized least mean squares filter (which converges to the Wiener solution, at least in theory). The Kim paper also discussed the Kalman filter (which models both filter weights and their uncertainty), as well as the gamma filter (which condenses the neural signal in time) and the principal component filter (which condenses the neural signal in channel-space, with respect to maximal variance).

We also looked at some Matlab code I wrote that duplicated much of the math in the paper. One lesson that we observed off the bat is that the models all work reasonably well provided that the kinematic parameter always stays in the same range of values used during the training phase.

We also set out to look at Li 2009, which uses a modified unscented kalman filter to track neurons. Their UKF system uses a non-linear (quadratic) neural firing model and also takes temporal correlations into account. Unfortunately, we didn't get to spend as much time on this paper as I would have hoped, but at least everyone read it ;)

I didn't get as far as I would have liked with my neural decoding simulator - I would still like to code up the gamma filter and the extended kalman filter, but those will have to wait for a rainy day.

Lecture 5 - Spike Decoding

Lecture 5 was a lively one! This week we started talking about neural decoding algorithms. These are methods for trying to estimate what large numbers of neurons are 'thinking' about in real time. We started with an old standard, Georgopoulos 1986, which presented the concept of population vectors. The article was relatively easy for everyone to understand although we did note that most of the results validating their method were not published in that article. Still, the paper represents a landmark bit of thinking and we gave it its due.

The next article we discussed was at the request of one of our classmates who was in the process of reviewing a Schwartz paper for some other journal club she attends. The paper, Jarosiewicz 2008, discussed strategies monkeys undertake to compensate their brain machine interfaces when the preferred directions used in the population vector are rotated out of synch with the monkey's true preferred directions. It turns out the monkeys use a combination of three strategies: (1) changing their 'aim' to offset for the mis-tuned neurons, (2) reducing the relevance of the affected neurons on the BMI control, and (3) retuning those neurons to match the rotated preferred directions. It was an interesting and well done paper, but there was some interesting debate about whether the experiment revealed anything that wasn't already fairly obvious. Someone raised the point that what would really be interesting is to understand the mechanisms that underly the observed changes (as opposed to just knowing what those changes were).

The final paper we discussed was Quiroga 2009, which gives a solid background into the fundamental statistical issues underlying neural decoding. Everyone seemed to praise this paper for its readability and scope. The paper explained the difference between neural decoding and information theory. The take-home message was that, while a BMI can implement a successful neural decoder, there is still a lot of information encoded in the neurons that is simply being discarded. The concept of information theory (i.e. mutual information) allows us to calculate numerically exactly how much information the decoder misses. Such an objective measure is a vital tool for comparing the efficacy of different decoders. The Wikipedia page on mutual information gives a nice introduction into the math underlying that technique. We also discussed a paper written by my old lab mate Debbie Won where they used mutual information to quantify how much information is lost when neurons are mis-detected and mis-sorted. Its a great (albeit quite dense) paper. We finished off the class by creating an Excel spreadsheet that calculated some basic information theory numbers for a simple example; we changed the prior probabilities and tried to predict how the information content would change.

Next week we will tackle two more spike decoding papers that go into a lot more mathematical depth than anything we've dealt with so far. Stay tuned for the report!

Lecture 4 - Neural Engineering

This week we started to get into specific signal processing issues. Our reading was the excellent technical BMI summary by Linderman et al (published in IEEE Signal Processing Magazine) from Dr. Shenoy's lab at Stanford University. The article discusses in some technical depth the signal processing stages of a BMI and then discusses their efforts to implement these steps as wireless and/or implantable electronics.

Our main topics of discussion were (a) electrode longevity, (b) spike sorting, (c) neuron tuning, and (d) statistical models of neural behavior. The electrode longevity question was especially interesting since its such a substantial obstacle and there are very few concrete ideas about how to extend the life of electrodes in the brain. The primary issue is that the brain eventually treats electrodes like foreign objects and initiates an immune response that results in the electrodes becoming coated with microglia and other scar-like tissue.  We also discussed electrode movement in the brain, what the ramifications are for the stability of recorded neurons (its not good) and how it might be overcome. We had some good discussion about whether larger electrodes might be the solution, since they would have a wider "listening" radius and would therefore be more resilient to micro-movements. It would seem like the flip side of the equation is that larger electrodes would record from more neurons which would increase the incidence of overlapped spikes. Decoding overlapped spikes is certainly possible but perhaps not in a computationally efficient manner.

Finally we spent a solid hour discussing spike sorting. We started by introducing the concept of neural tuning functions, since this motivates the need for spike sorting in the first place. We quickly looked at Georgopoulos' landmark 1982 paper in which he discovered that motor neurons were tuned to arm movement direction in a roughly cosinusoidal manner. Following that, we looked at some demo Matlab code I put together to demonstrate the concept and the math behind spike sorting. We started with some very simple methods (thresholding and windowing) and moved on to more sophisticated methods such as feature extraction (we tried spike amplitude and width) followed by principal component analysis (which is basically just another feature extraction technique). We discussed the concept of clustering, in which spikes with similar features are automatically clustered together. In particular, we examined the k-means clustering method (a built-in Matlab function!) which works pretty well provided you tell it ahead of time how many clusters you are looking for. We discussed some of the pros and cons, including the need for at least partial supervision in determining thresholds and cluster shapes and numbers. The figure below shows our sample data set thats been sorted using PCA and k-means clustering.

Next week we'll be looking at neuron decoding. I'm off now to find a good reading for next week and to develop a good chunk of Matlab demo code!

Lecture 3 - Brain Machine Interfaces

In Week 3 we started to get into the specifics of neural engineering - in this case we looked at brain machine interfaces, focussing on work of the Nicolelis Lab at Duke  University. In the first half of the class, we finished discussing Chapter 3 of my dissertation (see my Lecture 2 post). This chapter gives a good general overview of biomedical data acquisition. We discussed technical details such as input impedance, gain, filtering, analog to digital conversion, bit-rates, and spike detection.

Following that, we delved into the very helpful review paper by Lebedev and Nicolelis (2006 Trends in Neurosciences). This paper outlines the different types of Brain Machine Interfaces, discusses their relative strengths, and goes into some detail about the roadblocks moving forwards. These roadblocks are summarized as: implantable data acquisition devices, developing real-time computational algorithms, design realistic artificial prostheses, and incorporating sensorimotor feedback.

Upcoming this week, we will be discussing "Signal Processing Challenges for Neural Prostheses" by Linderman et al., in which we will finally start to delve into some mathematics.

Lecture 2 - Acquiring Bioelectrical Signals

A custom integrated circuit that I designed in grad school for conditioning neural signals.

This week's class will be focused on the problem of how one acquires bioelectrical signals from the body. This necessitates an understanding of electrodes and their interactions with the body's electrolytes, as well as analog signal conditioning and digitization. We will use the brain machine interface as an example data acquisition system, since the concepts involved are fairly general and are hence applicable in other domains. Readings include chapters 1 and 3 from my dissertation(!) as well as Chapter 5 from the venerable book "Medical Instrumentation" by Webster.

Here are the discussion topics I emailed to the class:

Dissertation Chapter 1
What are the things you typically have to do when making a biological recording? Describe the pathway from electrode to computer. What are the details along the way that affect design constraints for the engineer?

Dissertation Chapter 3
You can focus your reading on sections 3.1 - 3.3.
Relate the design that I present in this chapter to the general design constraints laid out in Chapter 1. How do the properties of the neural signals I'm trying to capture impact the design of the data acquisition hardware?

Webster Chapter 5 (Electrode/Electrolyte Interface)
Sections 5.6-5.9 can be skimmed (or skipped if you're pressed for time)
What is the electrode/electrolyte interface? How do the chemical processes involved drive electrode designs? What are polarized and non-polarized electrodes? What are the pros and cons of each? What is motion artifact and how do we protect against it? What is the circuit model for the electrode/electrolyte interface and what does it tell us?

First Lecture

The first lecture was mostly a success, although I'm as nervous as ever about the variety of backgrounds in the classroom. Adapting to the strengths of the class will be key. I'm also going to have to work to make sure the class has the desired feel of being a journal club and not a lecture. This might be hard considering that as many as 14 students will be taking the course.

Today we covered the basics of electrical engineering circuit and linear systems theory. And on top of that we covered the very basics of neuroanatomy. Not too shabby for one 2.5 hour session. The ECE theory came from a powerpoint presentation I gave a couple of years ago to some Neurology residents. The neuroanatomy review came from Chapter 1 of "Neuroscience" by Purves et al. The bio-types in the room didn't complain too much at my review of neurons and synapses, so either they are too polite to complain or I got most of the details at least partially correct ;)

There were many good questions - for example we talked in detail about why differential recordings are so important in biomedical settings (big offset voltages that need rejecting...). We also had a marginally uncomfortable chuckle over why the ears are so heavily represented in the homunculus - yes, they are very sensitive, but why should that be the case? Its not like they are fingers with their abundant dexterity. Hmm. We left that question open for a later date.

Next week we'll tackle instrumentation in more detail, including the electrode-electrolyte interface and common types of electrodes.

Intro to Neural Engineering

This week I'm kicking off my new graduate course "Intro to Neural Engineering". The goal is to introduce students to the concepts of neural engineering including both the scope of the field as well as some of the specific techniques. Since I've never taught this course before (indeed, I don't even think its been taught elsewhere all that much) it should be a real seat of the pants experience.

I'm going to have students treat this like a glorified journal club - every week one or two students will be responsible for guiding a discussion on a reading. Readings will come from textbooks, peer-review literature, and possibly even general-interest non-fiction. Additionally, students are going to have to roll up their sleeves and actually try implementing some of the techniques we'll be reading about. This could involve coding up a particular mathematical technique or building a circuit to do data acquisition of some sort.

One of my main challenges will be the range of students. Of the 11 students registered, I have two PhD students in Electrical Engineering, about six MS students in EE, two neuroscience students, and a physical therapy student. Thats a bit of a mixed bag! Figuring out how to keep all these abilities interested will be a real challenge...