Biology subjects

Schmitt, F. J.

Publications and source records attributed to Schmitt, F. J..

6 recordsLinked to original sources

bioRxiv2026

Developmental Trajectories of Dynamic Brain Network Organization and Their Alteration in Tourette Syndrome

Typical brain network maturation involves an increase in network flexibility and hemispheric specialization. Tourette syndrome (TS) disrupts these trajectories, with tic severity potentially modulating deviations. This study examined theta-band EEG source connectivity states in typically developing children and children with TS. We assessed age-related trajectories and the impact of tic severity using generalized linear modeling, accounting for sex and multiple comparisons. K-means clustering identified four recurrent source connectivity states (A-D), with metrics including Coverage, representing state prevalence (proportion of time spent in each state), Average Dwell Time, an index of state stability (mean duration of stable persistence of each state), and Transition Rate Per Minute, reflecting global network flexibility (frequency of state switches per minute). In healthy controls (HC), typical maturation was characterized by increased left intra-hemisphere connectivity state stability and prevalence, decreased diffuse connectivity state stability, and rising network flexibility. TS patients exhibited deviant trajectories, including age-dependent decreasing global network flexibility across subgroups stratified by tic severity and marginally divergent diffuse activity patterns, with high-severity cases showing increased diffuse connectivity state stability. The normative patterns suggest typical motor development requiring dynamic network reconfiguration and hemispheric specialization, processes that appear altered in TS. TS patients exhibit age-dependent network rigidity across severity subgroups, as reflected by decreased transition rates, alongside severity- modulated network imbalances, indicating that tic disorders disrupt mechanisms of brain network maturation underlying motor control. These findings suggest that atypical trajectories of network stability and flexibility represent a key feature of tic pathophysiology, highlighting the role of altered network dynamics in TS during maturation.

neuroscience↗

bioRxiv2026

Metastable Neural Assemblies on a Wiring-Weight Continuum

Neural population activity typically evolves on low-dimensional manifolds and can be described as trajectories through quasi-stable assembly states. Here we develop a unified definition of clustered neural networks with local excitatory-inhibitory balance in which enhanced within-cluster effective coupling is implemented through connection probability (structural clustering), synaptic efficacy (weight clustering), or any mixture of both. We introduce a single mixing parameter{kappa} [isin] [0, 1] that redistributes a defined cluster contrast between connection probabilities and synaptic efficacies while preserving the mean input of the underlying balanced random network. Mean-field theory and binary-network simulations show that metastable dynamics are supported across the full{kappa} continuum. Varying{kappa} changes higher-order input structure, reshaping multistable regimes, correlation structure, and the balance between single- and multi-cluster episodes. Because real nervous systems jointly organize topology and synaptic strength, our approach provides a biologically interpretable parametrization of clustered assembly models and a basis for future models combining structural and functional plasticity. We further demonstrate metastable switching in spiking leaky integrate-and-fire networks and in a BrainScaleS-2 neuromorphic implementation for the cases of structural, weight, and combined clustering. The{kappa} -framework offers a controlled translation axis for neuromorphic and other constrained substrates, exposing trade-offs between routed synapse count, fan-in, synaptic weight resolution, and calibration when implementing attractor-based computational primitives.

neuroscience↗

bioRxiv2023

Minimal circuit motifs for second-order conditioning in the insect mushroom body

In well-established first-order conditioning experiments, the concurrence of a sensory cue with reinforcement forms an association, allowing the cue to predict future reinforcement. Once a sensory cue is established as a predictor, it can also serve as indirect reinforcement, a phenomenon referred to as second-order conditioning. In the insect mushroom body, such associations are encoded in the plasticity of the synapses between the intrinsic and output neurons of the mushroom body, a process mediated by the activity of dopaminergic neurons that encode reinforcement signals. In second-order conditioning, a new sensory cue is paired with an already established one that presumably activates dopaminergic neurons due to its predictive power of the reinforcement. We explore minimal circuit motifs in the mushroom body for their ability to support second-order conditioning. We found that dopaminergic neurons can either be activated directly by the mushroom bodys intrinsic neurons or via feedback from the output neurons via several pathways. We demonstrate that the circuit motifs differ in their computational efficiency and robustness and suggest a particular motif that relies on feedforward input of the mushroom body intrinsic neurons to dopaminergic neurons as a promising additional candidate for experimental evaluation. It differentiates well between trained and novel stimuli, demonstrating robust performance across a range of model parameters.

neuroscience↗

bioRxiv2022

The mushroom body output encodes behavioral decision during sensory-motor transformation

Animal behavioral decisions are dynamically formed by evaluating momentary sensory evidence on the background of individual experience and the acute motivational state. In insects, the mushroom body (MB) has been implicated in forming associative memories and in assessing the appetitive or aversive valence of sensory stimuli to bias approach versus avoidance behavior. To study the MB involvement in innate feeding behavior we performed extracellular single-unit recordings from MB output neurons (MBONs) while simultaneously monitoring a defined feeding behavior in response to timed odor stimulation in naive cockroaches. All animals expressed the feeding behavior almost exclusively in response to food odors. Likewise, MBON responses were invariably and strongly tuned to the same odors. Importantly, MBON responses were restricted to behaviorally responded trials, which allowed the accurate prediction of the occurrence versus non-occurrence of the feeding behavior in individual trials from the neuronal population activity. During responded trials the neuronal activity generally preceded the onset of the feeding behavior, indicating a causal relation. Our results contest the predominant view that MBONs encode stimulus valence. Rather, we conclude that the MB output dynamically encodes the behavioral decision to inform downstream motor networks.

neuroscience↗

bioRxiv2022

Efficient parameter calibration and real-time simulation of large scale spiking neural networks with GeNN and NEST

Spiking neural networks (SNN) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters is necessary to achieve robust network function and demands high computing power and large memory resources. Special requirements arise from closed-loop model simulation in virtual environments, and from real-time simulation in robotic application. Here, we compare two complementary approaches to efficient large scale and realtime SNN simulation. The widely used NEural Simulation Tool (NEST) parallelizes simulation across multiple CPU cores. The GPU-enhanced Neural Network (GeNN) simulator uses the highly parallel GPU-based architecture to gain simulation speed. We quantify fixed and variable simulation costs on single machines with different hardware configurations. As benchmark model we use a spiking cortical attractor network with a topology of densely connected excitatory and inhibitory neuron clusters with homogeneous or distributed synaptic time constants and in comparison to the random balanced network. We show that simulation time scales linearly with the simulated biological model time and, for large networks, approximately linearly with the model size as dominated by the number of synaptic connections. Additional fixed costs with GeNN are almost independent of model size, while fixed costs with NEST increase linearly with model size. We demonstrate how GeNN can be used for simulating networks with up to 3.5 {middle dot} 106 neurons (> 3 {middle dot} 1012 synapses) on a high-end GPU, and up to 250, 000 neurons (25 {middle dot}109 synapses) on a low-cost GPU. Real-time simulation was achieved for networks with 100, 000 neurons. Network calibration and parameter grid search can be efficiently achieved using batch processing. We discuss the advantages and disadvantages of both approaches for different use cases.

neuroscience↗

bioRxiv2021

Reactive astrocytes prevent maladaptive plasticity after ischemic stroke

Restoration of functional connectivity is a major contributor to functional recovery after stroke. We investigated the role of reactive astrocytes in functional connectivity and recovery after photothrombotic stroke in mice with attenuated reactive gliosis (GFAP-/-Vim-/-). Infarct volume and longitudinal functional connectivity changes were determined by in vivo T2-weighted MRI and resting-state functional MRI. Sensorimotor function was assessed with behavioral tests, and glial and neural plasticity responses were quantified in the peri-infarct region. Four weeks after stroke, GFAP-/-Vim-/- mice showed impaired recovery of sensorimotor function and aberrant restoration of global neuronal connectivity. These mice also exhibited maladaptive plasticity responses, shown by higher number of lost and newly formed functional connections between primary and secondary targets of cortical stroke regions and increased peri-infarct expression of the axonal plasticity marker Gap43. We conclude that reactive astrocytes are required for optimal recovery-promoting plasticity responses after ischemic stroke.

neuroscience↗