Mihai A. Petrovici

Orcid: 0000-0003-2632-0427

Affiliations:
  • Heidelberg University, Kirchhoff Institute for Physics, Germany
  • University of Bern, Department of Physiology, Switzerland


According to our database1, Mihai A. Petrovici authored at least 54 papers between 2011 and 2024.

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Timeline

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Bibliography

2024
A method for the ethical analysis of brain-inspired AI.
Artif. Intell. Rev., June, 2024

Conductance-based dendrites perform Bayes-optimal cue integration.
PLoS Comput. Biol., 2024

Learning efficient backprojections across cortical hierarchies in real time.
Nat. Mac. Intell., 2024

DelGrad: Exact gradients in spiking networks for learning transmission delays and weights.
CoRR, 2024

Lu.i - A low-cost electronic neuron for education and outreach.
CoRR, 2024

Backpropagation through space, time, and the brain.
CoRR, 2024

Order from chaos: Interplay of development and learning in recurrent networks of structured neurons.
CoRR, 2024

Emulating insect brains for neuromorphic navigation.
CoRR, 2024

2023
Precision estimation and second-order prediction errors in cortical circuits.
CoRR, 2023

Gradient-based methods for spiking physical systems.
CoRR, 2023

Learning beyond sensations: how dreams organize neuronal representations.
CoRR, 2023

NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking.
CoRR, 2023

Learning Efficient Backprojections Across Cortical Hierarchies in Real Time.
Proceedings of the Artificial Neural Networks and Machine Learning, 2023

2022
Cortical oscillations support sampling-based computations in spiking neural networks.
PLoS Comput. Biol., 2022

DELAUNAY: a dataset of abstract art for psychophysical and machine learning research.
CoRR, 2022

The Yin-Yang dataset.
Proceedings of the NICE 2022: Neuro-Inspired Computational Elements Conference, 2022

Quantum many-body states: A novel neuromorphic application.
Proceedings of the NICE 2022: Neuro-Inspired Computational Elements Conference, 2022

2021
Structural plasticity on an accelerated analog neuromorphic hardware system.
Neural Networks, 2021

Fast and energy-efficient neuromorphic deep learning with first-spike times.
Nat. Mach. Intell., 2021

Uncovering Neuronal Learning Principles through Artificial Evolution.
ERCIM News, 2021

Fast and Energy-efficient Deep Neuromorphic Learning.
ERCIM News, 2021

BrainScaleS: Greater Versatility for Neuromorphic Emulation.
ERCIM News, 2021

Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons.
CoRR, 2021

Variational learning of quantum ground states on spiking neuromorphic hardware.
CoRR, 2021

Memory semantization through perturbed and adversarial dreaming.
CoRR, 2021

Latent Equilibrium: Arbitrarily fast computation with arbitrarily slow neurons.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Evolving neuronal plasticity rules using cartesian genetic programming.
Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

2020
Spiking neuromorphic chip learns entangled quantum states.
CoRR, 2020

Closed-loop experiments on the BrainScaleS-2 architecture.
Proceedings of the NICE '20: Neuro-inspired Computational Elements Workshop, 2020

Natural gradient learning for spiking neurons.
Proceedings of the NICE '20: Neuro-inspired Computational Elements Workshop, 2020

Conductance-based dendrites perform reliability-weighted opinion pooling.
Proceedings of the NICE '20: Neuro-inspired Computational Elements Workshop, 2020

Fast and deep neuromorphic learning with first-spike coding.
Proceedings of the NICE '20: Neuro-inspired Computational Elements Workshop, 2020



2019
Stochasticity from function - Why the Bayesian brain may need no noise.
Neural Networks, 2019

Fast and deep neuromorphic learning with time-to-first-spike coding.
CoRR, 2019

Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation, 2019

2018
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study.
CoRR, 2018

Generative models on accelerated neuromorphic hardware.
CoRR, 2018

2017
Spiking neurons with short-term synaptic plasticity form superior generative networks.
CoRR, 2017

Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System.
CoRR, 2017

Pattern representation and recognition with accelerated analog neuromorphic systems.
CoRR, 2017



Robustness from structure: Inference with hierarchical spiking networks on analog neuromorphic hardware.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

2016
Stochastic inference with spiking neurons in the high-conductance state.
CoRR, 2016

The high-conductance state enables neural sampling in networks of LIF neurons.
CoRR, 2016

Single Pixel Camera with Compressive Sensing by non-uniform sampling.
Proceedings of the International Conference on Communications, 2016

2015
Form vs. function: theory and models for neuronal substrates.
PhD thesis, 2015

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons.
Frontiers Comput. Neurosci., 2015

Rate-distortion performance of compressive sensing in single pixel camera.
Proceedings of the IEEE International Conference on Industrial Technology, 2015

2014
Bridging the gap between software simulation and emulation on neuromorphic hardware: An investigation of causes, effects and compensation of network-level anomalies in a mixed-signal waferscale neuromorphic modeling platform.
CoRR, 2014

2013
Stochastic inference with deterministic spiking neurons.
CoRR, 2013

2011
A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems.
Biol. Cybern., 2011


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