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Found 9 papers, 3 papers with code
Efficient population coding of sensory stimuli
Here we derive a general theory of optimal population coding with neuronal activation functions of any shape, different types of noise and heterogeneous firing rates of the neurons by maximizing the Shannon mutual information between a stimulus and the neuronal spiking output subject to a constrain on the maximal firing rate.
Learning, fast and slow
These efficient representations may be encoded in the genome, resulting in a repertoire of fast learning that differs across species.
Curved Micro-Electrode Arrays
Multi-electrode arrays serve to record electrical signals of many neurons in the brain simultaneously.
Fine-Grained System Identification of Nonlinear Neural Circuits
We study the problem of sparse nonlinear model recovery of high dimensional compositional functions.
Learning by Turning: Neural Architecture Aware Optimisation
To address this problem, this paper conducts a combined study of neural architecture and optimisation, leading to a new optimiser called Nero: the neuronal rotator.
Factorized linear discriminant analysis for phenotype-guided representation learning of neuronal gene expression data
A central goal in neurobiology is to relate the expression of genes to the structural and functional properties of neuronal types, collectively called their phenotypes.
Learning compositional functions via multiplicative weight updates
This paper proves that multiplicative weight updates satisfy a descent lemma tailored to compositional functions.
PanDA: Panoptic Data Augmentation
The recently proposed panoptic segmentation task presents a significant challenge of image understanding with computer vision by unifying semantic segmentation and instance segmentation tasks.
Synthetic Examples Improve Generalization for Rare Classes
The ability to detect and classify rare occurrences in images has important applications - for example, counting rare and endangered species when studying biodiversity, or detecting infrequent traffic scenarios that pose a danger to self-driving cars.
