375 research outputs found
Multiplexed, High Density Electrophysiology with Nanofabricated Neural Probes
Extracellular electrode arrays can reveal the neuronal network correlates of behavior with single-cell, single-spike, and sub-millisecond resolution. However, implantable electrodes are inherently invasive, and efforts to scale up the number and density of recording sites must compromise on device size in order to connect the electrodes. Here, we report on silicon-based neural probes employing nanofabricated, high-density electrical leads. Furthermore, we address the challenge of reading out multichannel data with an application-specific integrated circuit (ASIC) performing signal amplification, band-pass filtering, and multiplexing functions. We demonstrate high spatial resolution extracellular measurements with a fully integrated, low noise 64-channel system weighing just 330 mg. The on-chip multiplexers make possible recordings with substantially fewer external wires than the number of input channels. By combining nanofabricated probes with ASICs we have implemented a system for performing large-scale, high-density electrophysiology in small, freely behaving animals that is both minimally invasive and highly scalable
An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG
In this work, we present a neuromorphic system that combines for the first
time a neural recording headstage with a signal-to-spike conversion circuit and
a multi-core spiking neural network (SNN) architecture on the same die for
recording, processing, and detecting High Frequency Oscillations (HFO), which
are biomarkers for the epileptogenic zone. The device was fabricated using a
standard 0.18m CMOS technology node and has a total area of 99mm. We
demonstrate its application to HFO detection in the iEEG recorded from 9
patients with temporal lobe epilepsy who subsequently underwent epilepsy
surgery. The total average power consumption of the chip during the detection
task was 614.3W. We show how the neuromorphic system can reliably detect
HFOs: the system predicts postsurgical seizure outcome with state-of-the-art
accuracy, specificity and sensitivity (78%, 100%, and 33% respectively). This
is the first feasibility study towards identifying relevant features in
intracranial human data in real-time, on-chip, using event-based processors and
spiking neural networks. By providing "neuromorphic intelligence" to neural
recording circuits the approach proposed will pave the way for the development
of systems that can detect HFO areas directly in the operation room and improve
the seizure outcome of epilepsy surgery.Comment: 16 pages. A short video describing the rationale underlying the study
can be viewed on https://youtu.be/NuAA91fdma
700mV low power low noise implantable neural recording system design
This dissertation presents the work for design and implementation of a low power, low noise neural recording system consisting of Bandpass Amplifier and Pipelined Analog to Digital Converter (ADC) for recording neural signal activities. A low power, low noise two stage neural amplifier for use in an intelligent Radio-Frequency Identification (RFID) based on folded cascode Operational Transconductance Amplifier (OTA) is utilized to amplify the neural signals. The optimization of the number of amplifier stages is discussed to achieve the minimum power and area consumption. The amplifier power supply is 0.7V. The midband gain of amplifier is 58.4dB with a 3dB bandwidth from 0.71 to 8.26 kHz. Measured input-referred noise and total power consumption are 20.7 μVrms and 1.90 μW respectively. The measured result shows that the optimizing the number of stages can achieve lower power consumption and demonstrates the neural amplifier's suitability for instu neutral activity recording. The advantage of power consumption of Pipelined ADC over Successive Approximation Register (SAR) ADC and Delta-Sigma ADC is discussed. An 8 bit fully differential (FD) Pipeline ADC for use in a smart RFID is presented in this dissertation. The Multiplying Digital to Analog Converter (MDAC) utilizes a novel offset cancellation technique robust to device leakage to reduce the input drift voltage. Simulation results of static and dynamic performance show this low power Pipeline ADC is suitable for multi-channel neural recording applications. The performance of all proposed building blocks is verified through test chips fabricated in IBM 180nm CMOS process. Both bench-top and real animal test results demonstrate the system's capability of recording neural signals for neural spike detection
Analog VLSI Circuits for Biosensors, Neural Signal Processing and Prosthetics
Stroke, spinal cord injury and neurodegenerative diseases such as ALS and Parkinson's debilitate their victims by suffocating, cleaving communication between, and/or poisoning entire populations of geographically correlated neurons. Although the damage associated with such injury or disease is typically irreversible, recent advances in implantable neural prosthetic devices offer hope for the restoration of lost sensory, cognitive and motor functions by remapping those functions onto healthy cortical regions. The research presented in this thesis is directed toward developing enabling technology for totally implantable neural prosthetics that could one day restore lost sensory, cognitive and motor function to the victims of debilitating neural injury or disease.
There are three principal components to this work. First, novel integrated biosensors have been designed and implemented to transduce weak extra-cellular electrical potentials and optical signals from cells cultured directly on the surface of the sensor chips, as well as to manipulate cells on the surface of these chips. Second, a method of detecting and identifying stereotyped neural signals, or action potentials, has been mapped into silicon circuits which operate at very low power levels suitable for implantation. Third, as one small step towards the development of cognitive neural implants, a learning silicon synapse has been implemented and a neural network application demonstrated.
The original contributions of this dissertation include:
* A contact image sensor that adapts to background light intensity and can asynchronously detect statistically significant optical events in real-time;
* Programmable electrode arrays for enhanced electrophysiological recording, for directing cellular growth, for site-specific in situ bio-functionalization, and for analyte and particulate collection;
* Ultra-low power, programmable floating gate template matching circuits for the detection and classification of neural action potentials;
* A two transistor synapse that exhibits spike timing dependent plasticity and can implement adaptive pattern classification and silicon learning
A Closed-Loop Bidirectional Brain-Machine Interface System For Freely Behaving Animals
A brain-machine interface (BMI) creates an artificial pathway between the brain and the external world. The research and applications of BMI have received enormous attention among the scientific community as well as the public in the past decade. However, most research of BMI relies on experiments with tethered or sedated animals, using rack-mount equipment, which significantly restricts the experimental methods and paradigms. Moreover, most research to date has focused on neural signal recording or decoding in an open-loop method. Although the use of a closed-loop, wireless BMI is critical to the success of an extensive range of neuroscience research, it is an approach yet to be widely used, with the electronics design being one of the major bottlenecks. The key goal of this research is to address the design challenges of a closed-loop, bidirectional BMI by providing innovative solutions from the neuron-electronics interface up to the system level.
Circuit design innovations have been proposed in the neural recording front-end, the neural feature extraction module, and the neural stimulator. Practical design issues of the bidirectional neural interface, the closed-loop controller and the overall system integration have been carefully studied and discussed.To the best of our knowledge, this work presents the first reported portable system to provide all required hardware for a closed-loop sensorimotor neural interface, the first wireless sensory encoding experiment conducted in freely swimming animals, and the first bidirectional study of the hippocampal field potentials in freely behaving animals from sedation to sleep.
This thesis gives a comprehensive survey of bidirectional BMI designs, reviews the key design trade-offs in neural recorders and stimulators, and summarizes neural features and mechanisms for a successful closed-loop operation. The circuit and system design details are presented with bench testing and animal experimental results. The methods, circuit techniques, system topology, and experimental paradigms proposed in this work can be used in a wide range of relevant neurophysiology research and neuroprosthetic development, especially in experiments using freely behaving animals
Development and modelling of a versatile active micro-electrode array for high density in-vivo and in-vitro neural signal investigation
The electrophysiological observation of neurological cells has allowed much knowledge to be gathered regarding how living organisms are believed to acquire and process sensation. Although much has been learned about neurons in isolation, there is much more to be discovered in how these neurons communicate within large networks. The challenges of measuring neurological networks at the scale, density and chronic level of non invasiveness required to observe neurological processing and decision making are manifold, however methods have been suggested that have allowed small scale networks to be observed using arrays of micro-fabricated electrodes. These arrays transduce ionic perturbations local to the cell membrane in the extracellular fluid into small electrical signals within the metal that may be measured.
A device was designed for optimal electrical matching to the electrode interface and maximal signal preservation of the received extracellular neural signals. Design parameters were developed from electrophysiological computer simulations and experimentally obtained empirical models of the electrode-electrolyte interface. From this information, a novel interface based signal filtering method was developed that enabled high density amplifier interface circuitry to be realised.
A novel prototype monolithic active electrode was developed using CMOS microfabrication technology. The device uses the top metallization of a selected process to form the electrode substrate and compact amplification circuitry fabricated directly beneath the electrode to amplify and separate the neural signal from the baseline offsets and noise of the electrode interface. The signal is then buffered for high speed sampling and switched signal routing. Prototype 16 and 256 active electrode array with custom support circuitry is presented at the layout stage for a 20 μm diameter 100 μm pitch electrode array. Each device consumes 26.4 μW of power and contributes 4.509 μV (rms) of noise to the received signal over a controlled bandwidth of 10 Hz - 5 kHz.
The research has provided a fundamental insight into the challenges of high density neural network observation, both in the passive and the active manner. The thesis concludes that power consumption is the fundamental limiting factor of high density integrated MEA circuitry; low power dissipation being crucial for the existence of the surface adhered cells under measurement. With transistor sizing, noise and signal slewing each being inversely proportional to the dc supply current and the large power requirements of desirable ancillary circuitry such as analogue-to-digital converters, a situation of compromise is approached that must be carefully considered for specific application design
Applications of memristors in conventional analogue electronics
This dissertation presents the steps employed to activate and utilise analogue memristive devices in conventional analogue circuits and beyond.
TiO2 memristors are mainly utilised in this study, and their large variability in operation in between similar devices is identified.
A specialised memristor characterisation instrument is designed and built to mitigate this issue and to allow access to large numbers of devices at a time.
Its performance is quantified against linear resistors, crossbars of linear resistors, stand-alone memristive elements and crossbars of memristors.
This platform allows for a wide range of different pulsing algorithms to be applied on individual devices, or on crossbars of memristive elements, and is used throughout this dissertation.
Different ways of achieving analogue resistive switching from any device state are presented.
Results of these are used to devise a state-of-art biasing parameter finder which automatically extracts pulsing parameters that induce repeatable analogue resistive switching.
IV measurements taken during analogue resistive switching are then utilised to model the internal atomic structure of two devices, via fittings by the Simmons tunnelling barrier model.
These reveal that voltage pulses modulate a nano-tunnelling gap along a conical shape.
Further retention measurements are performed which reveal that under certain conditions, TiO2 memristors become volatile at short time scales.
This volatile behaviour is then implemented into a novel SPICE volatile memristor model.
These characterisation methods of solid-state devices allowed for inclusion of TiO2 memristors in practical electronic circuits.
Firstly, in the context of large analogue resistive crossbars, a crosspoint reading method is analysed and improved via a 3-step technique.
Its scaling performance is then quantified via SPICE simulations.
Next, the observed volatile dynamics of memristors are exploited in two separate sequence detectors, with applications in neuromorphic engineering.
Finally, the memristor as a programmable resistive weight is exploited to synthesise a memristive programmable gain amplifier and a practical memristive automatic gain control circuit.Open Acces
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Scalable Bundle Design for Massively Parallel Neuronal Recordings <i>In Vivo</i>
Neural coding consists of precise interactions between related neurons. New techniques are needed to measure the time sensitive interactions within entire neural networks to understand how the brain functions. Extracellular recording is the oldest method of measuring neural activity and can sample at a temporal resolution to resolve fast spiking neurons. If scaled to a sufficiently large number of simultaneous recorded neurons, this technique would be an excellent candidate for such large scale recording. I propose the combined use of glass ensheathed microwire bundle electrodes and an infrared camera readout integrated circuit to collect massively parallel neuronal recordings in vivo. This design will allow for the recording of high quality signals because of the non-intrusive dimensions, low stray capacitance, and enhanced surface impedance of the electrodes, as well as the high signal to noise amplification of the camera electronics.
Here the construction of a system to record neural activity is described and its electrical properties are characterized. The results demonstrate the ability to successfully connect fabricated bundle electrodes to the indium bumps of the readout integrated circuit chip and to record voltage waveforms with a signal to noise ratio to resolve simulated spikes. In vivo experiments in the olfactory bulb of anaesthetized mice have resulted in recordings of action potentials from single units. The spike rate of these units increases with odor presentation and is pharmacologically inhibited which demonstrates the biological origin of the recorded activity. To further advance this technology, the stability and rate of connection to the readout electronics need to improve and insertion of electrode bundles with hundreds of more recording sites needs to be optimized. This promising design has several distinct advantages over existing fluorescence imaging and extracellular recording neurotechnologies for large scale neuronal recording
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