For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance. Similarly, engineered systems are optimized for short time-to-solution ...and low energy-to-solution characteristics. At the level of neuronal implementation, this implies achieving the desired results with as few and as early spikes as possible. With time-to-first-spike coding, both of these goals are inherently emerging features of learning. Here, we describe a rigorous derivation of a learning rule for such first-spike times in networks of leaky integrate-and-fire neurons, relying solely on input and output spike times, and show how this mechanism can implement error backpropagation in hierarchical spiking networks. Furthermore, we emulate our framework on the BrainScaleS-2 neuromorphic system and demonstrate its capability of harnessing the system’s speed and energy characteristics. Finally, we examine how our approach generalizes to other neuromorphic platforms by studying how its performance is affected by typical distortive effects induced by neuromorphic substrates.Spiking neural networks promise fast and energy-efficient information processing. The ‘time-to-first-spike’ coding scheme, where the time elapsed before a neuron’s first spike is utilized as the main variable, is a particularly efficient approach and Göltz and Kriener et al. demonstrate that error backpropagation, an essential ingredient for learning in neural networks, can be implemented in this scheme.
Wafer-scale integration of analog neural networks Schemmel, J.; Fieres, J.; Meier, K.
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence),
06/2008, Letnik:
10
Conference Proceeding, Journal Article
Odprti dostop
This paper introduces a novel design of an artificial neural network tailored for wafer-scale integration. The presented VLSI implementation includes continuous-time analog neurons with up to 16 k ...inputs. A novel interconnection and routing scheme allows the mapping of a multitude of network models derived from biology on the VLSI neural network while maintaining a high resource usage. A single 20 cm wafer contains about 60 million synapses. The implemented neurons are highly accelerated compared to biological real time. The power consumption of the dense interconnection network providing the necessary communication bandwidth is a critical aspect of the system integration. A novel asynchronous low-voltage signaling scheme is presented that makes the wafer-scale approach feasible by limiting the total power consumption while simultaneously providing a flexible, programmable network topology.
We present a normal incidence terahertz reflectivity technique to determine the optical thickness and birefringence of yttria-stabilized zirconia (YSZ) thermal barrier coatings (TBCs). Initial ...verification of the method was achieved by measurement of a set of fused silica calibration samples with known thicknesses and showed excellent agreement (<1% of refractive index) with the literature. The THz-measured optical thickness and its variation through the depth profile of the YSZ coating are shown to be in good agreement (<4%) with scanning electron microscope cross-sectional thickness measurements. In addition, the position of discontinuities in both the optical thickness and birefringence appear to be correlated to coating failure points observed during accelerated aging trials.
A high-dynamic-range CMOS image sensor consisting of nonintegrating, continuously working photoreceptors with logarithmic response is presented. The nonuniformity problem caused by the ...device-to-device variations is greatly reduced by an implemented analog self-calibration. After performing this calibration, the remaining fixed pattern noise amounts to 3.8% (RMS) of an intensity decade at a uniform illumination of 1 W/m/sup 2/. The sensor provides a resolution of 384/spl times/288 pixels and a dynamic range of 6 decades in the intensity region from 3 mW/m/sup 2/ to 3 kW/m/sup 2/. It contains all components required for operating as a camera-on-a-chip. The image data can be read out either via a single analog line (video standard) or via a digital interface after undergoing an analog-to-digital conversion on the chip. Additional features like automatic exposure control, averaging of adjacent pixels, and digital zoom have been implemented, making the sensor suitable for a wide field of applications.
Abstract Purpose To assess the impact of separate non-image interpretive task and image-interpretive task workflows in an academic neuroradiology practice. Materials and Methods A prospective, ...randomized, observational investigation of a centralized academic neuroradiology reading room was performed. The primary reading room fellow was observed over a one-month period using a time-and-motion methodology, recording frequency and duration of tasks performed. Tasks were categorized into separate image interpretive and non-image interpretive workflows. Post-intervention observation of the primary fellow was repeated following the implementation of a consult assistant responsible for non-image interpretive tasks. Pre- and post-intervention data were compared. Results Following separation of image-interpretive and non-image interpretive workflows, time spent on image-interpretive tasks by the primary fellow increased from 53.8% to 73.2% while non-image interpretive tasks decreased from 20.4% to 4.4%. Mean time duration of image interpretation nearly doubled, from 05:44 to 11:01 ( p = 0.002). Decreases in specific non-image interpretive tasks, including phone calls/paging (2.86/hr versus 0.80/hr), in-room consultations (1.36/hr versus 0.80/hr), and protocoling (0.99/hr versus 0.10/hr), were observed. The consult assistant experienced 29.4 task switching events per hour. Rates of specific non-image interpretive tasks for the CA were 6.41/hr for phone calls/paging, 3.60/hr for in-room consultations, and 3.83/hr for protocoling. Conclusion Separating responsibilities into NIT and IIT workflows substantially increased image interpretation time and decreased TSEs for the primary fellow. Consolidation of NITs into a separate workflow may allow for more efficient task completion.
An analog VLSI hardware architecture for the distributed simulation of large-scale spiking neural networks has been developed. Several hundred integrated computing nodes, each hosting up to 512 ...neurons, will be interconnected and operated on un-cut silicon wafers. The electro-technical aspects and the details of the hardware implementation are covered in a separate contribution to this conference. This paper focuses on the usability of the system by demonstrating that biologically relevant network models can in fact be mapped to this system. Different network configurations are established on the hardware by programmable switch matrices, repeaters, and address decoders. Systematic routing algorithms are presented to map a given network model to the hardware system. Routing is simulated for several network examples, proving the systempsilas practical applicability. Furthermore, the routing simulations are used to fix values for yet open hardware parameters.
The study aimed to assess perceptions of reading room workflow and the impact separating image-interpretive and nonimage-interpretive task workflows can have on radiologist perceptions of workplace ...disruptions, workload, and overall satisfaction.
A 14-question survey instrument was developed to measure radiologist perceptions of workplace interruptions, satisfaction, and workload prior to and following implementation of separate image-interpretive and nonimage-interpretive reading room workflows. The results were collected over 2 weeks preceding the intervention and 2 weeks following the end of the intervention. The results were anonymized and analyzed using univariate analysis.
A total of 18 people responded to the preintervention survey: 6 neuroradiology fellows and 12 attending neuroradiologists. Fifteen people who were then present for the 1-month intervention period responded to the postintervention survey. Perceptions of workplace disruptions, image interpretation, quality of trainee education, ability to perform nonimage-interpretive tasks, and quality of consultations (P < 0.0001) all improved following the intervention. Mental effort and workload also improved across all assessment domains, as did satisfaction with quality of image interpretation and consultative work.
Implementation of parallel dedicated image-interpretive and nonimage-interpretive workflows may improve markers of radiologist perceptions of workplace satisfaction.
We have designed and constructed a physical pendulum that allows us to demonstrate its behavior for a variety of forces and initial conditions. All parameters in the equation of motion can be defined ...by the user. The apparatus can demonstrate phenomena ranging from simple undamped harmonic oscillations to chaotic behavior. The position of the pendulum and the derived quantities such as the velocity and acceleration can be stored for later analysis.
Background
The diagnosis and treatment of acute stroke requires timed and coordinated effort across multiple clinical teams.
Purpose
To analyze the frequency and temporal distribution of emergent ...stroke evaluations (ESEs) to identify potential contributory workflow factors that may delay the initiation and subsequent evaluation of emergency department stroke patients.
Material and Methods
A total of 719 sentinel ESEs with concurrent neuroimaging were identified over a 22-month retrospective time period. Frequency data were tabulated and odds ratios calculated.
Results
Of all ESEs, 5% occur between 01:00 and 07:00. ESEs were most frequent during the late morning and early afternoon hours (10:00–14:00). Unexpectedly, there was a statistically significant decline in the frequency of ESEs that occur at the 14:00 time point.
Conclusion
Temporal analysis of ESEs in the emergency department allowed us to identify an unexpected decrease in ESEs and through process improvement methodologies (Lean and Six Sigma) and identify potential workflow elements contributing to this observation.
We propose a Markov process model for spike-frequency adapting neural ensembles that synthesizes existing mean-adaptation approaches, population density methods, and inhomogeneous renewal theory, ...resulting in a unified and tractable framework that goes beyond renewal and mean-adaptation theories by accounting for correlations between subsequent interspike intervals. A method for efficiently generating inhomogeneous realizations of the proposed Markov process is given, numerical methods for solving the population equation are presented, and an expression for the first-order interspike interval correlation is derived. Further, we show that the full five-dimensional master equation for a conductance-based integrate-and-fire neuron with spike-frequency adaptation and a relative refractory mechanism driven by Poisson spike trains can be reduced to a two-dimensional generalization of the proposed Markov process by an adiabatic elimination of fast variables. For static and dynamic stimulation, negative serial interspike interval correlations and transient population responses, respectively, of Monte Carlo simulations of the full five-dimensional system can be accurately described by the proposed two-dimensional Markov process.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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