Biology subjects

Schmid, D.

Publications and source records attributed to Schmid, D..

4 recordsLinked to original sources

bioRxiv2026

Coherence from Context - Two-Point Neuron Models for Contextual Integration in Visual Information Processing

The integration of contextual information is crucial for coherent perception and cognition. The morphology and conductance properties of cortical pyramidal cells suggest that they operate as "two-point neurons" (TPNs), asymmetrically combining driving sensory input at basal dendrites with modulating context at apical compartments. We present a mechanistic computational TPN model that captures the causal cell-internal apical-basal integration. The model is extended incorporating the interactions between pyramidal cells and local inhibitory interneuron circuits of PV, SOM, and VIP cells. The model rests on guiding principles of asymmetric feedforward-feedback integration, contextual feedback, and pooled inhibition to implement local competition and global cooperation supporting the selective amplification of coherent signals. We validate our approach against detailed multi-compartment pyramidal cell simulations reproducing key electrophysiological phenomena. We then extend it to interacting TPN populations with joint spatial and feature selectivity. In such networks, contextual signals propagate through structured lateral recurrence and top-down feedback, exhibiting contextual integration, coherence formation, and evidence propagation. To support larger-scale network simulations, we derive a reduced mathematical model that preserves the core computational principles of TPNs, while substantially reducing complexity. We demonstrate the models applicability in biological vision, showing how it explains motion integration and incremental grouping--processes requiring dynamic resolution of perceptual ambiguity. Finally, we discuss how the proposed framework connects cellular and circuit-level mechanisms of pyramidal neurons to broader questions about cortical computation, the formation of representations for globally consistent perceptual states, and the potential for embedding TPN principles into artificial neural network architectures. HighlightsO_LIMechanistic TPN model integrates basal driving input and apical context modulation. C_LIO_LILocal inhibitory circuits (PV/SOM/VIP) regulate competition and cooperation. C_LIO_LINetworks of TPN populations exhibit global coherence via context propagation. C_LIO_LIReduced models retain core computations for efficient large-scale vision networks. C_LIO_LILinks pyramidal cell mechanisms to cortical computation and AI models. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/721094v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@18a10b2org.highwire.dtl.DTLVardef@14e33a1org.highwire.dtl.DTLVardef@11f9afborg.highwire.dtl.DTLVardef@1f7dc7e_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

The number of effectors limits the relative sleep gain: Insights from seven finger tapping experiments

The role of sleep in motor memory consolidation is still a matter of ongoing debate. A classic task to investigate mechanisms of motor memory consolidation is the finger tapping task, which reliably shows small effects in performance enhancement after sleep but not after a corresponding wake interval. However, variants of the task with a varying number of effectors (e.g., one hand) failed to demonstrate this effect on motor memory consolidation. Thus, in a series of seven experiments we investigate five variants of the classic finger tapping task in which the number of effectors (1 or 2 hands combined with 1, 2 or 4 fingers) used to perform the task are systematically varied. For the groups, where sleep immediately followed learning, a beneficial effect of sleep in comparison with a corresponding wake interval was found, except for the task variant where the finger tapping task was performed with 1 hand and 1 finger. However, no clear-cut pattern could be identified for the numbers of effectors used to perform the task and the magnitude of offline motor memory consolidation. Furthermore, for groups with an intervening wake interval between learning and sleep no differences between the post-sleep and post-wake gain were observed. HighlightsIn a variation of the classical finger tapping task with one hand and one finger, no sleep-dependent enhancement was found. In all other variations, small to large effects of sleep-dependent offline-gains were found. An interposed wake interval between learning and sleep substantially diminishes the post-sleep enhancement. Motor skill complexity, with respect to the coordination of more than one effector, does partially play a role in predicting sleep-dependent motor memory enhancement.

neuroscience↗

bioRxiv2023

Thalamo-Cortical Interaction for Incremental Binding in Mental Contour-Tracing

Visual object-based attention marks a key process of mammalian perception. By which mechanisms this process is implemented and how it can be interacted with by means of attentional control is not completely understood yet. Incremental binding is a mechanism required in more demanding scenarios of object-based attention and is likewise experimentally investigated quite well. Attention spreads across a representation of the visual object and labels bound elements by constant up-modulation of neural activity. The speed of incremental binding was found to be dependent on the spatial arrangement of distracting elements in the scene and to be scale invariant giving rise to the growth-cone hypothesis. In this work, we propose a neural dynamical model of incremental binding that provides a mechanistic account for these findings. Through simulations, we investigate the model properties and demonstrate how an attentional spreading mechanism tags neurons that participate in the object binding process. They utilize Gestalt properties and eventually show growth-cone characteristics labeling perceptual items by delayed activity enhancement of neuronal firing rates. We discuss the algorithmic process underlying incremental binding and relate it to the models computation. This theoretical investigation encompasses complexity considerations and finds the model to be not only of explanatory value in terms of neurohpysiological evidence, but also to be an efficient implementation of incremental binding striving to establish a normative account. By relating the connectivity motifs of the model to neuroanatomical evidence, we suggest thalamo-cortical interactions to be a likely candidate for the flexible and efficient realization suggested by the model. There, pyramidal cells are proposed to serve as the processors of incremental grouping information. Local bottom-up evidence about stimulus features is integrated via basal dendritic sites. It is combined with an apical signal consisting of contextual grouping information which is gated by attentional task-relevance selection mediated via higher-order thalamic representations. Author SummaryUnderstanding a visual scene requires us to tell apart visual objects from one another. Object-based attention is the process by which mammals achieve this. Mental processing of object components determines whether they are compatible to the overall object and, thus, should be grouped together to be perceived as a whole or not. For complicated objects, this processing needs to happen serially, determining the compatibility step by step. In this work, we propose a neural model of this process and try to answer the question of how it might be implemented in the brain. We test the model on a case of object-based attention for grouping elongated lines and compare it to the available experimental evidence. We additionally show that the model not only explains this evidence, but it does so also by spending neurons and connections efficiently -- a property likewise desirable for brains and machines. Together, these findings suggest which brain areas might be involved in realizing this process and how to reason about the complexity of this computation.

neuroscience↗

bioRxiv2020

Mutational dynamics and transmission properties of SARS-CoV-2 superspreading events in Austria

Superspreading events shape the COVID-19 pandemic. Here we provide a national-scale analysis of SARS-CoV-2 outbreaks in Austria, a country that played a major role for virus transmission across Europe and beyond. Capitalizing on a national epidemiological surveillance system, we performed deep whole-genome sequencing of virus isolates from 576 samples to cover major Austrian SARS-CoV-2 clusters. Our data chart a map of early viral spreading in Europe, including the path from low-frequency mutations to fixation. Detailed epidemiological surveys enabled us to calculate the effective SARS-CoV-2 population bottlenecks during transmission and unveil time-resolved intra-patient viral quasispecies dynamics. This study demonstrates the power of integrating deep viral genome sequencing and epidemiological data to better understand how SARS-CoV-2 spreads through populations. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

microbiology↗