Refractory Neuron Circuits

Neural networks typically use an abstraction of the behaviour of a biological neuron, in which the continuously varying mean firing rate of the neuron is presumed to carry information about the neuron's time-varying state of excitation. However, the detailed timing of action potentials is known to be important in many biological systems. To build electronic models of such systems, one must have well-characterized neuron circuits that capture the essential behaviour of real neurons in biological systems. In this paper, we describe two simple and compact circuits that fire narrow action potentials with controllable thresholds, pulse widths, and refractory periods. Both circuits are well suited as high-level abstractions of spiking neurons. We have used the first circuit to generate action potentials from a current input, and have used the second circuit to delay and propagate action potentials in an axon delay line. The circuit mechanisms are derived from the behaviour of sodium and potassium conductances in nerve membranes of biological neurons. The first circuit models behaviours at the axon hillock; the second circuit models behaviour at the node of Ranvier in biological neurons. The circuits have been implemented in a 2-micron double-poly CMOS process. Results are presented from working chips

An analog VLSI cochlea with new transconductance amplifiers and nonlinear gain control

We show data from a working 45-stage analog VLSI cochlea, built on a 2.2 mm×2.2 mm tiny chip. The novel architectural features in this cochlea are: (1) The use of a wide-linear-range low-noise subthreshold transconductance amplifier. (2) The use of “fuse-like” nonlinear positive-feedback amplification in the second-order cochlear filter. Several new circuit techniques used in the design are described here. The fuse nonlinearity shuts off the positive-feedback amplification at large signal levels instead of merely saturating it, like in prior designs, and leads to increased adaptation and improved large-signal stability in the filter. The fuse filter implements a functional model of gain control due to outer hair cells in the biological cochlea. We present data for travelling-wave patterns in our silicon cochlea that reproduce linear and nonlinear effects in the biological cochlea

Bioelectronic measurement and feedback control of molecules in living cells

We describe an electrochemical measurement technique that enables bioelectronic measurements of reporter proteins in living cells as an alternative to traditional optical fluorescence. Using electronically programmable microfluidics, the measurement is in turn used to control the concentration of an inducer input that regulates production of the protein from a genetic promoter. The resulting bioelectronic and microfluidic negative-feedback loop then serves to regulate the concentration of the protein in the cell. We show measurements wherein a user-programmable set-point precisely alters the protein concentration in the cell with feedback-loop parameters affecting the dynamics of the closed-loop response in a predictable fashion. Our work does not require expensive optical fluorescence measurement techniques that are prone to toxicity in chronic settings, sophisticated time-lapse microscopy, or bulky/expensive chemo-stat instrumentation for dynamic measurement and control of biomolecules in cells. Therefore, it may be useful in creating a: Cheap, portable, chronic, dynamic, and precise all-electronic alternative for measurement and control of molecules in living cells.National Science Foundation (U.S.) (Grant CCF 1124247)National Science Foundation (U.S.) (Grant 1606406

White Noise in MOS Transistors and Resistors

Shot noise and thermal noise have long been considered the results of two distinct mechanisms, but they aren't

Nonvolatile correction of Q-offsets and instabilities in cochlear filters

We present a feedback circuit that performs nonvolatile correction of instabilities and resonant-gain offsets (Q-offsets) in individual cochlear filters. The subthreshold CMOS circuit adapts using analog floating-gate technology. We present experimental data from a working chip that illustrates the performance of the circuit. We discuss how to extend our work to do very long-term gain control in the silicon cochlea. Positive-feedback circuits, such as our cochlear filters, are very sensitive to parameter variations. This potential problem becomes an advantage in our corrective feedback loop where the hypersensitivity behaves merely like high loop gain

Efficient Universal Computing Architectures for Decoding Neural Activity

The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain– machine interfaces (BMIs). Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they should also minimize power dissipation in order to avoid damaging neural tissue; and they should transmit data wirelessly in order to minimize the risk of infection associated with chronic, transcutaneous implants. Electronic architectures for brain– machine interfaces must therefore minimize size and power consumption, while maximizing the ability to compress data to be transmitted over limited-bandwidth wireless channels. Here we present a system of extremely low computational complexity, designed for real-time decoding of neural signals, and suited for highly scalable implantable systems. Our programmable architecture is an explicit implementation of a universal computing machine emulating the dynamics of a network of integrate-and-fire neurons; it requires no arithmetic operations except for counting, and decodes neural signals using only computationally inexpensive logic operations. The simplicity of this architecture does not compromise its ability to compress raw neural data by factors greater than . We describe a set of decoding algorithms based on this computational architecture, one designed to operate within an implanted system, minimizing its power consumption and data transmission bandwidth; and a complementary set of algorithms for learning, programming the decoder, and postprocessing the decoded output, designed to operate in an external, nonimplanted unit. The implementation of the implantable portion is estimated to require fewer than 5000 operations per second. A proof-of-concept, 32-channel field-programmable gate array (FPGA) implementation of this portion is consequently energy efficient. We validate the performance of our overall system by decoding electrophysiologic data from a behaving rodent.United States. National Institutes of Health (Grant NS056140

Low-Power Circuits for Brain–Machine Interfaces

This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinson’s disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; low-power radio-frequency (RF) impedance-modulation circuits for data telemetry that minimize power consumption of implanted systems in the body; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons with power-conserving sleep modes and awake modes. Experimental results from chips that have stimulated and recorded from neurons in the zebra finch brain and results from RF power-link, RF data-link, electrode- recording and electrode-stimulating systems are presented. Simulations of analog learning circuits that have successfully decoded prerecorded neural signals from a monkey brain are also presented

Consequences of converting graded to action potentials upon neural information coding and energy efficiency

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a ‘footprint’ in the generator potential that obscures incoming signals. These three processes reduce information rates by ~50% in generator potentials, to ~3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation

Neural Decision Boundaries for Maximal Information Transmission

We consider here how to separate multidimensional signals into two categories, such that the binary decision transmits the maximum possible information transmitted about those signals. Our motivation comes from the nervous system, where neurons process multidimensional signals into a binary sequence of responses (spikes). In a small noise limit, we derive a general equation for the decision boundary that locally relates its curvature to the probability distribution of inputs. We show that for Gaussian inputs the optimal boundaries are planar, but for non-Gaussian inputs the curvature is nonzero. As an example, we consider exponentially distributed inputs, which are known to approximate a variety of signals from natural environment.Comment: 5 pages, 3 figure

A wirelessly powered and controlled device for optical neural control of freely-behaving animals

Optogenetics, the ability to use light to activate and silence specific neuron types within neural networks in vivo and in vitro, is revolutionizing neuroscientists' capacity to understand how defined neural circuit elements contribute to normal and pathological brain functions. Typically, awake behaving experiments are conducted by inserting an optical fiber into the brain, tethered to a remote laser, or by utilizing an implanted light-emitting diode (LED), tethered to a remote power source. A fully wireless system would enable chronic or longitudinal experiments where long duration tethering is impractical, and would also support high-throughput experimentation. However, the high power requirements of light sources (LEDs, lasers), especially in the context of the extended illumination periods often desired in experiments, precludes battery-powered approaches from being widely applicable. We have developed a headborne device weighing 2 g capable of wirelessly receiving power using a resonant RF power link and storing the energy in an adaptive supercapacitor circuit, which can algorithmically control one or more headborne LEDs via a microcontroller. The device can deliver approximately 2 W of power to the LEDs in steady state, and 4.3 W in bursts. We also present an optional radio transceiver module (1 g) which, when added to the base headborne device, enables real-time updating of light delivery protocols; dozens of devices can be controlled simultaneously from one computer. We demonstrate use of the technology to wirelessly drive cortical control of movement in mice. These devices may serve as prototypes for clinical ultra-precise neural prosthetics that use light as the modality of biological control.National Institutes of Health (U.S.) (NIH Director’s New Innovator Award (DP2OD002002))National Institutes of Health (U.S.) (Grant 1R01DA029639)National Institutes of Health (U.S.) (Grant 1RC1MH088182)National Institutes of Health (U.S.) (Grant 1RC2DE020919)National Institutes of Health (U.S.) (Grant 1R01NS067199)National Institutes of Health (U.S.) (Grant 1R43NS070453)National Science Foundation (U.S.) (CAREER award)National Science Foundation (U.S.) (NSF Grant DMS 1042134)National Science Foundation (U.S.) (NSF Grant DMS 0848804)National Science Foundation (U.S.) (NSF Grant EFRI 0835878)Benesse FoundationGoogle (Firm)Dr. Gerald Burnett and Marjorie BurnettUnited States. Dept. of Defense (CDMRP PTSD Program)Massachusetts Institute of TechnologyBrain & Behavior Research FoundationAlfred P. Sloan FoundationSociety for NeuroscienceMassachusetts Institute of Technology. Media LaboratoryMcGovern Institute for Brain Research at MITWallace H. Coulter Foundatio