170 citations · 356 across the 12 of their papers we have counts for
12 papers
How random connectivity shapes the fluctuating dynamics of finite-size neural populations
Nils E. Greven, Jonas Ranft, Tilo Schwalger
Mesoscopic models of finite-size neuronal populations are crucial to understand the dynamics of neural networks in the brain, especially their fluctuations and response to stimuli.…
A refractory density approach to a multi-scale SEIRS epidemic model
Anton Chizhov, Laurent Pujo-Menjouet, Tilo Schwalger +1
We propose a novel multi-scale modeling framework for infectious disease spreading, borrowing ideas and modeling tools from the so-called Refractory Density (RD) approach. We intro…
Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity
Bastian Pietras, Valentin Schmutz, Tilo Schwalger
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 pat…
Mapping Input Noise to Escape Noise in Integrate-and-fire neurons: A Level-Crossing Approach
Tilo Schwalger
Noise in spiking neurons is commonly modeled by a noisy input current or by generating output spikes stochastically with a voltage-dependent hazard rate ("escape noise"). While inp…
On a finite-size neuronal population equation
Valentin Schmutz, Eva Löcherbach, Tilo Schwalger
Population equations for infinitely large networks of spiking neurons have a long tradition in theoretical neuroscience. In this work, we analyze a recent generalization of these e…
Low-dimensional firing-rate dynamics for populations of renewal-type spiking neurons
Bastian Pietras, Noé Gallice, Tilo Schwalger
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 captu…
Mind the Last Spike -- Firing Rate Models for Mesoscopic Populations of Spiking Neurons
Tilo Schwalger, Anton V. Chizhov
The dominant modeling framework for understanding cortical computations are heuristic firing rate models. Despite their success, these models fall short to capture spike synchroniz…
Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity
Valentin Schmutz, Wulfram Gerstner, Tilo Schwalger
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…
How single neuron properties shape chaotic dynamics and signal transmission in random neural networks
Samuel P. Muscinelli, Wulfram Gerstner, Tilo Schwalger
While most models of randomly connected networks assume nodes with simple dynamics, nodes in realistic highly connected networks, such as neurons in the brain, exhibit intrinsic dy…
Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size
Tilo Schwalger, Moritz Deger, Wulfram Gerstner
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…
Fluctuations and information filtering in coupled populations of spiking neurons with adaptation
Moritz Deger, Tilo Schwalger, Richard Naud +1
Finite-sized populations of spiking elements are fundamental to brain function, but also used in many areas of physics. Here we present a theory of the dynamics of finite-sized pop…
Patterns of interval correlations in neural oscillators with adaptation
Tilo Schwalger, Benjamin Lindner
Neural firing is often subject to negative feedback by adaptation currents. These currents can induce strong correlations among the time intervals between spikes. Here we study ana…