Publication
Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models
Publications associées (44)
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 Infoscience2024Task-driven neural network models predict neural dynamics of proprioception
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 ...
2023Mesoscopic 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 SCIENCE2022Taming 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 ...
EPFL2022Fitting summary statistics of neural data with a differentiable spiking network simulator
Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Shuqi Wang
Fitting network models to neural activity is an important tool in neuroscience. A popular approach is to model a brain area with a probabilistic recurrent spiking network whose parameters maximize the likelihood of the recorded activity. Although this is w ...
2021SPHARMA approximations for stationary functional time series on the sphere
In this paper, we focus on isotropic and stationary sphere-cross-time random fields. We first introduce the class of spherical functional autoregressive-moving average processes (SPHARMA), which extend in a natural way the spherical functional autoregressi ...
2021Low-dimensional firing-rate dynamics for populations of renewal-type spiking neurons
The macroscopic dynamics of large populations of neurons can be mathematically analyzed using low-dimensional firing-rate or neural-mass models. However, these models fail to capture spike synchronization effects and nonstationary responses of the populati ...
AMER PHYSICAL SOC2020How 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 ...
2019Inferring and validating mechanistic models of neural microcircuits based on spike-train data
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 ...
2019Fréchet means and Procrustes analysis in Wasserstein space
We consider two statistical problems at the intersection of functional and non-Euclidean data analysis: the determination of a Fréchet mean in the Wasserstein space of multivariate distributions; and the optimal registration of deformed random measures and ...
2019