Neural Coding of Electric Field Amplitude Modulations in Eigenmannia Electric Fish
Gabriel Kreiman, Ruediger Krahe (Department of Biology, U.C. Riverside), Fabrizio Gabbiani, Walter Metzner (Department of Biology, U.C. Riverside), Christof Koch

We are using the electric fish as a model to study the encoding of time-varying signals by single and multiple neurons. Our approach combines signal detection and information-theoretic ideas to quantify the amount of information conveyed by sensory afferents and its targets about amplitude modulations in the electric field. We have shown that single sensory neurons robustly encode a significant proportion of the incoming signal while the pyramidal cell targets extract specific features of the signal. We are currently performing a quantitative study of the possibility of extracting these features by coincidence detection of two pyramidal cells. (full report)

Psychobiophysics of Transcranial Magnetic Stimulation
Yukiyasu Kamitani, Shinsuke Shimojo

We investigate the relationship between human visual experience and underlying neuronal electrical activity, using transcranial magnetic stimulation (TMS). We explore methods that make the effect of TMS on the visual cortex directly visible, to look at purely cortical activity underlying our conscious visual experience. We also develop a biophysical theory to simulate the effect of magnetic stimulation on single neurons. Based on it, we create compartmental models of realistic cortical neurons to find neural activity underlying perceptual effects of TMS. (full report)

Toward Prosthetic Systems Controlled by Parietal Cortex
Krishna Shenoy, Sohaib Kureshi, Richard Andersen, Shiyan Cao, Joel W. Burdick

At present there are no satisfactory treatments or assistive aids for people suffering from neurological disorders such as stroke, ALS, or spinal cord injuries. Neuroscientists have taken great strides in the past few decades toward uncovering basic principles underlying our ability to see and move. The combination of these discoveries and the revolutionary advances in computer technology have led to an emerging view that neural prosthetics --- or electronic interfaces with the brain --- may one day be possible. This project aims to demonstrate the potential for neural prosthetics to help patients with upper spinal cord injury, which results in the loss of arm movements. Andersen and colleagues recently discovered a cortical area in monkeys and humans that encodes the next intended arm movement. This area is ideally suited to provide high-level control signals for guiding real or prosthetic arms. We propose to implant chronic electrode arrays in this region of monkey cortex and to record neural activity generated during reaching arm movements. We will process these neural signals in real-time to construct control signals for guiding a prosthetic arm. Combining behaving-monkey electrophysiology techniques, state-of-the-art electrode array technology, and feedback control systems should provide the foundation on which to build neural prosthetics for humans. Below we outline our major aims and, in the achievements section, we describe our progress in the past year. (full report)

Sensing and Control for Robotic Fish Locomotion
Richard Mason, Joel Burdick

We are studying issues in fluid mechanics, nonlinear control, and sensing that are necessary for the development of self-propelled robot fish. (full report)

Awareness-Based Computation: The Bin Packing Problem
Greg Billock, Demetri Psaltis, Christof Koch

In previous work (see report entitled Awareness-Based Computation), we have investigated the impact of using approaches to simulated environments/problems inspired by the way human beings use awareness and attentional mechanisms to interact with a complex world. In this work, we explore how this works in the context of a familiar computer science problem: bin packing. As an abstract problem, the bin-packing problem has the advantage of having been subjected to extensive analysis and so much is known about it. It is a very important practical problem, as well, with applications to cutting stock, machine and job scheduling, parallel processing scheduling, FPGA layout, loading problems, and more. By using ideas about reduced representations of what is most important in an on-line solution of the problem, we are able to devise a heuristic which outperforms existing heuristics, and understand how and why it does so. (full report)

Awareness-Based Computation
George Barbastathis, Greg Billock, Demetri Psaltis, Christof Koch

In this project, we are developing design principles for intelligent systems that can interact with very complex, variable, and poorly modeled environments. In doing so, we draw inspiration from the discoveries of neurobiology relating to the role of attention and awareness. These aspects of biological processing systems is key in conferring on them the ability to function in such high-dimensional real-world environments. At the heart of our architecture lies the idea of adapting an abstraction of awareness with which to endow artificial man-made systems. (full report)

Visual Sensor With Resolution Enhancement by Mechanical Vibrations
Oliver Landolt, Ania Mitros, Christof Koch

The resolution of both biological and man-made vision systems is limited by the finite spacing between receptors. This limit can be overcome by applying continuous low-amplitude vibrations to the image or taking advantage of existing vibrations in the environment. Some animals rely on this principle for improved visual resolution. We are applying it to a novel CMOS visual sensor to increase resolution and decrease fixed pattern noise. (full report)

Micromachined Gyroscope Using Operating Principles from the Fly's Halteres
Oliver Landolt, Zhigang Han, Christof Koch, Yu-Chong Tai

We are developing a surface micromachined 2D angular velocity sensor -- also known as gyroscope -- with the intention of minimizing power consumption. By using a detection principle inspired by the fly's haltere system, we expect our sensor to tolerate a higher noise level than previous designs for detecting the direction of the axis of rotation, thereby enabling a significant reduction of supply voltage and power consumption. Another feature is that the mechanical structure will be fabricated with a material called parylene using a novel technology developed in-house. The target application is flight control in extremely small air vehicles. (full report)

Guiding a Robot with an Analog VLSI Motion Sensor Based on the Visual System of the Fly
Reid Harrison, Christof Koch

Sensing visual motion gives a creature valuable information about its interactions with the environment. Flies in particular use visual motion information to navigate through turbulent air, avoid obstacles, and land safely. Mobile robots are ideal candidates for using this sensory modality to enhance their performance, but so far have been limited by the computational expense of processing video. Also, the complex structure of natural visual scenes poses an algorithmic challenge for extracting useful information in a robust manner. We address both issues by creating a small, low-power visual sensor with integrated analog parallel processing to extract motion in real-time. Because our architecture is based on biological motion detectors, we gain the advantages of this highly evolved system: a design that robustly and continuously extracts relevant information from its visual environment. We show that this sensor is suitable for use in the real world, and demonstrate its ability to compensate for an imperfect motor system in the control of an autonomous robot. The sensor attenuates open-loop rotation by a factor of 31 with less than 1 mW power dissipation. (full report)