Differential medial temporal lobe morphometric predictors of item- and relational-encoded memories in healthy individuals and in individuals with mild cognitive impairment and Alzheimer's disease.

INTRODUCTION:Episodic memory processes are supported by different subregions of the medial temporal lobe (MTL). In contrast to a unitary model of memory recognition supported solely by the hippocampus, a current model suggests that item encoding engages perirhinal cortex, whereas relational encoding engages parahippocampal cortex and the hippocampus. However, this model has not been examined in the context of aging, neurodegeneration, and MTL morphometrics. METHODS:Forty-four healthy subjects (HSs) and 18 cognitively impaired subjects (nine mild cognitive impairment [MCI] and nine Alzheimer's disease [AD] patients) were assessed with the relational and item-specific encoding task (RISE) and underwent 3T magnetic resonance imaging. The RISE assessed the differential contribution of relational and item-specific memory. FreeSurfer was used to obtain measures of cortical thickness of MTL regions and hippocampus volume. RESULTS:Memory accuracies for both item and relational memory were significantly better in the HS group than in the MCI/AD group. In MCI/AD group, relational memory was disproportionately impaired. In HSs, hierarchical regressions demonstrated that memory was predicted by perirhinal thickness after item encoding, and by hippocampus volume after relational encoding (both at trend level) and significantly by parahippocampal thickness at associative recognition. The same brain morphometry profiles predicted memory accuracy in MCI/AD, although more robustly perirhinal thickness for item encoding (R2 = 0.31) and hippocampal volume and parahippocampal thickness for relational encoding (R2 = 0.31). DISCUSSION:Our results supported a model of episodic memory in which item-specific encoding was associated with greater perirhinal cortical thickness, while relational encoding was associated with parahippocampal thickness and hippocampus volume. We identified these relationships not only in HSs but also in individuals with MCI and AD. In the subjects with cognitive impairment, reductions in hippocampal volume and impairments in relational memory were especially prominent

Dense Associative Memory for Pattern Recognition

A model of associative memory is studied, which stores and reliably retrieves many more patterns than the number of neurons in the network. We propose a simple duality between this dense associative memory and neural networks commonly used in deep learning. On the associative memory side of this duality, a family of models that smoothly interpolates between two limiting cases can be constructed. One limit is referred to as the feature-matching mode of pattern recognition, and the other one as the prototype regime. On the deep learning side of the duality, this family corresponds to feedforward neural networks with one hidden layer and various activation functions, which transmit the activities of the visible neurons to the hidden layer. This family of activation functions includes logistics, rectified linear units, and rectified polynomials of higher degrees. The proposed duality makes it possible to apply energy-based intuition from associative memory to analyze computational properties of neural networks with unusual activation functions - the higher rectified polynomials which until now have not been used in deep learning. The utility of the dense memories is illustrated for two test cases: the logical gate XOR and the recognition of handwritten digits from the MNIST data set.Comment: Accepted for publication at NIPS 201

A Specialized Processor for Track Reconstruction at the LHC Crossing Rate

We present the results of an R&D study of a specialized processor capable of precisely reconstructing events with hundreds of charged-particle tracks in pixel detectors at 40 MHz, thus suitable for processing LHC events at the full crossing frequency. For this purpose we design and test a massively parallel pattern-recognition algorithm, inspired by studies of the processing of visual images by the brain as it happens in nature. We find that high-quality tracking in large detectors is possible with sub-$\mu$s latencies when this algorithm is implemented in modern, high-speed, high-bandwidth FPGA devices. This opens a possibility of making track reconstruction happen transparently as part of the detector readout.Comment: Presented by G.Punzi at the conference on "Instrumentation for Colliding Beam Physics" (INSTR14), 24 Feb to 1 Mar 2014, Novosibirsk, Russia. Submitted to JINST proceeding