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  1. Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics

    Publication
    Artificial neural networks that learn to perform Principal Component Analysis (PCA) and related tasks using strictly local learning rules have been previously derived based on the principle of …

  2. Flexibility in motor timing constrains the topology and dynamics of pattern generator circuits

    Publication
    Temporally precise movement patterns underlie many motor skills and innate actions, yet the flexibility with which the timing of such stereotyped behaviors can be modified is poorly understood …

  3. Holography, Fractals and the Weyl Anomaly

    Publication
    We study the large source asymptotics of the generating functional in quantum field theory using the holographic renormalization group, and draw comparisons with the asymptotics of the Hopf …

  4. Why Do Similarity Matching Objectives Lead to Hebbian / Anti-Hebbian Networks?

    Publication
    Modeling self-organization of neural networks for unsupervised learning using Hebbian and anti-Hebbian plasticity has a long history in neuroscience. Yet, derivations of single-layer networks with …

  5. A clustering neural network model of insect olfaction

    Publication

  6. Blind Nonnegative Source Separation Using Biological Neural Networks

    Publication

  7. Resource-efficient perceptron has sparse synaptic weight distribution

    Publication
    Resource-efficiency is important for biological function of neurons. Using the perceptron as a model of a neuron, we show that resource-efficient learning implies sparse neural connectivity …

  8. Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?

    Publication
    Modeling self-organization of neural networks for unsupervised learning using Hebbian and anti-Hebbian plasticity has a long history in neuroscience. Yet, derivations of single-layer networks with …

  9. Integrative neuromechanics of crawling in D. melanogaster larvae

    Publication
    Locomotion in an organism is a consequence of the coupled interaction between brain, body and environment. Motivated by qualitative observations and quantitative perturbations of crawling in Drosophila melanogaster …

  10. Derivation of Neural Circuits from the Similarity Matching Principle

    Publication
    Our brains analyze high-dimensional datasets streamed by our sensory organs in multiple stages. Sensory cortices, for example, perform tasks like dimensionality reduction, sparse feature discovery and clustering …

  11. Do retinal ganglion cells project natural scenes to their principal subspace and whiten them?

    Publication
    Several theories of early sensory processing suggest that it whitens sensory stimuli. Here, we test three key predictions of the whitening theory using recordings from 152 ganglion cells …

  12. Self-calibrating neural networks for dimensionality reduction

    Publication
    Recently, a novel family of biologically plausible online algorithms for reducing the dimensionality of streaming data has been derived from the similarity matching principle. In these algorithms, the …

  13. Acute off-target effects of neural circuit manipulations

    Publication
    Rapid and reversible manipulations of neural activity in behaving animals are transforming our understanding of brain function. An important assumption underlying much of this work is that evoked …

  14. A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks

    Publication
    To make sense of the world our brains must analyze high-dimensional datasets streamed by our sensory organs. Because such analysis begins with dimensionality reduction, modelling early sensory …

  15. Optimization theory of Hebbian/anti-Hebbian networks for PCA and whitening

    Publication
    In analyzing information streamed by sensory organs, our brains face challenges similar to those solved in statistical signal processing. This suggests that biologically plausible implementations of online signal …

  16. A Hebbian/Anti-Hebbian Network for Online Sparse Dictionary Learning Derived from Symmetric Matrix Factorization

    Publication
    Olshausen and Field (OF) proposed that neural computations in the primary visual cortex (V1) can be partially modelled by sparse dictionary learning. By minimizing the regularized representation error …