568 research outputs found
Compact low-power calibration mini-DACs for neural arrays with programmable weights
This paper considers the viability of compact low-resolution low-power mini digital-to-analog converters (mini-DACs) for use in large arrays of neural type cells, where programmable weights are required. Transistors are biased in weak inversion in order to yield small currents and low power consumptions, a necessity when building large size arrays. One important drawback of weak inversion operation is poor matching between transistors. The resulting effective precision of a fabricated array of 50 DACs turned out to be 47% (1.1 bits), due to transistor mismatch. However, it is possible to combine them two by two in order to build calibrated DACs, thus compensating for inter-DAC mismatch. It is shown experimentally that the precision can be improved easily by a factor of 10 (4.8% or 4.4 bits), which makes these DACs viable for low-resolution applications such as massive arrays of neural processing circuits. A design methodology is provided, and illustrated through examples, to obtain calibrated mini-DACs of a given target precision. As an example application, we show simulation results of using this technique to calibrate an array of digitally controlled integrate-and-fire neurons.Gobierno de España TIC1999-0446-C02-02, TIC2000-0406-P4-05, FIT-07000/2002/921, TIC2002-10878-EEuropean Union IST- 2001-3412
Dataset for neutron and gamma-ray pulse shape discrimination
The publicly accessible dataset includes neutron and gamma-ray pulse signals
for conducting pulse shape discrimination experiments. Several traditional and
recently proposed pulse shape discrimination algorithms are utilized to
evaluate the performance of pulse shape discrimination under raw pulse signals
and noise-enhanced datasets. These algorithms comprise zero-crossing (ZC),
charge comparison (CC), falling edge percentage slope (FEPS), frequency
gradient analysis (FGA), pulse-coupled neural network (PCNN), ladder gradient
(LG), and het-erogeneous quasi-continuous spiking cortical model (HQC-SCM). In
addition to the pulse signals, this dataset includes the source code for all
the aforementioned pulse shape discrimination methods. Moreover, the dataset
provides the source code for schematic pulse shape discrimination performance
evaluation and anti-noise performance evaluation. This feature enables
researchers to evaluate the performance of these methods using standard
procedures and assess their anti-noise ability under various noise conditions.
In conclusion, this dataset offers a comprehensive set of resources for
conducting pulse shape discrimination experiments and evaluating the
performance of various pulse shape discrimination methods under different noise
scenarios.Comment: 11 pages,10 figure
On Real-Time AER 2-D Convolutions Hardware for Neuromorphic Spike-Based Cortical Processing
In this paper, a chip that performs real-time image
convolutions with programmable kernels of arbitrary shape is presented.
The chip is a first experimental prototype of reduced size
to validate the implemented circuits and system level techniques.
The convolution processing is based on the address–event-representation
(AER) technique, which is a spike-based biologically
inspired image and video representation technique that favors
communication bandwidth for pixels with more information. As
a first test prototype, a pixel array of 16x16 has been implemented
with programmable kernel size of up to 16x16. The
chip has been fabricated in a standard 0.35- m complimentary
metal–oxide–semiconductor (CMOS) process. The technique also
allows to process larger size images by assembling 2-D arrays of
such chips. Pixel operation exploits low-power mixed analog–digital
circuit techniques. Because of the low currents involved (down
to nanoamperes or even picoamperes), an important amount of
pixel area is devoted to mismatch calibration. The rest of the
chip uses digital circuit techniques, both synchronous and asynchronous.
The fabricated chip has been thoroughly tested, both at
the pixel level and at the system level. Specific computer interfaces
have been developed for generating AER streams from conventional
computers and feeding them as inputs to the convolution
chip, and for grabbing AER streams coming out of the convolution
chip and storing and analyzing them on computers. Extensive
experimental results are provided. At the end of this paper, we
provide discussions and results on scaling up the approach for
larger pixel arrays and multilayer cortical AER systems.Commission of the European Communities IST-2001-34124 (CAVIAR)Commission of the European Communities 216777 (NABAB)Ministerio de EducaciĂłn y Ciencia TIC-2000-0406-P4Ministerio de EducaciĂłn y Ciencia TIC-2003-08164-C03-01Ministerio de EducaciĂłn y Ciencia TEC2006-11730-C03-01Junta de AndalucĂa TIC-141
Neuromorphic silicon neuron circuits
23 páginas, 21 figuras, 2 tablas.-- et al.Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.This work was supported by the EU ERC grant 257219 (neuroP), the EU ICT FP7 grants 231467 (eMorph), 216777 (NABAB), 231168 (SCANDLE), 15879 (FACETS), by the Swiss National Science Foundation grant 119973 (SoundRec), by the UK EPSRC grant no. EP/C010841/1, by the Spanish grants (with support from the European Regional Development Fund) TEC2006-11730-C03-01 (SAMANTA2), TEC2009-10639-C04-01 (VULCANO) Andalusian grant num. P06TIC01417 (Brain System), and by the Australian Research Council grants num. DP0343654 and num. DP0881219.Peer Reviewe
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TGFβ signaling is associated with changes in inflammatory gene expression and perineuronal net degradation around inhibitory neurons following various neurological insults.
Brain damage due to stroke or traumatic brain injury (TBI), both leading causes of serious long-term disability, often leads to the development of epilepsy. Patients who develop post-injury epilepsy tend to have poor functional outcomes. Emerging evidence highlights a potential role for blood-brain barrier (BBB) dysfunction in the development of post-injury epilepsy. However, common mechanisms underlying the pathological hyperexcitability are largely unknown. Here, we show that comparative transcriptome analyses predict remodeling of extracellular matrix (ECM) as a common response to different types of injuries. ECM-related transcriptional changes were induced by the serum protein albumin via TGFβ signaling in primary astrocytes. In accordance with transcriptional responses, we found persistent degradation of protective ECM structures called perineuronal nets (PNNs) around fast-spiking inhibitory interneurons, in a rat model of TBI as well as in brains of human epileptic patients. Exposure of a naïve brain to albumin was sufficient to induce the transcriptional and translational upregulation of molecules related to ECM remodeling and the persistent breakdown of PNNs around fast-spiking inhibitory interneurons, which was contingent on TGFβ signaling activation. Our findings provide insights on how albumin extravasation that occurs upon BBB dysfunction in various brain injuries can predispose neural circuitry to the development of chronic inhibition deficits
Pulse shape discrimination based on the Tempotron: a powerful classifier on GPU
This study introduces the Tempotron, a powerful classifier based on a
third-generation neural network model, for pulse shape discrimination. By
eliminating the need for manual feature extraction, the Tempotron model can
process pulse signals directly, generating discrimination results based on
learned prior knowledge. The study performed experiments using GPU
acceleration, resulting in over a 500 times speedup compared to the CPU-based
model, and investigated the impact of noise augmentation on the Tempotron's
performance. Experimental results showed that the Tempotron is a potent
classifier capable of achieving high discrimination accuracy. Furthermore,
analyzing the neural activity of Tempotron during training shed light on its
learning characteristics and aided in selecting the Tempotron's
hyperparameters. The dataset used in this study and the source code of the
GPU-based Tempotron are publicly available on GitHub at
https://github.com/HaoranLiu507/TempotronGPU.Comment: 14 pages,7 figure
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