Short-term plasticity (STP) represents a key neuronal mechanism of information processing. In excitatory hippocampal synapses, STP serves as a high-pass filter optimized for the transmission of information-carrying place-field discharges. This STP filter enables synapses to perform a highly nonlinear, switch-like operation permitting the passage and amplification of signals with place-field-like characteristics. Because of the complexity of interactions among STP processes, the synaptic mechanisms underlying this filtering paradigm remain poorly understood. Here, we describe a simple mechanistic model of STP, derived in large part from basic principles of synaptic function, that reproduces this highly nonlinear synaptic behavior. The model, formulated in terms of release probability, considers the interactions between calcium-dependent forms of presynaptic enhancement and their impact on vesicle pool dynamics, which is described using a two-pool model of vesicle recruitment. By considering the interdependency between release probability and various forms of STP, the model attempts to provide a realistic coupling among major presynaptic processes. The model parameters are first determined using synaptic dynamics during constant-frequency stimulation. The model then accurately reproduces all major characteristics of the synaptic filtering paradigm during natural stimulus patterns without free parameters. An elimination approach is then used to identify the contribution of each STP component to synaptic dynamics. Based on this analysis, the model predicts strong calcium dependence of synaptic filtering properties, which is verified experimentally in rat hippocampal slices. This simple model may thus offer a useful framework to further investigate the role of STP in neural computations.

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Several recent advancements in the field of texture analysis prompt some fundamental questions. For instance, what is the true impact of these novel advancements under real-world environments? When do these novel advancements fail to perform? Which methods perform better and under what conditions? In this work, we investigate these and other issues under nonideal image acquisition environments, specifically, environments with changing conditions due to illumination variations and those caused by both affine and nonaffine transformations. We study the performance of nine popular texture analysis algorithms using three different datasets, with varying levels of difficulty. Experiments are performed on nonideal texture datasets under five different setups. We find that most state-of-the-art techniques do not perform well under these conditions. To a large extent, their performance under nonideal conditions depends critically on the nature of the textural surface. Moreover, most techniques fail to perform reliably when the number of classes in the dataset is increased significantly, over the regular-size datasets used in previous work. Multiscale features performed reasonably well against variations caused by illumination and rotation but are prone to fail under changes in scale. Surprisingly, the performance for most of the algorithms is generally stable on structured or periodic textures, even with variations in illumination or affine transformations.

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