I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

http://ilab.usc.edu/publications/doc/Itti_Koch00vr.pdf

A saliency-based search mechanism for overt and covert shifts of visual attention.

Synchronization phenomena in large populations of interacting elements are the subject of intense research efforts in physical, biological, chemical, and social systems. A successful approach to the problem of synchronization consists of modeling each member of the population as a phase oscillator. In this review, synchronization is analyzed in one of the most representative models of coupled phase oscillators, the Kuramoto model. A rigorous mathematical treatment, specific numerical methods, and many variations and extensions of the original model that have appeared in the last few years are presented. Relevant applications of the model in different contexts are also included.

/pdf/the-kuramoto-model-a-simple-paradigm-for-synchronization-2akwf0t24m.pdf

The Kuramoto model: A simple paradigm for synchronization phenomena

A survey of models, analysis tools and compensation methods for the control of machines with friction

Human skin is a remarkable organ. It consists of an integrated, stretchable network of sensors that relay information about tactile and thermal stimuli to the brain, allowing us to maneuver within our environment safely and effectively. Interest in large-area networks of electronic devices inspired by human skin is motivated by the promise of creating autonomous intelligent robots and biomimetic prosthetics, among other applications. The development of electronic networks comprised of flexible, stretchable, and robust devices that are compatible with large-area implementation and integrated with multiple functionalities is a testament to the progress in developing an electronic skin (e-skin) akin to human skin. E-skins are already capable of providing augmented performance over their organic counterpart, both in superior spatial resolution and thermal sensitivity. They could be further improved through the incorporation of additional functionalities (e.g., chemical and biological sensing) and desired properties (e.g., biodegradability and self-powering). Continued rapid progress in this area is promising for the development of a fully integrated e-skin in the near future.

25th Anniversary Article: The Evolution of Electronic Skin (E-Skin): A Brief History, Design Considerations, and Recent Progress

Biological systems are able to perform movements in unpredictable environments more elegantly than traditionally engineered robotic systems. A current limitation of robotic systems is their inability to simultaneously and independently control both joint angle and joint stiffness without electromechanical feedback loops, which can reduce system stability. In this paper, we describe the development and physical implementation of a servo-actuated robotic joint that uses antagonistic, series-elastic actuation with novel nonlinear spring mechanisms. These mechanisms form a real-time mechanical feedback loop that provides the joint with angle and stiffness control through differential and common-mode actuation of the servos, respectively. This approach to joint control emulates the mechanics of antagonistic muscle groups used by animals, and we experimentally show that it is capable of independently controlling both joint angle and joint stiffness using a simple open-loop control algorithm.

Biologically Inspired Joint Stiffness Control

We have designed, fabricated, and tested an analog integrated-circuit architecture to implement the conductance-based dynamics that model the electrical activity of neurons. The dynamics of this architecture are in accordance with the Hodgkin-Huxley formalism, a widely exploited, biophysically plausible model of the dynamics of living neurons. Furthermore the architecture is modular and compact in size so that we can implement networks of silicon neurons, each of desired complexity, on a single integrated circuit. We present in this paper a six-conductance silicon-neuron implementation, and characterize it in relation to the Hodgkin-Huxley formalism. This silicon neuron incorporates both fast and slow ionic conductances, which are required to model complex oscillatory behaviors (spiking, bursting, subthreshold oscillations).

A multiconductance silicon neuron with biologically matched dynamics

The complexity of analog VLSI systems is often limited by the number of pins on a chip rather than by the die area. Currently, many analog parameters and biases are stored off-chip. Moving parameter storage on-chip could save pins and allow us to create complex programmable analog systems. In this paper, we present a design for an on-chip nonvolatile analog memory cell that can be configured in addressable arrays and programmed easily. We use floating-gate MOS transistors to store charge, and we use the processes of tunneling and hot-electron injection to program values. We have fabricated two versions of this design: one with an nFET injection mechanism and one with a pFET injection mechanism. With these designs, we achieve greater than 13-bit output precision with a 39-dB power-supply rejection ratio and no crosstalk between memory cells.

A CMOS programmable analog memory-cell array using floating-gate circuits

Numerous applications in neuroscience research and neural prosthetics, such as electrocorticogram (ECoG) recording and retinal prosthesis, involve electrical interactions with soft excitable tissues using a surface recording and/or stimulation approach. These applications require an interface that is capable of setting up high-throughput communications between the electrical circuit and the excitable tissue and that can dynamically conform to the shape of the soft tissue. Being a compliant material with mechanical impedance close to that of soft tissues, polydimethylsiloxane (PDMS) offers excellent potential as a substrate material for such neural interfaces. This paper describes an integrated technology for fabrication of PDMS-based stretchable microelectrode arrays (MEAs). Specifically, as an integral part of the fabrication process, a stretchable MEA is directly fabricated with a rigid substrate, such as a thin printed circuit board (PCB), through an innovative bonding technology-via-bonding-for integrated packaging. This integrated strategy overcomes the conventional challenge of high-density packaging for this type of stretchable electronics. Combined with a high-density interconnect technology developed previously, this stretchable MEA technology facilitates a high-resolution, high-density integrated system solution for neural and muscular surface interfacing. In this paper, this PDMS-based integrated stretchable MEA (isMEA) technology is demonstrated by an example design that packages a stretchable MEA with a small PCB. The resulting isMEA is assessed for its biocompatibility, surface conformability, electrode impedance spectrum, and capability to record muscle fiber activity when applied epimysially.

A PDMS-Based Integrated Stretchable Microelectrode Array (isMEA) for Neural and Muscular Surface Interfacing

Analog very large-scale integrated (VLSI) technology can be used not only to study and simulate biological systems, but also to emulate them in designing artificial sensory systems. A methodology for building these systems in CMOS VLSI technology has been developed using analog micropower circuit elements that can be hierarchically combined. Using this methodology, experimental VLSI chips of visual and motor subsystems have been designed and fabricated. These chips exhibit behavior similar to that of biological systems, and perform computations useful for artificial sensory systems. >

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Stephen P. DeWeerth

Papers

Biologically Inspired Joint Stiffness Control

A multiconductance silicon neuron with biologically matched dynamics

A CMOS programmable analog memory-cell array using floating-gate circuits

A PDMS-Based Integrated Stretchable Microelectrode Array (isMEA) for Neural and Muscular Surface Interfacing

Implementing neural architectures using analog VLSI circuits