RESUMO
Information is encoded in neural networks through changes in synaptic weights. Synaptic learning rules involve a combination of rapid Hebbian plasticity and slower homeostatic synaptic plasticity that regulates neuronal activity through global synaptic scaling. Hebbian and homeostatic plasticity have been extensively investigated, whereas much less is known about their interaction. Here we investigated structural and functional consequences of homeostatic plasticity at dendritic spines of mouse hippocampal neurons. We found that prolonged activity blockade induced spine growth, paralleling synaptic strength increases. Following activity blockade, glutamate uncaging-mediated stimulation at single spines led to size-dependent structural potentiation: smaller spines underwent robust growth, whereas larger spines remained unchanged. Moreover, spines near the stimulated spine exhibited volume changes following homeostatic plasticity, indicating that there was a breakdown of input specificity following homeostatic plasticity. Overall, these findings demonstrate that Hebbian and homeostatic plasticity interact to shape neural connectivity through non-uniform structural plasticity at inputs.
RESUMO
BACKGROUND: Neuronal morphology and function are highly coupled. In particular, dendritic spine morphology is strongly governed by the incoming neuronal activity. The first step towards understanding the structure-function relationships is to classify spine shapes into the main spine types suggested in the literature. Due to the lack of reliable automated analysis tools, classification is mostly performed manually, which is a time-intensive task and prone to subjectivity. NEW METHOD: We propose an automated method to classify dendritic spines using shape and appearance features based on challenging two-photon laser scanning microscopy (2PLSM) data. Disjunctive Normal Shape Models (DNSM) is a recently proposed parametric shape representation. We perform segmentation of spine images by applying DNSM and use the resulting representation as shape features. Furthermore, we use Histogram of oriented gradients (HOG) to extract appearance features. In this context, we propose a kernel density estimation (KDE) based framework for dendritic spine classification, which uses these shape and appearance features. RESULTS: Our shape and appearance features based approach combined with Neural Network (NN) correctly classifies 87.06% of spines on a dataset of 456 spines. COMPARISON WITH EXISTING METHODS: Our proposed method outperforms standard morphological feature based approaches. Our KDE based framework also enables neuroscientists to analyze the separability of spine shape classes in the likelihood ratio space, which leads to further insights about nature of the spine shape analysis problem. CONCLUSIONS: Results validate that performance of our proposed approach is comparable to a human expert. It also enable neuroscientists to study shape statistics in the likelihood ratio space.
