Plasticity of the nervous system is dependent on mechanisms that regulate the strength of synaptic transmission. Excitatory synapses in the brain undergo long-term potentiation (LTP) and long-term depression (LTD), cellular models of learning and memory. Protein phosphorylation is required for the induction of many forms of synaptic plasticity, including LTP and LTD. However, the critical kinase substrates that mediate plasticity have not been identified. We previously reported that phosphorylation of the GluR1 subunit of AMPA receptors, which mediate rapid excitatory transmission in the brain, is modulated during LTP and LTD. To test if GluR1 phosphorylation is necessary for plasticity and learning and memory, we generated mice with knockin mutations in the GluR1 phosphorylation sites. The phosphomutant mice show deficits in LTD and LTP and have memory defects in spatial learning tasks. These results demonstrate that phosphorylation of GluR1 is critical for LTD and LTP expression and the retention of memories.

Abstract. In this paper, we propose Local Binary Pattern Histogram Fourier features (LBP-HF), a novel rotation invariant image descriptor computed from discrete Fourier transforms of local binary pattern (LBP) histograms. Unlike most other histogram based invariant texture descriptors which normalize rotation locally, the proposed invariants are constructed globally for the whole region to be described. In addition to being rotation invariant, the LBP-HF features retain the highly discriminative nature of LBP histograms. In the experiments, it is shown that these features outperform non-invariant and earlier version of rotation invariant LBP and the MR8 descriptor in texture classification, material categorization and face recognition tests.

Inspired by Weber's Law, this paper proposes a simple, yet very powerful and robust local descriptor, called the Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends not only on the change of a stimulus (such as sound, lighting) but also on the original intensity of the stimulus. Specifically, WLD consists of two components: differential excitation and orientation. The differential excitation component is a function of the ratio between two terms: One is the relative intensity differences of a current pixel against its neighbors, the other is the intensity of the current pixel. The orientation component is the gradient orientation of the current pixel. For a given image, we use the two components to construct a concatenated WLD histogram. Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT). In addition, experimental results on human face detection also show a promising performance comparable to the best known results on the MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set.

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