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Carbon nanotubes might improve neuronal performance by favouring electrical shortcuts

Publications associées (42)

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Wulfram Gerstner, Shuqi Wang, Valentin Marc Schmutz

Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved? Modeling the unobserved neurons with large pools of hidden spiking neurons leads to severely underconstr ...

2022

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity

Tilo Schwalger, Valentin Marc Schmutz

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

Uncovering the neural encoding of behavior in the adult Drosophila motor system

Chin-Lin Chen

One of the most important goals in neuroscience research has always been to understand how animals control their behavior. However, the long focus on the role of brain neurons in behavioral control might be missing the full story. In fact, brain-wide fluct ...

EPFL2022

A modeling framework to understand and optimize ultrasound neuromodulation

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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

Intracellular recording of cardiomyocyte action potentials with nanopatterned volcano-shaped microelectrodes

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Micro-nanotechnology based multielectrode arrays have led to remarkable progress in the field of transmembrane voltage recordings of excitable cells. However, electrode geometries alone have failed to produce a cell-electrode interface that is sufficiently ...

EPFL2020

An efficient analytical reduction of detailed nonlinear neuron models

Felix Schürmann, Pramod Shivaji Kumbhar, Michael Emiel Gevaert, Oren Amsalem, Idan Segev, Guy Eyal

Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of these ...

2020

Extensive and diverse patterns of cell death sculpt neural networks in insects

Pavan P Ramdya, Chin-Lin Chen

Changes to the structure and function of neural networks are thought to underlie the evolutionary adaptation of animal behaviours. Among the many developmental phenomena that generate change programmed cell death (PCD) appears to play a key role. We show t ...

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Microchannel-based technologies for nerve interfacing

Sandra Gribi

Biomedical technologies in neuroprosthetics are made to restore or replace functions from the nervous system, as well as alleviate pathological conditions. Peripheral nerve interfaces use neuromodulatory techniques to inhibit pain or depression, prevent ep ...

EPFL2019

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

Wulfram Gerstner, Tilo Schwalger, Samuel Pavio Muscinelli

While most models of randomly connected neural networks assume single-neuron models with simple dynamics, neurons in the brain exhibit complex intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of single neurons and recurr ...

2019

Nonlinear microscopy for temporally and spatially resolved label-free imaging of living cells

Marie Eve Pascale Didier

The human brain consists of approximately a hundred billion of neurons that communicate through a unique series of biochemical and electrical processes. When neuronal networks are solicited, electrochemical processes happen involving millions of cells with ...

EPFL2018

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