2,417 research outputs found
Simulation-based Inference for Model Parameterization on Analog Neuromorphic Hardware
The BrainScaleS-2 (BSS-2) system implements physical models of neurons as
well as synapses and aims for an energy-efficient and fast emulation of
biological neurons. When replicating neuroscientific experiment results, a
major challenge is finding suitable model parameters. This study investigates
the suitability of the sequential neural posterior estimation (SNPE) algorithm
for parameterizing a multi-compartmental neuron model emulated on the BSS-2
analog neuromorphic hardware system. In contrast to other optimization methods
such as genetic algorithms or stochastic searches, the SNPE algorithms belongs
to the class of approximate Bayesian computing (ABC) methods and estimates the
posterior distribution of the model parameters; access to the posterior allows
classifying the confidence in parameter estimations and unveiling correlation
between model parameters. In previous applications, the SNPE algorithm showed a
higher computational efficiency than traditional ABC methods. For our
multi-compartmental model, we show that the approximated posterior is in
agreement with experimental observations and that the identified correlation
between parameters is in agreement with theoretical expectations. Furthermore,
we show that the algorithm can deal with high-dimensional observations and
parameter spaces. These results suggest that the SNPE algorithm is a promising
approach for automating the parameterization of complex models, especially when
dealing with characteristic properties of analog neuromorphic substrates, such
as trial-to-trial variations or limited parameter ranges
Efficient viscosity contrast calculation for blood flow simulations using the lattice Boltzmann method
Computational Capabilities and Compiler Development for Neutral Atom Quantum Processors: Connecting Tool Developers and Hardware Experts
Neutral Atom Quantum Computing (NAQC) emerges as a promising hardware
platform primarily due to its long coherence times and scalability.
Additionally, NAQC offers computational advantages encompassing potential
long-range connectivity, native multi-qubit gate support, and the ability to
physically rearrange qubits with high fidelity. However, for the successful
operation of a NAQC processor, one additionally requires new software tools to
translate high-level algorithmic descriptions into a hardware executable
representation, taking maximal advantage of the hardware capabilities.
Realizing new software tools requires a close connection between tool
developers and hardware experts to ensure that the corresponding software tools
obey the corresponding physical constraints. This work aims to provide a basis
to establish this connection by investigating the broad spectrum of
capabilities intrinsic to the NAQC platform and its implications on the
compilation process. To this end, we first review the physical background of
NAQC and derive how it affects the overall compilation process by formulating
suitable constraints and figures of merit. We then provide a summary of the
compilation process and discuss currently available software tools in this
overview. Finally, we present selected case studies and employ the discussed
figures of merit to evaluate the different capabilities of NAQC and compare
them between two hardware setups.Comment: 32 pages, 13 figures, 2 table
Gradient-based methods for spiking physical systems
Recent efforts have fostered significant progress towards deep learning in
spiking networks, both theoretical and in silico. Here, we discuss several
different approaches, including a tentative comparison of the results on
BrainScaleS-2, and hint towards future such comparative studies.Comment: 2 page abstract, submitted to and accepted by the NNPC (International
conference on neuromorphic, natural and physical computing
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