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Automated point-neuron simplification of data-driven microcircuit models

Publications associées (50)

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Principles of Network Plasticity in Neocortical Microcircuits

András Ecker

Synaptic plasticity underlies our ability to learn and adapt to the constantly changing environment. The phenomenon of synapses changing their efficacy in an activity-dependent manner is often studied in small groups of neurons in vitro or indirectly throu ...

EPFL2023

QuNex-An integrative platform for reproducible neuroimaging analytics

Valerio Zerbi

IntroductionNeuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in ...

FRONTIERS MEDIA SA2023

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

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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

Towards the end of the second trimester of gestation, a human fetus is able to register environmental sounds. This in utero auditory experience is characterized by comprising strongly low-pass-filtered versions of sounds from the external world. Here, we p ...

WILEY2022

Over the last decade, Low Intensity Focused Ultrasound Stimulation (LIFUS) has emerged as an attractive technology to modulate the activity of deep neural targets without invasive procedures. However, the underlying mechanisms by which ultrasonic waves can ...

EPFL2021

Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity

Wulfram Gerstner, Tilo Schwalger, Valentin Marc Schmutz

Coarse-graining microscopic models of biological neural networks to obtain mesoscopic models of neural activities is an essential step towards multi-scale models of the brain. Here, we extend a recent theory for mesoscopic population dynamics with static s ...

2020

Recent advances in Voice Activity Detection (VAD) are driven by artificial and Recurrent Neural Networks (RNNs), however, using a VAD system in battery-operated devices requires further power efficiency. This can be achieved by neuromorphic hardware, which ...

IEEE2020

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Recently the building of large neuronal circuits from realistic neuron models has gained traction. This bottom-up approach relies on the accurate description of the primitive elements composing the brain such as neurons and astrocytes, that are then aggreg ...

ACM2020

Dense Computer Replica of Cortical Microcircuits Unravels Cellular Underpinnings of Auditory Surprise Response

Henry Markram, Eilif Benjamin Muller, Srikanth Ramaswamy, Michael Reimann, James Gonzalo King, Oren Amsalem, Idan Segev

The nervous system is notorious for its strong response evoked by a surprising sensory input, but the biophysical and anatomical underpinnings of this phenomenon are only partially understood. Here we utilized in-silico experiments of a biologically-detail ...

2020