Neuroscience captured my interest long before I knew such a word existed. As a boy, I can remember gazing up into the sky while spinning in circles and, after coming to a stop but my head still spinning madly around, wondering: “Why?” Has a more pure forum for scientific query ever existed than in the innocent mind of a child? Indeed, my desire to pursue a Ph.D. in neuroscience has arisen from a lifelong fascination with how the brain works and why it causes us to do and think the things we do. I am not alone. The brain’s mysterious nature has inspired yet completely baffled scientists and other thinkers alike since the very beginning of time, even as man was acquiring his newly found cognitive skills. Neuroscience, then, appears to be an ageless endeavor, and is as natural and close to us as questioning who we really are. Even still, the number of approaches to the study of the brain continue to grow: science, philosophy, mathematics and even literature. Emily Dickinson once wrote that the “brain is wider than the sky.” I must agree, for, as she predicted when they are placed “side by side, the one the other contains” and me beside.
Why the Jaeger lab?
I was drawn to Dieter’s laboratory because I was interested in his unique approach to studying the brain: computational neuroscience. Although we most often think of computation as something only microprocessors and calculators can carry out, neurons are actually some of the most sophisticated computational devices known to man. Using this approach, neurons are viewed as a system of electrical circuits, which we can represent mathematically as electrical circuit equations. We can construct this system of electrical circuit equations in such a way as to represent a “model” of the neuron. Using specialized computer software, we can then manipulate (i.e., test) the model by inputting data which simulates the environment that a neuron may actually find itself in, such as excitatory and/or inhibitory connections from neighboring neurons. We can then observe how our model neuron behaves given these inputs relative to how actual neurons behave in in vivo and in vitro recording experiments. When we determine a particular set of equations in the model which correctly replicates actual data from our experiments, we interpret the system of equations as an algorithm that the neuron is using to determine its output characteristics.
I am currently working to help build a model of a particular type of neuron in the cerebellum known as a deep cerebellar nuclear (DCN) neuron. Solid anatomical and physiological evidence for cerebellar circuitry has existed for quite some time. However, the development of a realistic computational model of cerebellar circuitry would further advance and integrate our understanding of the types of algorithms or computational schemes the cerebellum uses to carry out its functions, such as motor and cognitive operations. This computational approach is important because firing patterns of neurons constitute complex, non-linear dynamic systems and are not easily predicted without the aid of such a model. Realistic compartmental models for Purkinje and granule cells have been established. However, such a model for DCN neurons does not yet exist. A computational model of cerebellar circuitry is absolutely dependent on an understanding of DCN neurons through a compartmental model, because DCN neurons serve as the major source of output from the cerebellum to the rest of the nervous system. A post-doctoral fellow in the laboratory is in the process of developing a single cell compartmental model of DCN neurons based on intracellular slice recordings of DCN neurons. However, in order to replicate realistic, in vivo functioning of DCN neurons, this model must be able to account for spiking patterns observed in extracellular in vivo recording experiments.
As a rotation student in Dieter’s laboratory, I performed several preliminary experiments involving electrophysiological recordings of the spontaneous and stimulus-driven firing patterns of DCN neurons. These experiments helped establish a characteristic pattern of spontaneous firing of DCN neurons as well as characteristic responses to both peripheral and direct electrical stimulation under ketamine anesthesia. This preliminary study involved extracellular in vivo electrophysiological recordings of DCN neurons below crus IIa of the rat cerebellum and was undertaken in conjunction with Kevin Erreger.
Now working full time in Dieter’s lab, I am extending this study of DCN neurons. My project involves three specific aims: (1) obtain additional in vivo electrophysiological recordings of neurons in the deep cerebellar nuclei (DCN) of rats. This aim will be carried out in order to further characterize both the stimulus-driven and pharmacologically-altered firing of neurons in the DCN of rats; (2) perform histological reconstruction of recorded sites and recorded neurons; (3) use a single cell compartmental model of DCN neurons to replicate data from extracellular in vivo electrophysiological recordings of DCN neurons.
One of my interests outside of my current research is the nature of consciousness. I have not yet decided where I stand in the ongoing debate between scientists and philosophers regarding the “mind/brain” problem. As a budding neuroscientist, I am tempted to fall on the side of reductionism, which in the context of consciousness means that every single thought, feeling and action that we have or do can be traced back to a particular firing pattern of neurons in our brains. However, the scientific method is not infallible. Philosophical proofs that consciousness is not completely explicable in biological terms are also quite convincing. Nevertheless, the subject of consciousness is a fascinating one. For me, it inspires endless questions: Can we trust consciousness to represent reality? Is our consciousness unique among the animal kingdom? Etc.
Another interest of mine is the developing field of neuroprosthetics. Some of the treatments for Parkinson's disease and epilepsy involve permanently implanting electrodes in the brains of affected patients. Patients may then stimulate these electrodes and the areas of the brain in which they are implanted when they begin to experience symptoms of hypokinesis or seizure activity. These types of devices, though, are relatively crude compared to what I believe may be accomplished by having a more mathematically precise understanding of how whole units of the brain operate and solve problems such as movement initiation and overactive neuron firing.
I am excited about the prospects of combining a computational approach to studying neurons along with modeling of whole cells. This approach will allow us to ask questions about how whole neurons behave without having to involve ourselves in the detailed study of the channels which contribute to the very behavior we are studying. The hope, or course, is to be able to one day 'hook up' all of these models - cerebellar cortex, white matter, nuclei - and then ask: How does the cerebellum help us move smoothly? What particular role does the cerebellum play in cognition? Is the cerebellum capable of more than we are consciously aware?