Biology subjects

Rotter, S.

Publications and source records attributed to Rotter, S..

11 recordsLinked to original sources

bioRxiv2025

The role of cortico-thalamic feedback for visual information processing

In the early visual system, neurons in the dorsal lateral geniculate nucleus (dLGN) not only relay visual inputs from the retina to the primary visual cortex (V1), but also receive cortico-thalamic (CT) feedback inputs, which anatomically outnumber feedforward inputs. Most experimental and theoretical work so far has focused on the modulatory influence of CT feedback on relay cells in the dLGN. However, the role of feedback for the activity of V1 neurons, which are mainly driven by visual input meditated by dLGN neurons, is still unclear. To address this question, we devised a spiking neural network model of the thalamo-cortical and cortico-thalamic loop, where the CT feedback can be turned on and off to study its effect. We identified three main phenomena of the effect of CT feedback on V1 neurons: (i) orientation selectivity of cortical responses is increased for all contrasts, (ii) preferred orientations of input and output are better aligned with each other, (iii) neuronal population activity represents the stimulus orientation with higher confidence. Our analysis showed that CT feedback generally improves the signal-to-noise ratio (SNR) of the responses of dLGN neurons and therefore also increases the SNR of thalamic inputs to V1 neurons. Moreover, we found that the modulatory effects on visual processing depend on the geometry of the CT feedback projections. We conclude that CT feedback increases the selectivity of V1 neurons particularly at low contrasts by modulating the activity of dLGN neurons. Author summaryExtensive experimental and theoretical studies have been conducted to understand the functional role of cortico-thalamic (CT) feedback on relay cells in the dorsal lateral geniculate nucleus (dLGN) during visual processing. However, it remains unclear how CT feedback affects the neuronal responses of V1 neurons. To address this question, we developed a computational model of the thalamo-cortico-thalamic loop circuit based on biophysical parameters. Our results indicate that CT feedback modulates the orientation preference of V1 neurons in a contrast-dependent manner: The increase of orientation selectivity (OS) is stronger and the input preferred orientation (PO) is better preserved at lower contrasts. Our analysis suggests a mechanistic explanation: The CT feedback attenuates the untuned part of the thalamic input, while it amplifies the tuned part. This effectively increases the signal-to-noise ratio (SNR) in particular for weak contrasts. Our network model for the first time allows us to study the effect of feedback on cortical neurons and understand the impact of the number and the geometry of feedback connections.

neuroscience↗

bioRxiv2025

Resolving inconsistent effects of tDCS on learning using a homeostatic structural plasticity model

Transcranial direct current stimulation (tDCS) is increasingly used to modulate motor learning. Current polarity and intensity, electrode montage, and application before or during learning had mixed effects. Both Hebbian and homeostatic plasticity were proposed to account for the observed effects, but the explanatory power of these models is limited. In a previous modeling study, we showed that homeostatic structural plasticity (HSP) can explain long-lasting after-effects of tDCS and transcranial magnetic stimulation (TMS). The interference between motor learning and tDCS, which are both based on HSP in our model, is a candidate mechanism to resolve complex and seemingly contradictory experimental observations. We implemented motor learning and tDCS in a spiking neural network subject to HSP. The anatomical connectivity of the engram induced by motor learning was used to quantify the impact of tDCS on motor learning. Our modeling results demonstrated that transcranial direct current stimulation applied before learning had weak modulatory effects. It led to a small reduction in connectivity if it was applied uniformly. When applied during learning, targeted anodal stimulation significantly strengthened the engram, while targeted cathodal or uniform stimulation weakened it. Applied after learning, targeted cathodal, but not anodal, tDCS boosted engram connectivity. Strong tDCS would distort the engram structure if not applied in a targeted manner. Our model explained both Hebbian and homeostatic phenomena observed in human tDCS experiments by assuming memory strength positively correlates with engram connectivity. This includes applications with different polarity, intensity, electrode montage, and timing relative to motor learning. The HSP model provides a promising framework for unraveling the dynamic interaction between learning and transcranial DC stimulation.

neuroscience↗

bioRxiv2023

Repetitive transcranial magnetic stimulation (rTMS) triggers dose-dependent homeostatic rewiring in recurrent neuronal networks

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique used to induce neuronal plasticity in healthy individuals and patients. Designing effective and reproducible rTMS protocols poses a major challenge in the field as the underlying biomechanisms remain elusive. Current clinical protocol designs are often based on studies reporting rTMS-induced long-term potentiation or depression of synaptic transmission. Herein, we employed computational modeling to explore the effects of rTMS on long-term structural plasticity and changes in network connectivity. We simulated a recurrent neuronal network with homeostatic structural plasticity between excitatory neurons, and demonstrated that this mechanism was sensitive to specific parameters of the stimulation protocol (i.e., frequency, intensity, and duration of stimulation). The feedback-inhibition initiated by network stimulation influenced the net stimulation outcome and hindered the rTMS-induced homeostatic structural plasticity, highlighting the role of inhibitory networks. These findings suggest a novel mechanism for the lasting effects of rTMS, i.e., rTMS-induced homeostatic structural plasticity, and highlight the importance of network inhibition in careful protocol design, standardization, and optimization of stimulation. Author summaryThe cellular and molecular mechanisms of clinically employed repetitive transcranial magnetic stimulation (rTMS) protocols remain not well understood. However, it is clear that stimulation outcomes depend heavily on protocol designs. Current protocol designs are mainly based on experimental studies that explored functional synaptic plasticity, such as long-term potentiation of excitatory neurotransmission. Using a computational approach, we sought to address the dose-dependent effects of rTMS on the structural remodeling of stimulated and non-stimulated connected networks. Our results suggest a new mechanism of action--activity-dependent homeostatic structural remodeling--through which rTMS may assert its lasting effects on neuronal networks. We showed that the effect of rTMS on structural plasticity critically depends on stimulation intensity, frequency, and duration and that recurrent inhibition can affect the outcome of rTMS-induced homeostatic structural plasticity. These findings emphasize the use of computational approaches for an optimized rTMS protocol design, which may support the development of more effective rTMS-based therapies.

neuroscience↗

bioRxiv2022

A theory of orientation selectivity emerging from randomly sampling the visual field

Neurons in rodent primary visual cortex are simultaneously tuned to several stimulus features, including orientation and spatial frequency of moving gratings used in experiments. Light-induced signals emitted by retinal ganglion cells (RGC) are relayed to the primary visual cortex (V1) via cells in the dorsal lateral geniculate nucleus (dLGN). However, there is currently no agreement on which thalamocortical transformation leads to the neuronal tuning curves observed in experiments. Here, we outline a model that explains the emergence of feature-specific neural responses as the result of a two-step integration process: First, the compound input to cortical neurons comes from a set of retinal sensors randomly placed in the receptive field. Second, the cortical responses to the combined input are shaped by the rectification caused by the spike threshold of the neurons. We performed numerical simulations of a thalamocortical network stimulated by moving gratings and found that simultaneous tuning to orientation and spatial frequency results from this spatio-temporal integration process. We also show how this tuning is related to the complex structure of the receptive fields that reflect the input. We conclude that different types of feature selectivity arise naturally from random thalamocortical projections. Moreover, we describe in detail the underlying neural mechanism.

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bioRxiv2022

Overcoming the Reproducibility Crisis - Results of the first Community Survey of the German National Research Data Infrastructure for Neuroscience.

The lack of reproducibility of research results is a serious problem - known as "the reproducibility crisis". The German National Research Data Infrastructure (NFDI) initiative implemented by the German Research Foundation (DFG) aims to help overcoming this crisis by developing sustainable solutions for research data management (RDM). NFDI comprises domain specific consortia across all science disciplines. In the field of neuroscience, NFDI Neuroscience (NFDI-Neuro) contributes to the strengthening of systematic and standardized RDM in its research communities. NFDI-Neuro conducted a comprehensive survey amongst the neuroscience community to determine the current needs, challenges, and opinions with respect to RDM. The outcomes of this survey are presented here. The German neuroscience community perceives barriers with respect to RDM and data sharing mainly linked to (1) lack of data and metadata standards, (2) lack of community adopted provenance tracking methods, 3) lack of a privacy preserving research infrastructure for sensitive data (4) lack of RDM literacy and (5) lack of required time and resources for proper RDM. NFDI-Neuro aims to systematically address these barriers by leading and contributing to the development of standards, tools, and infrastructure and by providing training, education, and support, as well as additional resources for RDM to its research community. The RDM work of NFDI-Neuro is conducted in close collaboration with its partner EBRAINS AISBL, the coordinating entity of the EU Flagship Human Brain Project, and its Research Infrastructure (RI) EBRAINS with more than 5000 registered users and developers from more than 70 countries of all continents. While NFDI-Neuro aims to address the German national needs, it closely aligns with the international community and the topics of the Digital Europe Program and EU Data Spaces. Significance StatementA comprehensive survey amongst the neuroscience community in Germany determined the current needs, challenges, and opinions with respect to standardized research data management (RDM) to overcome the reproducibility crisis. Significant deficits were pointed out concerning the perceived lack of standards for data and metadata, lack of provenance tracking and versioning of data, lack of protected digital research infrastructure for sensitive data and the lack of education and resources for proper RDM. Yet, at the same time, an overwhelming majority of community members indicated that they would be willing to share their data with other researchers and are interested to increase their RDM skills. Thus, the survey results suggest that training, the provision of standards, tools, infrastructure and additional resources for RDM holds the potential to significantly facilitate reproducible research in neuroscience.

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bioRxiv2021

NEST Desktop - An educational application for neuroscience

Simulation software for spiking neuronal network models matured in the past decades regarding performance and flexibility. But the entry barrier remains high for students and early career scientists in computational neuroscience since these simulators typically require programming skills and a complex installation. Here, we describe an installation-free Graphical User Interface (GUI) running in the web browser, which is distinct from the simulation engine running anywhere, on the students laptop or on a supercomputer. This architecture provides robustness against technological changes in the software stack and simplifies deployment for self-education and for teachers. Our new open source tool, NEST Desktop, comprises graphical elements for creating and configuring network models, running simulations, and visualizing and analyzing the results. NEST Desktop allows students to explore important concepts in computational neuroscience without the need to learn a simulator control language before. Our experiences so far highlight that NEST Desktop helps advancing both quality and intensity of teaching in computational neuroscience in regular university courses. We view the availability of the tool on public resources like the European ICT infrastructure for neuroscience EBRAINS as a contribution to equal opportunities. Significance StatementThe graphical user interface NEST Desktop makes neuronal network simulations accessible to non-programmers. It facilitates the interactive exploration of neuronal network models by integrating the whole workflow of wiring up the setup, simulating the neuronal dynamics, and analyzing the recorded activity data into a single tool. NEST Desktop effectively supports teaching the concepts and methods of computational neuroscience. Due to its installation-free web-based implementation, it is in particularly suitable for online courses.

neuroscience↗

bioRxiv2020

Time course of homeostatic structural plasticity in response to optogenetic stimulation in mouse anterior cingulate cortex

Plasticity is the mechanistic basis of development, aging, learning and memory, both in healthy and pathological brains. Structural plasticity is rarely accounted for in computational network models, due to a lack of insight into the underlying neuronal mechanisms and processes. Little is known about how the rewiring of networks is dynamically regulated. To inform such models, we characterized the time course of neural activity, the expression of synaptic proteins, and neural morphology employing an in vivo optogenetic mouse model. We stimulated pyramidal neurons in the anterior cingulate cortex of mice and harvested their brains at 1.5 h, 24 h, and 48 h after stimulation. Stimulus-induced cortical hyperactivity persisted up to 1.5 h and decayed to baseline after 24 h, indicated by c-Fos expression. The synaptic proteins VGLUT1 and PSD-95, in contrast, were upregulated at 24 h and downregulated at 48 h, respectively. Spine density and spine head volume were also increased at 24 h and decreased at 48 h. This specific sequence of events reflects a continuous joint evolution of activity and connectivity that is characteristic of the model of homeostatic structural plasticity. Our computer simulations thus corroborate the observed empirical evidence from our animal experiments.

neuroscience↗

bioRxiv2020

Homeostatic structural plasticity leads to the formation of memory engrams through synaptic rewiring in recurrent networks

Brain networks store new memories using functional and structural synaptic plasticity. Memory formation is generally attributed to Hebbian plasticity, while homeostatic plasticity is thought to have an ancillary role in stabilizing network dynamics. Here we report that homeostatic plasticity alone can also lead to the formation of stable memories. We analyze this phenomenon using a new theory of network remodeling, combined with numerical simulations of recurrent spiking neural networks that exhibit structural plasticity based on firing rate homeostasis. These networks are able to store repeatedly presented patterns and recall them upon the presentation of incomplete cues. Storage is fast, governed by the homeostatic drift. In contrast, forgetting is slow, driven by a diffusion process. Joint stimulation of neurons induces the growth of associative connections between them, leading to the formation of memory engrams. These memories are stored in a distributed fashion throughout connectivity matrix, and individual synaptic connections have only a small influence. Although memory-specific connections are increased in number, the total number of inputs and outputs of neurons undergo only small changes during stimulation. We find that homeostatic structural plasticity induces a specific type of "silent memories", different from conventional attractor states.

neuroscience↗

bioRxiv2018

Network remodeling induced by transcranial brain stimulation: A computational model of tDCS-triggered cell assembly formation

Transcranial direct current stimulation (tDCS) is a variant of non-invasive neuro-modulation, which promises treatment for diseases like major depressive disorder or chronic pain for patients resistant to conventional therapies. In experiments, long lasting after-effects were observed, suggesting that plastic changes were induced. The exact mechanism underlying the emergence and maintenance of these after-effects, however, remains elusive. Here we propose a model to explain how transcranial stimulation triggers a homeostatic response of the network involving growth and decay of synapses. In our model, the cortical tissue underneath the electrodes is conceived as a recurrent network of excitatory and inhibitory neurons, in which excitatory-to-excitatory synapses are subject to structural plasticity. Various aspects of stimulation were tested via numerical simulations of such networks, including size and montage of the electrode, as well as intensity and duration of the stimulation. Our results suggest that stimulation indeed perturbs the homeostatic equilibrium and leads to cell assembly formation. Strong focal stimulation, for example, enhances the connectivity of new cell assemblies by increasing the rate of synaptic remodeling. Repetitive stimulation with well-chosen duty cycles increases the impact of stimulation as well. The long-term goal of our work is to optimize the impact of tDCS in clinical applications.

neuroscience↗

bioRxiv2018

Propagation of orientation selectivity in a spiking network model of layered primary visual cortex

We studied the propagation of orientation selectivity over layers in a model of rodent primary visual cortex in terms of the underlying connectivity. While significant progress has been made in measuring the activity in the different sub-populations as well as the connectivity between those in experiments, a comprehensive theoretical explanation of how network structure and its dynamics are related is still missing. We suggest a model of layered mouse visual cortex by extending the model suggested by Potjans and Diesmann (2014) with thalamic input that has an orientation bias according to Sadeh et al. (2014). We then studied the response properties of the network to such non-homogeneous input. We found that, without further assumptions, the connectivity derived from experimental data leads to layer-specific distributions of orientation selectivity very similar to what has been observed in mouse experiments. Interestingly, the network settles in a dynamical operating point, in which the efficacy of the different projections and their contribution to orientation tuning deviate strongly from the expectations based on the underlying anatomical connectivity. To understand the processes that shape the observed dynamics, it is essential to perform the analysis of the microcircuit on the system level. With that perspective, we find in particular that the difference in tuning of L2/3 and L4 neurons is an immediate, albeit unexpected consequence of the specific network connectivity. Furthermore, we introduce a novel method for predicting the effects of optogenetic stimulation of specific neuronal sub-populations and demonstrate its power in network simulations.\n\nSignificance StatementUnderstanding the precise roles of neuronal sub-populations in shaping the activity of neuronal networks is a fundamental objective of neuroscience research. To this end, our work makes three important contributions. First, we show that the experimentally extracted connectivity suffices to explain the degree of selectivity of sub-populations in mouse visual cortex to visual stimulation. Second, we introduce a novel system-level approach for the analysis of input-output relations of recurrent networks, which lead to distinct activity patterns. Third, we present a method for the design of optogenetic experiments that can be used to devise specific stimuli which result in a desired and predictable change of neuronal activity.

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bioRxiv2018

Transmission of motor signals from the basal ganglia to the thalamus: effect of correlations, sensory responses, and excitation

Movement-related decreases in firing rate have been observed in basal ganglia output neurons. They may transmit motor signals to the thalamus, but the effect of these firing rate decreases on downstream neurons in the motor thalamus is not known. One possibility is that they lead to thalamic post-inhibitory rebound spikes. However, it has also been argued that the physiological conditions permitting rebound spiking are pathological, and primarily present in Parkinsons disease. As in Parkinsons disease neural activity becomes pathologically correlated, we investigated the impact of correlations in basal ganglia output on the transmission of motor signals using a Hodgkin-Huxley model of thalamocortical neurons. We found that such correlations disrupt the transmission of motor signals via rebound spikes by decreasing the signal-to-noise ratio and increasing the trial-to-trial variability. We further examined the role of sensory responses in basal ganglia output neurons and the effect of cortical excitation of motor thalamus in modulating rebound spiking. Interestingly, both could either promote or suppress the generation of rebound spikes depending on their timing relative to the motor signal. Finally, we determined parameter regimes, such as levels of excitation, under which rebound spiking is feasible in the model, and confirmed that the conditions for rebound spiking are primarily given in pathological regimes. However, we also identified specific conditions in the model that would allow rebound spiking to occur in healthy animals in a small subset of thalamic neurons. Overall, our model provides novel insights into differences between normal and pathological transmission of motor signals.

neuroscience↗