Cargando…

Verifying the biological relevance of a neuromorphic hardware device

Detalles Bibliográficos
Autores principales:	Brüderle, Daniel, Meier, Karlheinz, Mueller, Eilif, Schemmel, Johannes
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2007
Materias:	Poster Presentation
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4436130/ http://dx.doi.org/10.1186/1471-2202-8-S2-P10

Ejemplares similares

Establishing a Novel Modeling Tool: A Python-Based Interface for a Neuromorphic Hardware System
por: Brüderle, Daniel, et al.
Publicado: (2009)

Compensating Inhomogeneities of Neuromorphic VLSI Devices Via Short-Term Synaptic Plasticity
por: Bill, Johannes, et al.
Publicado: (2010)

Non-renewal Markov models for spike-frequency adapting neural ensembles
por: Muller, Eilif, et al.
Publicado: (2007)

Reward-based learning under hardware constraints—using a RISC processor embedded in a neuromorphic substrate
por: Friedmann, Simon, et al.
Publicado: (2013)

Six Networks on a Universal Neuromorphic Computing Substrate
por: Pfeil, Thomas, et al.
Publicado: (2013)

Benchmarking the impact of information processing in the insect olfactory system with a spiking neuromorphic classifier
por: Schmuker, Michael, et al.
Publicado: (2011)

Neuromorphic Hardware Learns to Learn
por: Bohnstingl, Thomas, et al.
Publicado: (2019)

General form of learning algorithms for neuromorphic hardware implementation
por: Gorchetchnikov, Anatoli, et al.
Publicado: (2010)

Is a 4-Bit Synaptic Weight Resolution Enough? – Constraints on Enabling Spike-Timing Dependent Plasticity in Neuromorphic Hardware
por: Pfeil, Thomas, et al.
Publicado: (2012)

Classification of multivariate data with a spiking neural network on neuromorphic hardware
por: Schmuker, Michael, et al.
Publicado: (2013)

Solving dynamical systems in neuromorphic hardware: simulation studies using balanced spiking networks
por: Bulanova, Anna S, et al.
Publicado: (2013)

Control of criticality and computation in spiking neuromorphic networks with plasticity
por: Cramer, Benjamin, et al.
Publicado: (2020)

A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware
por: Müller, Eric, et al.
Publicado: (2022)

Advances in neuromorphic hardware exploiting emerging nanoscale devices
por: Suri, Manan
Publicado: (2017)

Deterministic neural networks as sources of uncorrelated noise for probabilistic computations
por: Jordan, Jakob, et al.
Publicado: (2015)

Parallelization of Neural Processing on Neuromorphic Hardware
por: Peres, Luca, et al.
Publicado: (2022)

Beyond LIF Neurons on Neuromorphic Hardware
por: Ward, Mollie, et al.
Publicado: (2022)

Hybrid oxide brain-inspired neuromorphic devices for hardware implementation of artificial intelligence
por: Wang, Jingrui, et al.
Publicado: (2021)

Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms
por: Petrovici, Mihai A., et al.
Publicado: (2014)

The high-conductance state enables neural sampling in networks of LIF neurons
por: Petrovici, Mihai A, et al.
Publicado: (2015)

Real-time cortical simulation on neuromorphic hardware
por: Rhodes, Oliver, et al.
Publicado: (2020)

Benchmarking Neuromorphic Hardware and Its Energy Expenditure
por: Ostrau, Christoph, et al.
Publicado: (2022)

Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system
por: Han, Geonhui, et al.
Publicado: (2021)

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
por: Wunderlich, Timo, et al.
Publicado: (2019)

Hardware Realization of the Pattern Recognition with an Artificial Neuromorphic Device Exhibiting a Short-Term Memory
por: Przyczyna, Dawid, et al.
Publicado: (2019)

Hardware-Based Hopfield Neuromorphic Computing for Fall Detection
por: Yu, Zheqi, et al.
Publicado: (2020)

Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware
por: Primavera, Bryce A., et al.
Publicado: (2021)

Multi-state MRAM cells for hardware neuromorphic computing
por: Rzeszut, Piotr, et al.
Publicado: (2022)

Decoding of naturalistic textures from spike patterns of neuromorphic artificial mechanoreceptors
por: Mazzoni, Alberto, et al.
Publicado: (2015)

Finding a roadmap to achieve large neuromorphic hardware systems
por: Hasler, Jennifer, et al.
Publicado: (2013)

Criticality as a Set-Point for Adaptive Behavior in Neuromorphic Hardware
por: Srinivasa, Narayan, et al.
Publicado: (2015)

Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware
por: Stöckel, Andreas, et al.
Publicado: (2017)

Surrogate gradients for analog neuromorphic computing
por: Cramer, Benjamin, et al.
Publicado: (2022)

Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware
por: Knight, James C., et al.
Publicado: (2016)

Challenges hindering memristive neuromorphic hardware from going mainstream
por: Adam, Gina C., et al.
Publicado: (2018)

Efficient Synapse Memory Structure for Reconfigurable Digital Neuromorphic Hardware
por: Kim, Jinseok, et al.
Publicado: (2018)

Neuromorphic Systems Design by Matching Inductive Biases to Hardware Constraints
por: Muller, Lorenz K., et al.
Publicado: (2020)

Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
por: Camuñas-Mesa, Luis A., et al.
Publicado: (2019)

Variational learning of quantum ground states on spiking neuromorphic hardware
por: Klassert, Robert, et al.
Publicado: (2022)

A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware
por: Vanattou-Saïfoudine, Natacha, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_6ne5h1389nb4a11iig0d1b804g