Concept

Models of neural computation

Related publications (254)

CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data

Iordan Doytchinov, Qi Yan, Shanci Li, Jianhao Zheng

We present a visual localization system that learns to estimate camera poses in the real world with the help of synthetic data. Despite significant progress in recent years, most learning-based approaches to visual localization target at a single domain an ...

IEEE COMPUTER SOC2022

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

The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models

Jean-Denis Georges Emile Courcol, Michele Migliore, Genrich Ivaska, Carmen Alina Lupascu, Luca Leonardo Bologna, Shailesh Appukuttan

In the last decades, brain modeling has been established as a fundamental tool for understanding neural mechanisms and information processing in individual cells and circuits at different scales of observation. Building data-driven brain models requires th ...

FRONTIERS MEDIA SA2022

Methods for acute and long-term imaging of the ventral nerve cord in behaving adult Drosophila

Laura Joan Hermans

Understanding how neural circuits remodel and adapt to animal behavior is a central theme in the field of Neuroscience. One strategy to reach this goal is to repeatedly record the same animal's neural circuits and observe how they adapt when facing differe ...

EPFL2022

Breathing is coupled with voluntary initiation of mental imagery

Olaf Blanke, Oliver Alan Kannape, Hyeongdong Park

Previous research has suggested that bodily signals from internal organs are associated with diverse cortical and subcortical processes involved in sensory-motor functions, beyond homeostatic reflexes. For instance, a recent study demonstrated that the pre ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

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

Spike-Based Sensing and Communication for Highly Energy-Efficient Sensor Edge Nodes

Mihai Adrian Ionescu, Teodor Rosca

Highly energy-efficient wireless sensor nodes are a prerequisite for a sustainable operation of the Internet of things. Therefore, classical approaches for system design based on digital signal processing are not a viable solution, but system design has to ...

IEEE2022

A Time Encoding Approach to Training Spiking Neural Networks

Karen Adam

While Spiking Neural Networks (SNNs) have been gaining in popularity, it seems that the algorithms used to train them are not powerful enough to solve the same tasks as those tackled by classical Artificial Neural Networks (ANNs).In this paper, we provide ...

IEEE2022

HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data

Eilif Benjamin Muller, Werner Alfons Hilda Van Geit, Armando Romani, Szabolcs Kali, Carmen Alina Lupascu, Rosanna Migliore, Luca Leonardo Bologna, Sàra Sàray, Shailesh Appukuttan

Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophys ...

PUBLIC LIBRARY SCIENCE2021

Exact Neural Networks from Inexact Multipliers via Fibonacci Weight Encoding

David Atienza Alonso, Marina Zapater Sancho, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, William Andrew Simon

Edge devices must support computationally demanding algorithms, such as neural networks, within tight area/energy budgets. While approximate computing may alleviate these constraints, limiting induced errors remains an open challenge. In this paper, we pro ...

2021

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