Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI.

We study a range of neural dynamics under variations in biophysical parameters underlying extended Morris-Lecar and Hodgkin-Huxley models in three gating variables. The extended models are implemented in NeuroDyn, a four neuron, twelve synapse continuous-time analog VLSI programmable neural emulation platform with generalized channel kinetics and biophysical membrane dynamics. The dynamics exhibit a wide range of time scales extending beyond 100 ms neglected in typical silicon models of tonic spiking neurons. Circuit simulations and measurements show transition from tonic spiking to tonic bursting dynamics through variation of a single conductance parameter governing calcium recovery. We similarly demonstrate transition from graded to all-or-none neural excitability in the onset of spiking dynamics through the variation of channel kinetic parameters governing the speed of potassium activation. Other combinations of variations in conductance and channel kinetic parameters give rise to phasic spiking and spike frequency adaptation dynamics. The NeuroDyn chip consumes 1.29 mW and occupies 3 mm × 3 mm in 0.5 µm CMOS, supporting emerging developments in neuromorphic silicon-neuron interfaces.

OLAM: A wearable, non-contact sensor for continuous heart-rate and activity monitoring.

A wearable, multi-modal sensor is presented that can non-invasively monitor a patient's activity level and heart function concurrently for more than a week. The 4 in(2) sensor incorporates both a non-contact heartrate sensor and a 5-axis inertial measurement unit (IMU), allowing simultaneous heart, respiration, and movement monitoring without requiring physical contact with the skin [1]. Hence, this Oregon State University Life and Activity Monitor (OLAM) provides the unique opportunity to combine motion data with heart-rate information, enabling assessment of actual physical activity beyond conventional movement sensors. OLAM also provides a unique platform for non-contact sensing, enabling the filtering of movement artifacts generated by the non-contact capacitive interface, using the IMU data as a movement noise channel. Intended to be used in clinical trials for weeks at a time with no physician intervention, the OLAM allows continuous non-invasive monitoring of patients, providing the opportunity for long-term observation into a patient's physical activity and subtle longitudinal changes.

Wireless micropower instrumentation for multimodal acquisition of electrical and chemical neural activity.

The intricate coupling between electrical and chemical activity in neural pathways of the central nervous system, and the implication of this coupling in neuropathologies, such as Parkinson's disease, motivates simultaneous monitoring of neurochemical and neuropotential signals. However, to date, neurochemical sensing has been lacking in integrated clinical instrumentation as well as in brain-computer interfaces (BCI). Here, we present an integrated system capable of continuous acquisition of data modalities in awake, behaving subjects. It features one channel each of a configurable neuropotential and a neurochemical acquisition system. The electrophysiological channel is comprised of a 40-dB gain, fully differential amplifier with tunable bandwidth from 140 Hz to 8.2 kHz. The amplifier offers input-referred noise below 2 muV rms for all bandwidth settings. The neurochemical module features a picoampere sensitivity potentiostat with a dynamic range spanning six decades from picoamperes to microamperes. Both systems have independent on-chip, configurable DeltaSigma analog-to-digital converters (ADCs) with programmable digital gain and resolution. The system was also interfaced to a wireless power harvesting and telemetry module capable of powering up the circuits, providing clocks for ADC operation, and telemetering out the data at up to 32 kb/s over 3.5 cm with a bit-error rate of less than 10(-5). Characterization and experimental results from the electrophysiological and neurochemical modules as well as the full system are presented.

Micropower CMOS Integrated Low-Noise Amplification, Filtering, and Digitization of Multimodal Neuropotentials.

Electrical activity in the brain spans a wide range of spatial and temporal scales, requiring simultaneous recording of multiple modalities of neurophysiological signals in order to capture various aspects of brain state dynamics. Here, we present a 16-channel neural interface integrated circuit fabricated in a 0.5 mum 3M2P CMOS process for selective digital acquisition of biopotentials across the spectrum of neural signal modalities in the brain, ranging from single spike action potentials to local field potentials (LFP), electrocorticograms (ECoG), and electroencephalograms (EEG). Each channel is composed of a tunable bandwidth, fixed gain front-end amplifier and a programmable gain/resolution continuous-time incremental DeltaSigma analog-to-digital converter (ADC). A two-stage topology for the front-end voltage amplifier with capacitive feedback offers independent tuning of the amplifier bandpass frequency corners, and attains a noise efficiency factor (NEF) of 2.9 at 8.2 kHz bandwidth for spike recording, and a NEF of 3.2 at 140 Hz bandwidth for EEG recording. The amplifier has a measured midband gain of 39.6 dB, frequency response from 0.2 Hz to 8.2 kHz, and an input-referred noise of 1.94 muV rms while drawing 12.2 muA of current from a 3.3 V supply. The lower and higher cutoff frequencies of the bandpass filter are adjustable from 0.2 to 94 Hz and 140 Hz to 8.2 kHz, respectively. At 10-bit resolution, the ADC has an SNDR of 56 dB while consuming 76 muW power. Time-modulation feedback in the ADC offers programmable digital gain (1-4096) for auto-ranging, further improving the dynamic range and linearity of the ADC. Experimental recordings with the system show spike signals in rat somatosensory cortex as well as alpha EEG activity in a human subject.

VLSI Potentiostat Array With Oversampling Gain Modulation for Wide-Range Neurotransmitter Sensing.

A 16-channel current-measuring very large-scale integration (VLSI) sensor array system for highly sensitive electrochemical detection of electroactive neurotransmiters like dopamine and nitric-oxide is presented. Each channel embeds a current integrating potentiostat within a switched-capacitor first-order single-bit delta-sigma modulator implementing an incremental analog-to-digital converter. The duty-cycle modulation of current feedback in the delta-sigma loop together with variable oversampling ratio provide a programmable digital range selection of the input current spanning over six orders of magnitude from picoamperes to microamperes. The array offers 100-fA input current sensitivity at 3.4-muW power consumption per channel. The operation of the 3 mm times3 mm chip fabricated in 0.5-mum CMOS technology is demonstrated with real-time multichannel acquisition of neurotransmitter concentration.

Kerneltron: support vector "machine" in silicon.

Detection of complex objects in streaming video poses two fundamental challenges: training from sparse data with proper generalization across variations in the object class and the environment; and the computational power required of the trained classifier running real-time. The Kerneltron supports the generalization performance of a support vector machine (SVM) and offers the bandwidth and efficiency of a massively parallel architecture. The mixed-signal very large-scale integration (VLSI) processor is dedicated to the most intensive of SVM operations: evaluating a kernel over large numbers of vectors in high dimensions. At the core of the Kerneltron is an internally analog, fine-grain computational array performing externally digital inner-products between an incoming vector and each of the stored support vectors. The three-transistor unit cell in the array combines single-bit dynamic storage, binary multiplication, and zero-latency analog accumulation. Precise digital outputs are obtained through oversampled quantization of the analog array outputs combined with bit-serial unary encoding of the digital inputs. The 256 input, 128 vector Kerneltron measures 3 mm/spl times/3mm in 0.5 /spl mu/m CMOS, delivers 6.5 GMACS throughput at 5.9 mW power, and attains 8-bit output resolution.

Probabilistic synaptic weighting in a reconfigurable network of VLSI integrate-and-fire neurons.

We present a scheme for implementing highly-connected, reconfigurable networks of integrate-and-fire neurons in VLSI. Neural activity is encoded by spikes, where the address of an active neuron is communicated through an asynchronous request and acknowledgement cycle. We employ probabilistic transmission of spikes to implement continuous-valued synaptic weights, and memory-based look-up tables to implement arbitrary interconnection topologies. The scheme is modular and scalable, and lends itself to the implementation of multi-chip network architectures. Results from a prototype system with 1024 analog VLSI integrate-and-fire neurons, each with up to 128 probabilistic synapses, demonstrate these concepts in an image processing task.

Microscale Adaptive Optics: Wave-Front Control with a mu-Mirror Array and a VLSI Stochastic Gradient Descent Controller.

The performance of adaptive systems that consist of microscale on-chip elements [microelectromechanical mirror (mu-mirror) arrays and a VLSI stochastic gradient descent microelectronic control system] is analyzed. The mu-mirror arrays with 5 x 5 and 6 x 6 actuators were driven with a control system composed of two mixed-mode VLSI chips implementing model-free beam-quality metric optimization by the stochastic parallel perturbative gradient descent technique. The adaptation rate achieved was near 6000 iterations/s. A secondary (learning) feedback loop was used to control system parameters during the adaptation process, further increasing the adaptation rate.

Adaptive optics based on analog parallel stochastic optimization: analysis and experimental demonstration

Wave-front distortion compensation using direct system performance metric optimization is studied both theoretically and experimentally. It is shown how different requirements for wave-front control can be incorporated, and how information from different wave-front sensor types can be fused, within a generalized gradient descent optimization paradigm. In our experiments a very-large-scale integration (VLSI) system implementing a simultaneous perturbation stochastic approximation optimization algorithm was applied for real-time adaptive control of multielement wave-front correctors. The custom-chip controller is used in two adaptive laser beam focusing systems, one with a 127-element liquid-crystal phase modulator and the other with beam steering and 37-control channel micromachined deformable mirrors. The submillisecond response time of the micromachined deformable mirror and the parallel nature of the analog VLSI control architecture provide for high-speed adaptive compensation of dynamical phase aberrations of a laser beam under conditions of strong intensity scintillations. Experimental results demonstrate improvement of laser beam quality at the receiver plane in the spectral band up to 60 Hz.

An analog VLSI recurrent neural network learning a continuous-time trajectory.

Real-time algorithms for gradient descent supervised learning in recurrent dynamical neural networks fail to support scalable VLSI implementation, due to their complexity which grows sharply with the network dimension. We present an alternative implementation in analog VLSI, which employs a stochastic perturbation algorithm to observe the gradient of the error index directly on the network in random directions of the parameter space, thereby avoiding the tedious task of deriving the gradient from an explicit model of the network dynamics. The network contains six fully recurrent neurons with continuous-time dynamics, providing 42 free parameters which comprise connection strengths and thresholds. The chip implementing the network includes local provisions supporting both the learning and storage of the parameters, integrated in a scalable architecture which can be readily expanded for applications of learning recurrent dynamical networks requiring larger dimensionality. We describe and characterize the functional elements comprising the implemented recurrent network and integrated learning system, and include experimental results obtained from training the network to represent a quadrature-phase oscillator.

Analysis and verification of an analog VLSI incremental outer-product learning system.

An architecture is described for the microelectronic implementation of arbitrary outer-product learning rules in analog floating-gate CMOS matrix-vector multiplier networks. The weights are stored permanently on floating gates and are updated under uniform UV illumination with a general incremental analog four-quadrant outer-product learning scheme, performed locally on-chip by a single transistor per matrix element on average. From the mechanism of floating gate relaxation under UV radiation, the authors derive the learning parameters and their dependence on the illumination level and circuit parameters. It is shown that the weight increments consists of two parts: one term contains the outer product of two externally applied learning vectors; the other part represents a uniform weight decay, with time constant originating from the floating gate relaxation. The authors address the implementation of supervised and unsupervised learning algorithms with emphasis on the delta rule. Experimental results from a simple implementation of the delta rule on an 8x7 linear network are included.