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Signal-to-signal neural networks for improved spike estimation from calcium imaging data

Related publications (44)

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Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

EPFL Infoscience2024

Supervised learning and inference of spiking neural networks with temporal coding

Ana Stanojevic

The way biological brains carry out advanced yet extremely energy efficient signal processing remains both fascinating and unintelligible. It is known however that at least some areas of the brain perform fast and low-cost processing relying only on a smal ...

EPFL2023

Task-driven neural network models predict neural dynamics of proprioception

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

2023

Interpretable statistical representations of neural population dynamics and geometry

Pierre Vandergheynst, Alexis Arnaudon, Adám Gosztolai

The dynamics of neuron populations during diverse tasks often evolve on low-dimensional manifolds. However, it remains challenging to discern the contributions of geometry and dynamics for encoding relevant behavioural variables. Here, we introduce an unsu ...

2023

Learnable latent embeddings for joint behavioural and neural analysis

Mackenzie Mathis, Steffen Schneider, Jin Hwa Lee

Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural re ...

NATURE PORTFOLIO2023

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

Olga Fink, Luca Biggio

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability impr ...

2023

Timing is Everything

Karen Adam

In this thesis, timing is everything. In the first part, we mean this literally, as we tackle systems that encode information using timing alone. In the second part, we adopt the standard, metaphoric interpretation of this saying and show the importance of ...

EPFL2022

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal rep ...

PUBLIC LIBRARY SCIENCE2022

Using Animal Motion Capture to Learn Neural Representations

Semih Günel

Understanding behavior from neural activity is a fundamental goal in neuroscience. It has practical applications in building robust brain-machine interfaces, human-computer interaction, and assisting patients with neurological disabilities. Despite the eve ...

EPFL2022

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Developing energy-saving neural network models is a topic of rapidly increasing interest in the artificial intelligence community. Spiking neural networks (SNNs) are biologically inspired models that strive to leverage the energy efficiency stemming from a ...

IEEE2022