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Effective minimal threshold models of neuronal activity

Related publications (53)

Taming neuronal noise with large networks

How does reliable computation emerge from networks of noisy neurons? While individual neurons are intrinsically noisy, the collective dynamics of populations of neurons taken as a whole can be almost deterministic, supporting the hypothesis that, in the br ...

EPFL2022

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Detailed single neuron modeling is widely used to study neuronal functions. While cellular and functional diversity across the mammalian cortex is vast, most of the available computational tools are dedicated to the reproduction of a small set of specific ...

2022

For solving tasks such as recognizing a song, answering a question, or inverting a sequence of symbols, cortical microcircuits need to integrate and manipulate information that was dispersed over time during the preceding seconds. Creating biologically rea ...

ELIFE SCIENCES PUBLICATIONS LTD2021

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

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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

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

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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

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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

Inferring and validating mechanistic models of neural microcircuits based on spike-train data

Olivier Richard Hagens

The interpretation of neuronal spike train recordings often relies on abstract statistical models that allow for principled parameter estimation and model selection but provide only limited insights into underlying microcircuits. In contrast, mechanistic m ...

2019

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size

Wulfram Gerstner, Tilo Schwalger, Moritz Deger

Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacti ...

Public Library of Science2017

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