Project description:Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems. The brain has evolved over billions of years to solve difficult engineering problems by using efficient, parallel, low-power computation. The goal of NE is to design systems capable of brain-like computation. Numerous large-scale neuromorphic projects have emerged recently. This interdisciplinary field was listed among the top 10 technology breakthroughs of 2014 by the MIT Technology Review and among the top 10 emerging technologies of 2015 by the World Economic Forum. NE has two-way goals: one, a scientific goal to understand the computational properties of biological neural systems by using models implemented in integrated circuits (ICs); second, an engineering goal to exploit the known properties of biological systems to design and implement efficient devices for engineering applications. Building hardware neural emulators can be extremely useful for simulating large-scale neural models to explain how intelligent behavior arises in the brain. The principal advantages of neuromorphic emulators are that they are highly energy efficient, parallel and distributed, and require a small silicon area. Thus, compared to conventional CPUs, these neuromorphic emulators are beneficial in many engineering applications such as for the porting of deep learning algorithms for various recognitions tasks. In this review article, we describe some of the most significant neuromorphic spiking emulators, compare the different architectures and approaches used by them, illustrate their advantages and drawbacks, and highlight the capabilities that each can deliver to neural modelers. This article focuses on the discussion of large-scale emulators and is a continuation of a previous review of various neural and synapse circuits (Indiveri et al., 2011). We also explore applications where these emulators have been used and discuss some of their promising future applications.
Project description:PurposeSimultaneous acquisition of MR and positron emission tomography (PET) images requires the placement of the MR detection coil inside the PET detector ring where it absorbs and scatters photons. This constraint is the principal barrier to achieving optimum sensitivity on each modality. Here, we present a 31-channel PET-compatible brain array coil with reduced attenuation but improved MR sensitivity.MethodsA series of component tests were performed to identify tradeoffs between PET and MR performance. Aspects studied include the remote positioning of preamplifiers, coax size, coil trace size/material, and plastic housing. We then maximized PET performance at minimal cost to MR sensitivity. The coil was evaluated for MR performance (signal to noise ratio [SNR], g-factor) and PET attenuation.ResultsThe coil design showed an improvement in attenuation by 190% (average) compared with conventional 32-channel arrays, and no loss in MR SNR. Moreover, the 31-channel coil displayed an SNR improvement of 230% (cortical region of interest) compared with a PET-optimized 8-channel array with similar attenuation properties. Implementing attenuation correction of the 31-channel array successfully removed PET artifacts, which were comparable to those of the 8-channel array.ConclusionThe design of the 31-channel PET-compatible coil enables higher sensitivity for PET/MR imaging, paving the way for novel applications in this hybrid-imaging domain.
Project description:A highly stretchable neural interface of concurrent robust electrical and mechanical properties is developed with a conducting polymer film as the sole conductor for both the electrodes and the leads. This neural interface offers the benefits of conducting polymer electrodes in a demanding stretchable format, including low electrode impedance and high chargeinjection capacity, due to the large electroactive surface area of the electrode.
Project description:High-precision monitoring of electrophysiological signals with high spatial and temporal resolutions is one of the most important subjects for elucidating physiology functions. Recently, ultraflexible multielectrode arrays (MEAs) have been fabricated to establish conformal contacts with the surface of organs and to measure propagation of electrophysiological signals with high spatial-temporal resolution; however, plastic substrates have high Young's modulus, causing difficulties in creating appropriate stretchability and blood compatibility for applying them on the dynamically moving and surgical bleeding surface of the heart. Here, we have successfully fabricated an active MEA that simultaneously achieves nonthrombogenicity, stretchability, and stability, which allows long-term electrocardiographic (ECG) monitoring of the dynamically moving hearts of rats even with capillary bleeding. Because of the active data readout, the measured ECG signals exhibit a high signal-to-noise ratio of 52 dB. The novel stretchable MEA is carefully designed using state-of-the-art engineering techniques by combining extraordinarily high gain organic electrochemical transistors processed on microgrid substrates and a coating of poly(3-methoxypropyl acrylate), which exhibits significant antithrombotic properties while maintaining excellent ionic conductivity.
Project description:Mechanically flexible active multielectrode arrays (MEA) have been developed for local signal amplification and high spatial resolution. However, their opaqueness limited optical observation and light stimulation during use. Here, we show a transparent, ultraflexible, and active MEA, which consists of transparent organic electrochemical transistors (OECTs) and transparent Au grid wirings. The transparent OECT is made of Au grid electrodes and has shown comparable performance with OECTs with nontransparent electrodes/wirings. The transparent active MEA realizes the spatial mapping of electrocorticogram electrical signals from an optogenetic rat with 1-mm spacing and shows lower light artifacts than noise level. Our active MEA would open up the possibility of precise investigation of a neural network system with direct light stimulation.
Project description:Pose estimation is a fundamental step in many artificial vision tasks. It consists of estimating the 3D pose of an object with respect to a camera from the object's 2D projection. Current state of the art implementations operate on images. These implementations are computationally expensive, especially for real-time applications. Scenes with fast dynamics exceeding 30-60 Hz can rarely be processed in real-time using conventional hardware. This paper presents a new method for event-based 3D object pose estimation, making full use of the high temporal resolution (1 μs) of asynchronous visual events output from a single neuromorphic camera. Given an initial estimate of the pose, each incoming event is used to update the pose by combining both 3D and 2D criteria. We show that the asynchronous high temporal resolution of the neuromorphic camera allows us to solve the problem in an incremental manner, achieving real-time performance at an update rate of several hundreds kHz on a conventional laptop. We show that the high temporal resolution of neuromorphic cameras is a key feature for performing accurate pose estimation. Experiments are provided showing the performance of the algorithm on real data, including fast moving objects, occlusions, and cases where the neuromorphic camera and the object are both in motion.
Project description:Mixed-signal analog/digital circuits emulate spiking neurons and synapses with extremely high energy efficiency, an approach known as "neuromorphic engineering". However, analog circuits are sensitive to process-induced variation among transistors in a chip ("device mismatch"). For neuromorphic implementation of Spiking Neural Networks (SNNs), mismatch causes parameter variation between identically-configured neurons and synapses. Each chip exhibits a different distribution of neural parameters, causing deployed networks to respond differently between chips. Current solutions to mitigate mismatch based on per-chip calibration or on-chip learning entail increased design complexity, area and cost, making deployment of neuromorphic devices expensive and difficult. Here we present a supervised learning approach that produces SNNs with high robustness to mismatch and other common sources of noise. Our method trains SNNs to perform temporal classification tasks by mimicking a pre-trained dynamical system, using a local learning rule from non-linear control theory. We demonstrate our method on two tasks requiring temporal memory, and measure the robustness of our approach to several forms of noise and mismatch. We show that our approach is more robust than common alternatives for training SNNs. Our method provides robust deployment of pre-trained networks on mixed-signal neuromorphic hardware, without requiring per-device training or calibration.
Project description:Because of its large surface area and easy access for both delivery and monitoring, the skin is an attractive target for gene therapy for cutaneous diseases, vaccinations and several metabolic disorders. The critical factors for DNA delivery to the skin by electroporation (EP) are effective expression levels and minimal or no tissue damage. Here, we evaluated the non-invasive multielectrode array (MEA) for gene electrotransfer. For these studies we utilized a guinea pig model, which has been shown to have a similar thickness and structure to human skin. Our results demonstrate significantly increased gene expression 2 to 3 logs above injection of plasmid DNA alone over 15 days. Furthermore, gene expression could be enhanced by increasing the size of the treatment area. Transgene-expressing cells were observed exclusively in the epidermal layer of the skin. In contrast to caliper or plate electrodes, skin EP with the MEA greatly reduced muscle twitching and resulted in minimal and completely recoverable skin damage. These results suggest that EP with MEA can be an efficient and non-invasive skin delivery method with less adverse side effects than other EP delivery systems and promising clinical applications.
Project description:Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events. This principle results in significant advantages over conventional cameras, such as low power utilization, high dynamic range, and no motion blur. Moreover, by design, such cameras encode only the relative motion between the scene and the sensor and not the static background to yield a very sparse data structure. In this paper, we leverage these advantages of an event camera toward a critical vision application-video anomaly detection. We propose an anomaly detection solution in the event domain with a conditional Generative Adversarial Network (cGAN) made up of sparse submanifold convolution layers. Video analytics tasks such as anomaly detection depend on the motion history at each pixel. To enable this, we also put forward a generic unsupervised deep learning solution to learn a novel memory surface known as Deep Learning (DL) memory surface. DL memory surface encodes the temporal information readily available from these sensors while retaining the sparsity of event data. Since there is no existing dataset for anomaly detection in the event domain, we also provide an anomaly detection event dataset with a set of anomalies. We empirically validate our anomaly detection architecture, composed of sparse convolutional layers, on this proposed and online dataset. Careful analysis of the anomaly detection network reveals that the presented method results in a massive reduction in computational complexity with good performance compared to previous state-of-the-art conventional frame-based anomaly detection networks.
Project description:Translational comparison of rodent models of neurological and neuropsychiatric diseases to human electroencephalography (EEG) biomarkers in these conditions will require multisite rodent EEG on the skull surface, rather than local area electrocorticography (ECoG) or multisite local field potential (LFP) recording. We have developed a technique for planar multielectrode array (MEA) implantation on the mouse skull surface, which enables multisite EEG in awake and freely moving mice and reusability of the MEA probes. With this method, we reliably obtain 30-channel low-noise EEG from awake mice. Baseline and stimulus-evoked EEG recordings can be readily obtained and analyzed. For example, we have demonstrated EEG responses to auditory stimuli. Broadband noise elicits reliable 30-channel auditory event-related potentials (ERPs), and chirp stimuli induce phase-locked EEG responses just as in human sound presentation paradigms. This method is unique in achieving chronic implantation of novel MEA technology onto the mouse skull surface for chronic multisite EEG recordings. Furthermore, we demonstrate a reliable method for reusing MEA probes for multiple serial implantations without loss of EEG quality. This skull surface MEA methodology can be used to obtain simultaneous multisite EEG recordings and to test EEG biomarkers in diverse mouse models of human neurological and neuropsychiatric diseases. Reusability of the MEA probes makes it more cost-effective to deploy this system for various studies.