3,327 research outputs found
Development and Validation of a Spike Detection and Classification Algorithm Aimed at Implementation on Hardware Devices
Neurons cultured in vitro on MicroElectrode Array (MEA) devices connect to each other, forming a network. To study electrophysiological activity and long term plasticity effects, long period recording and spike sorter methods are needed. Therefore, on-line and real time analysis, optimization of memory use and data transmission rate improvement become necessary. We developed an algorithm for amplitude-threshold spikes detection, whose performances were verified with (a) statistical analysis on both simulated and real signal and (b) Big O Notation. Moreover, we developed a PCA-hierarchical classifier, evaluated on simulated and real signal. Finally we proposed a spike detection hardware design on FPGA, whose feasibility was verified in terms of CLBs number, memory occupation and temporal requirements; once realized, it will be able to execute on-line detection and real time waveform analysis, reducing data storage problems
NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking
The field of neuromorphic computing holds great promise in terms of advancing
computing efficiency and capabilities by following brain-inspired principles.
However, the rich diversity of techniques employed in neuromorphic research has
resulted in a lack of clear standards for benchmarking, hindering effective
evaluation of the advantages and strengths of neuromorphic methods compared to
traditional deep-learning-based methods. This paper presents a collaborative
effort, bringing together members from academia and the industry, to define
benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are
to be a collaborative, fair, and representative benchmark suite developed by
the community, for the community. In this paper, we discuss the challenges
associated with benchmarking neuromorphic solutions, and outline the key
features of NeuroBench. We believe that NeuroBench will be a significant step
towards defining standards that can unify the goals of neuromorphic computing
and drive its technological progress. Please visit neurobench.ai for the latest
updates on the benchmark tasks and metrics
NeuroBench:Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking
The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics
Resource efficient on-node spike sorting
Current implantable brain-machine interfaces are recording multi-neuron activity by utilising multi-channel, multi-electrode micro-electrodes. With the rapid increase in recording capability has come more stringent constraints on implantable system power consumption and size. This is even more so with the increasing demand for wireless systems to increase the number of channels being monitored whilst overcoming the communication bottleneck (in transmitting raw data) via transcutaneous bio-telemetries. For systems observing unit activity, real-time spike sorting within an implantable device offers a unique solution to this problem.
However, achieving such data compression prior to transmission via an on-node spike sorting system has several challenges. The inherent complexity of the spike sorting problem arising from various factors (such as signal variability, local field potentials, background and multi-unit activity) have required computationally intensive algorithms (e.g. PCA, wavelet transform, superparamagnetic clustering). Hence spike sorting systems have traditionally been implemented off-line, usually run on work-stations. Owing to their complexity and not-so-well scalability, these algorithms cannot be simply transformed into a resource efficient hardware. On the contrary, although there have been several attempts in implantable hardware, an implementation to match comparable accuracy to off-line within the required power and area requirements for future BMIs have yet to be proposed.
Within this context, this research aims to fill in the gaps in the design towards a resource efficient implantable real-time spike sorter which achieves performance comparable to off-line methods. The research covered in this thesis target: 1) Identifying and quantifying the trade-offs on subsequent signal processing performance and hardware resource utilisation of the parameters associated with analogue-front-end. Following the development of a behavioural model of the analogue-front-end and an optimisation tool, the sensitivity of the spike sorting accuracy to different front-end parameters are quantified. 2) Identifying and quantifying the trade-offs associated with a two-stage hybrid solution to realising real-time on-node spike sorting. Initial part of the work focuses from the perspective of template matching only, while the second part of the work considers these parameters from the point of whole system including detection, sorting, and off-line training (template building). A set of minimum requirements are established which ensure robust, accurate and resource efficient operation. 3) Developing new feature extraction and spike sorting algorithms towards highly scalable systems. Based on waveform dynamics of the observed action potentials, a derivative based feature extraction and a spike sorting algorithm are proposed. These are compared with most commonly used methods of spike sorting under varying noise levels using realistic datasets to confirm their merits. The latter is implemented and demonstrated in real-time through an MCU based platform.Open Acces
Closed-loop approaches for innovative neuroprostheses
The goal of this thesis is to study new ways to interact with the nervous system in case of damage or pathology. In particular, I focused my effort towards the development of innovative, closed-loop stimulation protocols in various scenarios: in vitro, ex vivo, in vivo
Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor
An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein integrated with machine learning features was developed. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed to SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles, the analytical protocol involving a single-step sample incubation. Immunosensor performance was assessed by validation carried out in viral transfer medium, which is commonly used for de-sorption of nasopharyngeal swabs. Remarkable specificity of the response was demonstrated by testing H1N1 Hemagglutinin from swine-origin influenza A virus and Spike Protein S1 from Middle East respiratory syndrome coronavirus. Machine learning was successfully used for data processing and analysis: different support vector machine classifiers were evaluated proving that algorithms affect the classifier accuracy. The test accuracy of the best classification model in terms of true positive/true negative sample classification was 97.3%. In addition, ML algorithm can be easily integrated into the developed cloud-based portable Wi-Fi device. Finally, the immunosensor was successfully tested using a third generation replicating incompetent lentiviral vector pseudotyped with SARS-CoV-2 spike glycoprotein, thus proving the applicability of the immunosensor to whole virus detection
Exploiting All-Programmable System on Chips for Closed-Loop Real-Time Neural Interfaces
High-density microelectrode arrays (HDMEAs) feature thousands of recording electrodes
in a single chip with an area of few square millimeters. The obtained electrode density is
comparable and even higher than the typical density of neuronal cells in cortical cultures.
Commercially available HDMEA-based acquisition systems are able to record the neural
activity from the whole array at the same time with submillisecond resolution. These devices
are a very promising tool and are increasingly used in neuroscience to tackle fundamental
questions regarding the complex dynamics of neural networks. Even if electrical or optical
stimulation is generally an available feature of such systems, they lack the capability of
creating a closed-loop between the biological neural activity and the artificial system. Stimuli
are usually sent in an open-loop manner, thus violating the inherent working basis of neural
circuits that in nature are constantly reacting to the external environment. This forbids to
unravel the real mechanisms behind the behavior of neural networks.
The primary objective of this PhD work is to overcome such limitation by creating a fullyreconfigurable
processing system capable of providing real-time feedback to the ongoing
neural activity recorded with HDMEA platforms. The potentiality of modern heterogeneous
FPGAs has been exploited to realize the system. In particular, the Xilinx Zynq All Programmable
System on Chip (APSoC) has been used. The device features reconfigurable
logic, specialized hardwired blocks, and a dual-core ARM-based processor; the synergy of
these components allows to achieve high elaboration performances while maintaining a high
level of flexibility and adaptivity. The developed system has been embedded in an acquisition
and stimulation setup featuring the following platforms:
\u2022 3\ub7Brain BioCam X, a state-of-the-art HDMEA-based acquisition platform capable of
recording in parallel from 4096 electrodes at 18 kHz per electrode.
\u2022 PlexStim\u2122 Electrical Stimulator System, able to generate electrical stimuli with
custom waveforms to 16 different output channels.
\u2022 Texas Instruments DLP\uae LightCrafter\u2122 Evaluation Module, capable of projecting
608x684 pixels images with a refresh rate of 60 Hz; it holds the function of optical
stimulation.
All the features of the system, such as band-pass filtering and spike detection of all the
recorded channels, have been validated by means of ex vivo experiments. Very low-latency
has been achieved while processing the whole input data stream in real-time. In the case
of electrical stimulation the total latency is below 2 ms; when optical stimuli are needed,
instead, the total latency is a little higher, being 21 ms in the worst case.
The final setup is ready to be used to infer cellular properties by means of closed-loop
experiments. As a proof of this concept, it has been successfully used for the clustering
and classification of retinal ganglion cells (RGCs) in mice retina. For this experiment, the
light-evoked spikes from thousands of RGCs have been correctly recorded and analyzed in
real-time. Around 90% of the total clusters have been classified as ON- or OFF-type cells.
In addition to the closed-loop system, a denoising prototype has been developed. The main
idea is to exploit oversampling techniques to reduce the thermal noise recorded by HDMEAbased
acquisition systems. The prototype is capable of processing in real-time all the input
signals from the BioCam X, and it is currently being tested to evaluate the performance in
terms of signal-to-noise-ratio improvement
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic
processors, new opportunities are emerging for applying deep and Spiking Neural
Network (SNN) algorithms to healthcare and biomedical applications at the edge.
This can facilitate the advancement of the medical Internet of Things (IoT)
systems and Point of Care (PoC) devices. In this paper, we provide a tutorial
describing how various technologies ranging from emerging memristive devices,
to established Field Programmable Gate Arrays (FPGAs), and mature Complementary
Metal Oxide Semiconductor (CMOS) technology can be used to develop efficient DL
accelerators to solve a wide variety of diagnostic, pattern recognition, and
signal processing problems in healthcare. Furthermore, we explore how spiking
neuromorphic processors can complement their DL counterparts for processing
biomedical signals. After providing the required background, we unify the
sparsely distributed research on neural network and neuromorphic hardware
implementations as applied to the healthcare domain. In addition, we benchmark
various hardware platforms by performing a biomedical electromyography (EMG)
signal processing task and drawing comparisons among them in terms of inference
delay and energy. Finally, we provide our analysis of the field and share a
perspective on the advantages, disadvantages, challenges, and opportunities
that different accelerators and neuromorphic processors introduce to healthcare
and biomedical domains. This paper can serve a large audience, ranging from
nanoelectronics researchers, to biomedical and healthcare practitioners in
grasping the fundamental interplay between hardware, algorithms, and clinical
adoption of these tools, as we shed light on the future of deep networks and
spiking neuromorphic processing systems as proponents for driving biomedical
circuits and systems forward.Comment: Submitted to IEEE Transactions on Biomedical Circuits and Systems (21
pages, 10 figures, 5 tables
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