Biology subjects

Scholl, B.

Publications and source records attributed to Scholl, B..

12 recordsLinked to original sources

bioRxiv2025

Fluorescence lifetime-based biosensor for monitoring compartmentalized autophagy dynamics in the intact mammalian brain

Autophagy is a key process in regulation of neuronal development, plasticity, and local metabolism. Yet, autophagy dynamics and regulation within intact neuronal circuits remains poorly understood. Here, we developed a pH sensitive fluorescence lifetime-based imaging method which allows to monitor autophagy dynamics in the living mouse brain. This approach allowed us to uncover compartmentalized autophagic activity across soma, dendrites, and axons of layer 2/3 cortical neurons. We found pronounced differences in dendritic dynamics of autophagic vesicles in vivo, where distal dendrites showed elevated vesicle motility compared to proximal dendrites. Notably, sensory experience modulated dendritic autophagy dynamics in the somatosensory cortex. We further combined in vivo autophagy imaging with disease associated genetic perturbation to uncover novel autophagy related phenotypes. Altogether, this approach highlights the spatial and functional complexity of autophagy in the intact mammalian brain and establishes a framework for investigating its role in synaptic regulation during development, plasticity, and aging.

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bioRxiv2025

Single-cell perturbations reveal adaptive, stimulus-dependent recurrence in visual cortex

Cortical circuits are proposed to amplify weak sensory inputs but transition to feature competition during strong drive. However, the evidence for this is scarce in cortices with a functional, modular organization, such as in primates and carnivores, where neighboring excitatory and inhibitory cells share feature selectivity. Here, we demonstrate that networks in ferret primary visual cortex switch between amplification and competition depending on sensory input. Combining cellular perturbations and statistical modeling, we uncovered broad suppressive influence modulated by visual contrast. At low contrast, functionally-coupled cells exhibited mutual amplification, switching to suppression at high contrast. This reversal emerged in recurrent network models in a Cross-Dominant regime, where inhibitory-excitatory coupling exceeds excitatory-excitatory coupling. These models predicted strong suppression from inhibitory cells, confirmed with cell-type-specific perturbations. Our results provide direct evidence that cortical recurrence with functional, modular organization toggles between amplification and suppression, supporting long-standing predictions from predictions from theory and models of visual cortex.

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bioRxiv2025

Activation of CP-AMPARs is required for homosynaptic and heterosynaptic structural LTP in the hippocampus

Long-term potentiation (LTP) involves alterations in synaptic structure that are believed to underlie the persistent increase in synaptic efficacy. Here we compared structural LTP (sLTP) in EGFP-labelled spines with functional LTP, using field potential recording, at CA3-CA1 synapses in mouse hippocampal slices for [~] 2 h following theta-burst stimulation (TBS). Activity-dependent labelling with FM4-64 allowed us to compare activated (FM+) and non-activated synapses and thereby compare homo- and hetero-synaptic sLTP. In addition, we related spine volume changes according to the probability of release, P(r), of activated synapses. At homosynaptic sites there was the expected NMDA receptor (NMDAR)-dependent potentiation of spine volume that persisted throughout the recording period. We found that this sLTP also required the synaptic activation of CP-AMPARs. There was also sLTP at heterosynaptic sites that, surprisingly, developed more quickly than the associated homosynaptic sLTP. This heterosynaptic sLTP was also dependent on the synaptic activation of both NMDARs and CP-AMPARs. Additionally, we observed a trans- and hetero-synaptic interaction, whereby the heterosynaptic spines grew according to the P(r) of the neighbouring active (homosynaptic) synapse. These observations have therefore advanced our understanding of sLTP in several ways; the first demonstration of the absolute requirement for the synaptic activation of CP-AMPARs for sLTP; the magnitude of heterosynaptic sLTP relative to homosynaptic sLTP and the hitherto unexpected combination of trans- and hetero-synaptic interactions.

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bioRxiv2023

Astrocyte coverage of excitatory synapses correlates to measures of synapse structure and function in primary visual cortex

Most excitatory synapses in the mammalian brain are contacted by astrocytes, forming the tripartite synapse. This interface is thought to be critical for glutamate turnover and structural or functional dynamics of synapses. While the degree of synaptic contact of astrocytes is known to vary across brain regions and animal species, the implications of this variability remain unknown. Furthermore, precisely how astrocyte coverage of synapses relates to in vivo functional properties of individual dendritic spines has yet to be investigated. Here, we characterized perisynaptic astrocyte processes (PAPs) contacting synapses of pyramidal neurons of the ferret visual cortex and, using correlative light and electron microscopy, examined their relationship to synaptic strength and to sensory-evoked Ca2+ activity. Nearly all synapses were contacted by PAPs, and most were contacted along the axon-spine interface (ASI). Structurally, we found that the degree of PAP coverage scaled with synapse size and complexity. Functionally, we found that PAP coverage scaled with the selectivity of Ca2+ responses of individual synapses to visual stimuli and, at least for the largest synapses, scaled with the reliability of visual stimuli to evoke postsynaptic Ca2+ events. Our study shows astrocyte coverage is highly correlated with structural properties of excitatory synapses in the visual cortex and implicates astrocytes as a contributor to reliable sensory activation.

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bioRxiv2023

Postsynaptic mitochondria are positioned to support functional diversity of dendritic spines

Postsynaptic mitochondria are critical to the development, plasticity, and maintenance of synaptic inputs. However, their relationship to synaptic structure and functional activity is unknown. We examined a correlative dataset from ferret visual cortex with in vivo two-photon calcium imaging of dendritic spines during visual stimulation and electron microscopy (EM) reconstructions of spine ultrastructure, investigating mitochondrial abundance near functionally- and structurally-characterized spines. Surprisingly, we found no correlation to structural measures of synaptic strength. Instead, we found that mitochondria are positioned near spines with orientation preferences that are dissimilar to the somatic preference. Additionally, we found that mitochondria are positioned near groups of spines with heterogeneous orientation preferences. For a subset of spines with mitochondrion in the head or neck, synapses were larger and exhibited greater selectivity to visual stimuli than those without a mitochondrion. Our data suggest mitochondria are not necessarily positioned to support the energy needs of strong spines, but rather support the structurally and functionally diverse inputs innervating the basal dendrites of cortical neurons.

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bioRxiv2022

Synaptic weights that correlate with presynaptic selectivity increase decoding performance

The activity of neurons in the visual cortex is often characterized by tuning curves, which are thought to be shaped by Hebbian plasticity during development and sensory experience. This leads to the prediction that neural circuits should be organized such that neurons with similar functional preference are connected with stronger weights. In support of this idea, previous experimental and theoretical work have provided evidence for a model of the visual cortex characterized by such functional subnetworks. A recent experimental study, however, have found that the postsynaptic preferred stimulus was defined by the total number of spines activated by a given stimulus and independent of their individual strength. While this result might seem to contradict previous literature, there are many factors that define how a given synaptic input influences postsynaptic selectivity. Here, we designed a computational model in which postsynaptic functional preference is defined by the number of inputs activated by a given stimulus. Using a plasticity rule where synaptic weights tend to correlate with presynaptic selectivity, and is independent of functional-similarity between pre- and postsynaptic activity, we find that this model can be used to decode presented stimuli in a manner that is comparable to maximum likelihood inference.

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bioRxiv2021

Efficient decoding of large-scale neural population responses with Gaussian-process multiclass regression

Neural decoding methods provide a powerful tool for quantifying the information content of neural population codes and the limits imposed by correlations in neural activity. However, standard decoding methods are prone to overfitting and scale poorly to high-dimensional settings. Here, we introduce a novel decoding method to overcome these limitations. Our approach, the Gaussian process multi-class decoder (GPMD), is well-suited to decoding a continuous low-dimensional variable from high-dimensional population activity, and provides a platform for assessing the importance of correlations in neural population codes. The GPMD is a multinomial logistic regression model with a Gaussian process prior over the decoding weights. The prior includes hyperparameters that govern the smoothness of each neurons decoding weights, allowing automatic pruning of uninformative neurons during inference. We provide a variational inference method for fitting the GPMD to data, which scales to hundreds or thousands of neurons and performs well even in datasets with more neurons than trials. We apply the GPMD to recordings from primary visual cortex in three different species: monkey, ferret, and mouse. Our decoder achieves state-of-the-art accuracy on all three datasets, and substantially outperforms independent Bayesian decoding, showing that knowledge of the correlation structure is essential for optimal decoding in all three species.

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bioRxiv2021

A binocular synaptic network supports interocular response alignment in visual cortical neurons

In the visual system, signals from the two eyes are combined to form a coherent representation through the convergence of synaptic input populations onto individual cortical neurons. As individual synapses originate from either monocular (representing one eye) or binocular (representing both eyes) cortical networks, it has been unclear how these inputs are integrated coherently. Here, we imaged dendritic spines on layer 2/3 binocular cells in ferret visual cortex with in vivo two-photon microscopy to examine how monocular and binocular synaptic networks contribute to the interocular alignment of orientation tuning. We found that binocular synapses varied in degree of congruency, namely response correlation between left and right eye visual stimulation. Binocular congruent inputs were functionally distinct from binocular noncongruent and monocular inputs, exhibiting greater tuning selectivity and connection specificity. Using correlative light and electron microscopy, we found no difference in ultrastructural anatomy and instead, observed strength in numbers using a simple model simulating aggregate synaptic input. This model demonstrated a predominate contribution of binocular congruent inputs in sculpting somatic orientation preference and interocular response alignment. Our study suggests that, in layer 2/3 cortical neurons, a binocular network is responsible for forming a coherent representation in individual neurons through recurrent intracortical interactions.

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bioRxiv2021

Perception of soft materials relies on physics-based object representations: Behavioral and computational evidence

When encountering objects, we readily perceive not only low-level properties (e.g., color and orientation), but also seemingly higher-level ones - including aspects of physics (e.g., mass). Perhaps nowhere is this contrast more salient than in the perception of soft materials such as cloths: the dynamics of these objects (including how their three-dimensional forms vary) are determined by their physical properties such as stiffness, elasticity, and mass. Here we hypothesize that the perception of cloths and their physical properties must involve not only image statistics, but also abstract object representations that incorporate "intuitive physics". We provide behavioral and computational evidence for this hypothesis. We find that humans can visually match the stiffness of cloths with unfamiliar textures from the way they undergo natural transformations (e.g. flapping in the wind) across different scenarios. A computational model that casts cloth perception as mental physics simulation explains important aspects of this behavior.

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bioRxiv2020

Cortical synaptic diversity naturally arises with heterogeneous neural populations

The synaptic inputs to single cortical neurons exhibit substantial diversity in their sensory-driven activity. What this diversity reflects is unclear, and appears counter-productive in generating selective somatic responses to specific stimuli. We propose that synaptic diversity arises because neurons decode information from upstream populations. Focusing on a single sensory variable, orientation, we construct a probabilistic decoder that estimates the stimulus orientation from the responses of a realistic, hypothetical input population of neurons. We provide a straightforward mapping from the decoder weights to real excitatory synapses, and find that optimal decoding requires diverse input weights. Analytically derived weights exhibit diversity whenever upstream input populations consist of noisy, correlated, and heterogeneous neurons, as is typically found in vivo. In fact, in silico weight diversity was necessary to accurately decode orientation and matched the functional heterogeneity of dendritic spines imaged in vivo. Our results indicate that synaptic diversity is a necessary component of information transmission and reframes studies of connectivity through the lens of probabilistic population codes. These results suggest that the mapping from synaptic inputs to somatic selectivity may not be directly interpretable without considering input covariance and highlights the importance of population codes in pursuit of the cortical connectome.

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bioRxiv2019

Cortical neuron response selectivity derives from strength in numbers of synapses

Single neocortical neurons are driven by populations of excitatory inputs, forming the basis of neural selectivity to features of sensory input. Excitatory connections are thought to mature during development through activity-dependent Hebbian plasticity1, whereby similarity between presynaptic and postsynaptic activity selectively strengthens some synapses and weakens others2. Evidence in support of this process ranges from measurements of synaptic ultrastructure to slice and in vivo physiology and imaging studies3,4,5,6,7,8. These corroborating lines of evidence lead to the prediction that a small number of strong synaptic inputs drive neural selectivity, while weak synaptic inputs are less correlated with the functional properties of somatic output and act to modulate activity overall6,7. Supporting evidence from cortical circuits, however, has been limited to measurements of neighboring, connected cell pairs, raising the question of whether this prediction holds for the full profile of synapses converging onto cortical neurons. Here we measure the strengths of functionally characterized excitatory inputs contacting single pyramidal neurons in ferret primary visual cortex (V1) by combining in vivo two-photon synaptic imaging and post hoc electron microscopy (EM). Using EM reconstruction of individual synapses as a metric of strength, we find no evidence that strong synapses play a predominant role in the selectivity of cortical neuron responses to visual stimuli. Instead, selectivity appears to arise from the total number of synapses activated by different stimuli. Moreover, spatial clustering of co-active inputs, thought to amplify synaptic drive, appears reserved for weaker synapses, further enhancing the contribution of the large number of weak synapses to somatic response. Our results challenge the role of Hebbian mechanisms in shaping neuronal selectivity in cortical circuits, and suggest that selectivity reflects the co-activation of large populations of presynaptic neurons with similar properties and a mixture of strengths.

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bioRxiv2017

Stability, affinity and chromatic variants of the glutamate sensor iGluSnFR

Single-wavelength fluorescent reporters allow visualization of specific neurotransmitters with high spatial and temporal resolution. We report variants of the glutamate sensor iGluSnFR that are functionally brighter; can detect sub-micromolar to millimolar concentrations of glutamate; and have blue, green or yellow emission profiles. These variants allow in vivo imaging where original-iGluSnFR was too dim, reveal glutamate transients at individual spine heads, and permit kilohertz imaging with inexpensive, powerful fiber lasers.

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