This piece appeared in the March 2007 issue of the International Society for Neuroethology newsletter.
You may reproduce this article only with proper attribution as follows:
Kaushik Ghose, “Flights into the unknown”, International Society for Neuroethology newsletter, March 2007 issue.
When I came to Cynthia (Cindy) Moss' batlab at the University of Maryland, "the bat" in my mind was an abstract construct. For the previous five years I had trained in a field where people had spent a century understanding and distilling empirical knowledge into neat, analytically tractable portions. I was trained to think of giant, complex pieces of machinery in terms of simple 'equivalent circuits'. A hydroelectric turbine - tons of wires and metal scaffolding rotating thousands of times a minute, converting the kinetic energy of last year's rainfall into megawatts of electricity - was represented on a regular sized sheet of paper by a few abstract differential equations based on a little circuit diagram. It was a powerfully seductive way of looking at things. The reality was violent and chaotic. The abstraction was serene and understandable. The abstraction gave the comforting vision that we understood and controlled some of the powerful forces of nature and man. As sophomore electrical engineers, however, we had been shown, for example, that transformers would get hot, would hum and would sometimes explode. These were behaviors that our little circuit diagrams could not explain[1]. It had been my first tantalizing glimpse into the unknown. Into lands that lay beyond the vague and short boundaries of what we knew and had plowed into equations and diagrams.
When I started my PhD course I wanted to model "the brain". For my masters I had moved on from circuit diagrams and transformer design to signal processing, computer vision and pattern recognition. These engineering problems were always being "solved". They would be solved for this dataset and that dataset, but they would never be *solved* in the broad sense. You could write a program to recognize 90% of a set of handwritten digits, but not 95%. A human, on that set, would do about 100%. How could a human do 100%? What kind of program could model the human brain, when, for instance, it was analyzing speech? That seemed to be a good question to study. I did not know it at that time, but I had stumbled across one of the biggest mysteries of our time - a giant continent, largely untravelled and unmapped, full of mystery and surprise.
The first few months in the batlab I puttered around with this abstract bat that had an abstract brain with abstract neurons and lived in a computer (well, mostly in a dog-eared notebook). Cindy quickly introduced me to Timothy (Timmer) Horiuchi who had just joined the University of Maryland. Timmer was planning to make robot bats with brains made of resistors, capacitors and transistors. He called it the 'microchipoptera' project. When you said this was how the brain worked, and you put it on a robot and you let the robot loose in the hallway, you were putting your money where your mouth was. When everything sat inside a computer you could potentially "solve" and "explain" anything. You were modeling both the brain and the environment it would interact with. The temptation to model the brain and the environment so that they fit and worked would be great. And it would possibly lead you to a tautology: A circular argument hidden under layers of sophisticated mathematical reasoning and computer code. When you put your model brain on a robot and let it interact with the real world, there would be less opportunity to succumb to this philosophical disease. Uptil then, I had some vague idea of what bats did. I had never seen a real bat before, I had never done animal experiments, and I was a little afraid of the fact that bats bit and could carry rabies. And I didn't know if I could learn to do surgeries. On the other hand, I could code computer programs and design electrical circuits. I got very excited by the idea of this robot bat. It seemed something I could do. A science fiction fan could hardly turn down the opportunity to build a robot bat! It turned out to be an excuse. I wanted to launch an expedition into uncharted territory, but I was afraid of dark corners.
Some time into simulating electrical circuits that would be used to model attention in Timmer's Computational Sensorimotor Systems Lab I got my rabies pre-exposure shots, which meant that I could go into the animal area. More importantly I could sit in on behavioral experiments people were running in the lab. I went in one day when Jeff Triblehorn was running one of his experiments. Jeff was researching down the hall in David Yager's insect hearing laboratory. He was running a collaborative experiment with our lab studying how praying mantises responded to bat attacks. I went into this darkened room where Jeff was releasing mantises and video taping bats fly after them. I watched through the monitor. I watched the slow motion recording as the bat flapped its wings, as the mantis whirred, as the bat dipped, weaved, looped and spiraled after it. I watched as it reached out its wing in mid flight, as it tipped the flying mantis into its tail membrane, as it grabbed it in its mouth, as it pulled up inches from the floor. "I want to study THAT", I said. "I want to know THAT, I want to understand THAT. I want to build a robot model of THAT. I want to know how neurons do THAT". The bat wasn't an abstract concept anymore. It wasn't a "model system", a "problem", a "research topic". It was an animal, a living being that did something, and did it well. And I wanted to understand THAT. My desire to explore had overcome my fear of the unknown.
It turns out the University of Maryland is a great place in pursuit of THAT. In Timmer's lab while I was trying to build a model of how a bat would use its echolocation system to deal with complex environments, I ran into many questions whose answers were necessary to build a useful model. Cindy's lab with its behavioral and electrophysiological setups was the ideal place to try and answer some of those questions. I made extensive use of the high speed cameras and large flight room in the Batlab to study where bats directed their sonar beam while chasing insects. During this time, another researcher in the lab, Murat Aytekin, got intensely interested in the question of how a bat can use echo information to localize objects. In collaboration with Jonathan Simon over at the department of Electrical Engineering, Murat and Cindy came up with a general theory of how animals could use sound alone to develop a sense of space. A pertinent question, since in bats vision is a low acuity sense, whereas echolocation seems to be an extremely fine grained sense, spatially.
During the course of my experiments Cindy introduced me to P.S. Krishnaprasad. PSK studies control systems of both the inanimate and animate kinds. His particular speciality is in taking complex real world systems and applying strange and wonderful results from the world of mathematics to pull out (of the hat as it were) an analytical understanding of, say fish schooling, bats swarming or people on uni-cycles. Together, PSK, Timmer, Cindy and I showed mathematically how the trajectory bats follow when chasing an erratically moving prey is time-optimal, a useful thing, if you only have a fraction of a second to catch your lunch.
During this period, as I was eagerly foraging in this mysterious land of bat behavior I was impressed by how important the whole community is to one's research. At 11:00am, every Friday, during the school year, everyone in Maryland interested in the neural basis of behavior gathers to hear talks by people at Maryland. This diversity of ears makes for a challenging presentation, since a talk on bat echolocation should be accessible to people working on molecular mechanisms of learning in rats (Betsy Quinlan's lab) as well as people mapping spatio-temporal receptive fields in ferret auditory cortex (Shihab Shamma's tribe). However, from this diverse and lively group will come diverse and lively comments on your approach and your results. I have gotten, on several occasions, questions and comments that forced me to think outside my box. Jens Heberholz, for example, who is currently studying social interactions in crayfish, looks at my results on sonar beam patterns in echolocating bats and thinks of evolutionary questions. Stephen V. David looks at the same data and thinks of parallels to the visual system in primates, which he studied before joining here. Kate McCloud, from Catherine Carr's lab asks challenging questions about anatomical substrates of sonar beam shape. Todd Troyer looks at some pursuit data from bat-insect chases and thinks of patterns of play in American Football. Not only is doing the science fun, but so is presenting it to such a lateral thinking audience.
Right, now, as I write this I'm trying to build a model of a subset of neurons in the inferior colliculus of the bat. People suspect that these neurons have something to do with sound localizaton in the bat, but they have some puzzling timing properties. I'm wondering if I put together a model of these neurons whether their projected population response will tell me something that is not obvious from their known single unit properties. But I'm stymied by some conflicting reports in the literature I'm going through. Perhaps a quick presentation at one of those 11:00am Friday meetings will help me come up with some ideas to get unstuck. It might even help me avoid a certain philosophical disease that afflicts people making computer models...
[1] These particular phenomena are actually resonably well understood and modeled, unlike the human brain.
(c) Kaushik Ghose 2007