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Showing 1–5 of 5 results
Advanced filters: Author: Laura Kriener Clear advanced filters

  • The authors propose a Generalized Latent Equilibrium framework for fully local credit assignment in physical, dynamical neuronal networks such as the brain. By exploiting dendritic structure and prospective coding in cortical neurons, it enables an online approximation of backpropagation through space and time.

    • Benjamin Ellenberger
    • Paul Haider
    • Mihai A. Petrovici
    ResearchOpen Access
    Nature Communications
    Volume: 17, P: 1-23

  • It has recently been shown that synaptic transmission delays enhance the computational capabilities of spiking neural networks. In this manuscript, the authors introduce an exact, event-based training method for various types of delays and benchmark it on mixed-signal neuromorphic hardware.

    • Julian Göltz
    • Jimmy Weber
    • Mihai A. Petrovici
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-10

  • 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.

    • J. Göltz
    • L. Kriener
    • M. A. Petrovici
    Research
    Nature Machine Intelligence
    Volume: 3, P: 823-835

  • The credit assignment problem involves assigning credit to synapses in a neural network so that weights are updated appropriately and the circuit learns. Max et al. developed an efficient solution to the weight transport problem in networks of biophysical neurons. The method exploits noise as an information carrier and enables networks to learn to solve a task efficiently.

    • Kevin Max
    • Laura Kriener
    • Mihai A. Petrovici
    Research
    Nature Machine Intelligence
    Volume: 6, P: 619-630

  • Brain-inspired neuromorphic algorithms and systems have shown essential advance in efficiency and capabilities of AI applications. In this Perspective, the authors introduce NeuroBench, a benchmark framework for neuromorphic approaches, collaboratively designed by researchers across industry and academia.

    • Jason Yik
    • Korneel Van den Berghe
    • Vijay Janapa Reddi
    ReviewsOpen Access
    Nature Communications
    Volume: 16, P: 1-24