Biology subjects

Stimberg, M.

Publications and source records attributed to Stimberg, M..

7 recordsLinked to original sources

bioRxiv2026

Active control of surface immobilization in the foraging behavior of Paramecium

Paramecium is a common freshwater ciliate, a unicellular swimming eukaryote that feeds on bacteria. Since the discovery that its motility is controlled by action potentials, which earned it the nickname of "swimming neuron", many studies have documented the effect of various stimuli on its behavior, as well as their physiological basis, highlighting the richness of adaptive behavior in this unicellular organism. The logic of its autonomous behavior has received comparably less attention. For example, when Paramecium encounters a surface, it may slide or avoid it by a directional change, triggered by an action potential, or it may immobilize on the surface, a behavior often called thigmotaxis. Here we ask why and how Paramecium immobilizes on surfaces. With long term behavioral tracking, we show that this variable behavior is a component of Paramecium's foraging behavior in the presence of bacteria, where the organism alternates between exploring and stopping on surfaces to feed. This immobilization is physiologically controlled by a calcium influx. Using mutant strains, high speed imaging, particle image velocimetry and hydrodynamics simulation, we further show that surface immobilization occurs by an inhibition of locomotor cilia, while oral cilia continue beating strongly. These findings open the perspective of studying the physiological control of foraging in a unicellular organism, a complex ecologically relevant behavior potentially involving sensory processing, memory, decision making, and motor control.

microbiology↗

bioRxiv2025

Single camera estimation of microswimmer depth with a convolutional network

A number of techniques have been developed to measure the three-dimensional trajectories of protists, which require special experimental setups, such as a pair of orthogonal cameras. On the other hand, machine learning techniques have been used to estimate the vertical position of spherical particles from the defocus pattern, but they require the acquisition of a labeled dataset with finely spaced vertical positions. Here we describe a simple way to make a dataset of Paramecium images labeled with vertical position from a single 5 minutes movie, based on a tilted slide setup. We used this dataset to train a simple convolutional network to estimate the vertical position of Paramecium from conventional bright field images. As an application, we show that this technique has sufficient accuracy to study the surface following behavior of Paramecium (thigmotaxis).

microbiology↗

bioRxiv2023

Combined statistical-mechanistic modeling links ionchannel genes to physiology of cortical neuron types

Neural cell types have classically been characterized by their anatomy and electrophysiology. More recently, single-cell transcriptomics has enabled an increasingly fine genetically defined taxonomy of cortical cell types, but the link between the gene expression of individual cell types and their physiological and anatomical properties remains poorly understood. Here, we develop a hybrid modeling approach to bridge this gap. Our approach combines statistical and mechanistic models to predict cells electrophysiological activity from their gene expression pattern. To this end, we fit biophysical Hodgkin-Huxley-based models for a wide variety of cortical cell types using simulation-based inference, while overcoming the challenge posed by the mismatch between the mathematical model and the data. Using multimodal Patch-seq data, we link the estimated model parameters to gene expression using an interpretable sparse linear regression model. Our approach recovers specific ion channel gene expressions as predictive of biophysical model parameters including ion channel densities, directly implicating their mechanistic role in determining neural firing.

neuroscience↗

bioRxiv2020

Dendrites decrease the synaptic weight resolution necessary to compute

In theory, neurons modelled as single layer perceptrons can implement all linearly separable computations. In practice, however, these computations may require arbitrarily precise synaptic weights. This is a strong constraint since both, biological neurons and their artificial counterparts, have to cope with limited precision. Here, we explore how the non-linear processing in dendrites helps overcoming this constraint. We start by finding a class of computations which requires increasing precision with the number of inputs in a perceptron and show that it can be implemented without this constraint in a neuron with sub-linear subunits. Then, we complement this analytical study by a simulation of a biophysical neuron model with two passive dendrites and a soma, and show that it can implement this computation. This works demonstrates a new role of dendrites in neural computation: by distributing the computation across independent subunits, the same computation can be performed more efficiently with less precise tuning of the synaptic weights. We hope that this works not only offers new insight into the importance of dendrites for biological neurons, but also paves the way for new, more efficient architectures of artificial neuromorphic chips. Author SummaryIn theory, we know how much neurons can compute, in practice, the number of possible synaptic weights values limits their computation capacity. Such a limitation holds true for artificial and synthetic neurons. We introduce here a computation where the required means evolve significantly with the number of inputs, this poses a problem as neurons receive multiple thousands of inputs. We study here how the neurons receptive element-called dendrites-can mitigate such a problem. We show that, without dendrites, the largest synaptic weight need to be multiple orders of magnitude larger than the smallest to implement the computation. Yet a neuron with dendrites implements the same computation with constant synaptic weights whatever the number of inputs. This study paves the way for the use of dendritic neurons in a new generation of artificial neural network and neuromorphic chips with a considerably better cost-benefit balance.

neuroscience↗

bioRxiv2019

Brian 2: an intuitive and efficient neural simulator

To be maximally useful for neuroscience research, neural simulators must make it possible to define original models. This is especially important because a computational experiment might not only need descriptions of neurons and synapses, but also models of interactions with the environment (e.g. muscles), or the environment itself. To preserve high performance when defining new models, current simulators offer two options: low-level programming, or mark-up languages (and other domain specific languages). The first option requires time and expertise, is prone to errors, and contributes to problems with reproducibility and replicability. The second option has limited scope, since it can only describe the range of neural models covered by the ontology. Other aspects of a computational experiment, such as the stimulation protocol, cannot be expressed within this framework. \"Brian\" 2 is a complete rewrite of Brian that addresses this issue by using runtime code generation with a procedural equation-oriented approach. Brian 2 enables scientists to write code that is particularly simple and concise, closely matching the way they conceptualise their models, while the technique of runtime code generation automatically transforms high level descriptions of models into efficient low level code tailored to different hardware (e.g. CPU or GPU). We illustrate it with several challenging examples: a plastic model of the pyloric network of crustaceans, a closed-loop sensorimotor model, programmatic exploration of a neuron model, and an auditory model with real-time input from a microphone.

neuroscience↗

bioRxiv2018

Brian2GeNN: a system for accelerating alarge variety of spiking neural networkswith graphics hardware

\"Brian\" is a popular Python-based simulator for spiking neural networks, commonly used in computational neuroscience. GeNN is a C++-based meta-compiler for accelerating spiking neural network simulations using consumer or high performance grade graphics processing units (GPUs). Here we introduce a new software package, Brian2GeNN, that connects the two systems so that users can make use of GeNN GPU acceleration when developing their models in Brian, without requiring any technical knowledge about GPUs, C++ or GeNN. The new Brian2GeNN software uses a pipeline of code generation to translate Brian scripts into C++ code that can be used as input to GeNN, and subsequently can be run on suitable NVIDIA GPU accelerators. From the users perspective, the entire pipeline is invoked by adding two simple lines to their Brian scripts. We have shown that using Brian2GeNN, typical models can run tens to hundreds of times faster than on CPU.

neuroscience↗

bioRxiv2017

Modeling neuron-glia interactions with the Brian 2 simulator

Despite compelling evidence that glial cells could crucially regulate neural network activity, the vast majority of available neural simulators ignores the possible contribution of glia to neuronal physiology. Here, we show how to model glial physiology and neuron-glia interactions in the Brian 2 simulator. Brian 2 offers facilities to explicitly describe any model in mathematical terms with limited and simple simulator-specific syntax, automatically generating high-performance code from the user-provided descriptions. The flexibility of this approach allows us to model not only networks of neurons, but also individual glial cells, electrical coupling of glial cells, and the interaction between glial cells and synapses. We therefore conclude that Brian 2 provides an ideal platform to efficiently simulate glial physiology, and specifically, the influence of astrocytes on neural activity.

neuroscience↗