Biology subjects

Nold, A.

Publications and source records attributed to Nold, A..

5 recordsLinked to original sources

bioRxiv2021

Reconciling contrast invariance and non-linear computation in cortical circuits

Network selectivity for orientation is invariant to changes in the stimulus contrast in the primary visual cortex. Similarly, the selectivity for odor identity is invariant to changes in odorant concentration in the piriform cortex. Interestingly, invariant network selectivity appears robust to local changes in synaptic strength induced by synaptic plasticity, even though: i) synaptic plasticity can potentiate or depress connections between neurons in a feature-dependent manner, and ii) in networks with balanced excitation and inhibition, synaptic plasticity is a determinant for the network non-linearity. In this study, we investigate whether network contrast invariance is consistent with a variety of synaptic states and connectivities in balanced networks. By using mean-field models and spiking network simulations, we show how the synaptic state controls the non-linearity in the network response to contrast and how it can lead to the emergence of contrast-invariant or contrast-dependent selectivity. Different forms of synaptic plasticity sharpen or broaden the network selectivity, while others do not affect it. Our results explain how the physiology of individual synapses is linked to contrast-invariant selectivity at the network level.

neuroscience↗

bioRxiv2021

Unbiased choice of global clustering parameters in single-molecule localization microscopy

Single-molecule localization microscopy resolves objects below the diffraction limit of light via sparse, stochastic detection of target molecules. Single molecules appear as clustered detection events after image reconstruction. However, identification of clusters of localizations is often complicated by the spatial proximity of target molecules and by background noise. Clustering results of existing algorithms often depend on user-generated training data or user-selected parameters, which can lead to unintentional clustering errors. Here we suggest an unbiased algorithm (FINDER) based on adaptive global parameter selection and demonstrate that the algorithm is robust to noise inclusion and target molecule density. We benchmarked FINDER against the most common density based clustering algorithms in test scenarios based on experimental datasets. We show that FINDER can keep the number of false positive inclusions low while also maintaining a low number of false negative detections in densely populated regions.

bioinformatics↗

bioRxiv2020

Formation and synaptic control of active transient working memory representations

Neural representations of working memory maintain information temporarily and make it accessible for processing. This is most feasible in active, spiking representations. State-of-the-art modeling frameworks, however, reproduce working memory representations that are either transient but non-active or active but non-transient. Here, we analyze a biologically motivated working memory model which shows that synaptic short-term plasticity and noise emerging from spiking networks can jointly produce a working memory representation that is both active and transient. We investigate the effect of a synaptic signaling mechanism whose dysregulation is related to schizophrenia and show how it controls transient working memory duration through presynaptic, astrocytic and postsynaptic elements. Our findings shed light on the computational capabilities of healthy working memory function and offer a possible mechanistic explanation for how molecular alterations observed in psychiatric diseases such as schizophrenia can lead to working memory impairments.

neuroscience↗

bioRxiv2020

The Spatial Scale of Synaptic Protein Allocation during Homeostatic Plasticity

An individual neuron hosts up to 10,000 individual synapses that can be made stronger or weaker by local and cell-wide plasticity mechanisms, both of which require protein synthesis. To address over what spatial scale a neuron allocates synaptic resources, we quantified the distribution of newly synthesized proteins after global homeostatic upscaling using metabolic labeling and single-molecule localization (DNA-PAINT). Following upscaling, we observed a global increase in locally synthesized nascent protein in synapses and at dendrites, with a high degree of variability between individual synapses. We determined the smallest spatial scale over which nascent proteins were evenly distributed and found that it is best described by synaptic neighborhoods (~ 10 microns in length)-smaller than a dendritic branch and larger than an individual synapse. Protein allocation at the level of neighborhoods thus represents a solution to the problem of protein allocation within a neuron that balances local autonomy and global homeostasis.

neuroscience↗

bioRxiv2019

A model of neuron-glia crosstalk explains stages of neural tissue resilience

Glia, the helper cells of the brain, are essential in maintaining neural resilience across time and varying challenges: By reacting to changes in neuronal health glia carefully balance repair or disposal of injured neurons to prevent further tissue damage. Malfunction of these interactions is implicated in many neurodegenerative diseases. Reductionist models with a minimal number of parameters provide the opportunity to gain insight into biological functions and inform experimental designs. We introduce such a model that mimics long-term implications of repair-or-dispose decisions. Depending on the functionality of the decision-making process, the model assumes four distinct tissue states: healthy, challenged, primed tissue at risk of acute damage propagation, and chronic neurodegeneration. These states of the model correspond to the progression stages observed in the most common neurodegenerative conditions. The underlying mechanisms are in agreement with experimental observations of glia-neuron crosstalk and reproduce a homeostatic balance between repairing and damage-inducing reactions. The model suggests that the onset of neurodegeneration results from a tug-of-war between two conflicting goals: short-term resilience to stressors vs long-term prevention of tissue damage.

systems biology↗