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

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

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

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

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

Measuring and modeling energy and power consumption in living microbial cells with a synthetic ATP reporter

Background: Adenosine triphosphate (ATP) is the main energy carrier in living organisms, critical for metabolism and essential physiological processes. In humans, abnormal regulation of energy levels (ATP concentration) and power consumption (ATP consumption flux) in cells is associated with numerous diseases from cancer, to viral infection and immune dysfunction, while in microbes it influences their responses to drugs and other stresses. The measurement and modeling of ATP dynamics in cells is therefore a critical component in understanding fundamental physiology and its role in pathology. Despite the importance of ATP, our current understanding of energy dynamics and homeostasis in living cells has been limited by the lack of easy-to-use ATP sensors and the lack of models that enable accurate estimates of energy and power consumption related to these ATP dynamics. Here we describe a dynamic model and an ATP reporter that tracks ATP in E. coli over different growth phases. Results: The reporter is made by fusing an ATP-sensing rrnB P1 promoter with a fast-folding and fast-degrading GFP. Good correlations between reporter GFP and cellular ATP were obtained in E. coli growing in both minimal and rich media and in various strains. The ATP reporter can reliably monitor bacterial ATP dynamics in response to nutrient availability. Fitting the dynamics of experimental data corresponding to cell growth, glucose, acetate, dissolved oxygen, and ATP yielded a mathematical and circuit model. This model can accurately predict cellular energy and power consumption under various conditions. We found that cellular power consumption varies significantly from approximately 0.8 and 0.2 million ATP/s for a tested strain during lag and stationary phases to 6.4 million ATP/s during exponential phase, indicating ~ 8–30-fold changes of metabolic rates among different growth phases. Bacteria turn over their cellular ATP pool a few times per second during the exponential phase and slow this rate by ~ 2–5-fold in lag and stationary phases. Conclusion: Our rrnB P1-GFP reporter and kinetic circuit model provide a fast and simple way to monitor and predict energy and power consumption dynamics in bacterial cells, which can impact fundamental scientific studies and applied medical treatments in the future

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