Evolving unipolar memristor spiking neural networks

© 2015 Taylor & Francis. Neuromorphic computing – brain-like computing in hardware – typically requires myriad complimentary metal oxide semiconductor spiking neurons interconnected by a dense mesh of nanoscale plastic synapses. Memristors are frequently cited as strong synapse candidates due to their statefulness and potential for low-power implementations. To date, plentiful research has focused on the bipolar memristor synapse, which is capable of incremental weight alterations and can provide adaptive self-organisation under a Hebbian learning scheme. In this paper, we consider the unipolar memristor synapse – a device capable of non-Hebbian switching between only two states (conductive and resistive) through application of a suitable input voltage – and discuss its suitability for neuromorphic systems. A self-adaptive evolutionary process is used to autonomously find highly fit network configurations. Experimentation on two robotics tasks shows that unipolar memristor networks evolve task-solving controllers faster than both bipolar memristor networks and networks containing constant non-plastic connections whilst performing at least comparably

Encoding and retrieval in a CA1 microcircuit model of the hippocampus

Recent years have witnessed a dramatic accumulation of knowledge about the morphological, physiological and molecular characteristics, as well as connectivity and synaptic properties of neurons in the mammalian hippocampus. Despite these advances, very little insight has been gained into the computational function of the different neuronal classes; in particular, the role of the various inhibitory interneurons in encoding and retrieval of information remains elusive. Mathematical and computational models of microcircuits play an instrumental role in exploring microcircuit functions and facilitate the dissection of operations performed by diverse inhibitory interneurons. A model of the CA1 microcircuitry is presented using biophysical representations of its major cell types: pyramidal, basket, axo-axonic, bistratified and oriens lacunosummoleculare cells. Computer simulations explore the biophysical mechanisms by which encoding and retrieval of spatio-temporal input patterns are achieved by the CA1 microcircuitry. The model proposes functional roles for the different classes of inhibitory interneurons in the encoding and retrieval cycles

Two-dimensional electron gas in Zn-polar ZnMgO/ZnO heterostructure grown by metal-organic vapor phase epitaxy

10.1063/1.3489101Applied Physics Letters9711-APPL

Propranolol inhibits the proliferation, migration and tube formation of hemangioma cells through HIF-1α dependent mechanisms

The aim of this study was to investigate the mechanism of propranolol on the regression of hemangiomas. Propranolol-treated hemangioma tissues were collected and the expression of hypoxia inducible factor-1α (HIF-1α) was examined. We also established HIF-1α overexpression and knockdown hemangioma cells, and determined the effects of HIF-1α on the hemangioma cells proliferation, apoptosis, migration and tube formation. Significantly increased HIF-1α level was found in the hemangioma tissues compared to that in normal vascular tissues, whereas propranolol treatment decreased the HIF-1α level in hemangioma tissues in a time- and dose-dependent manner. Moreover, propranolol treatment significantly decreased cell proliferation, migration and tube formation as well as promoted cell apoptosis in HIF-1α overexpression and knockdown hemangioma cells. Propranolol suppressed the cells proliferation, migration and tube formation of hemangioma cells through HIF-1α dependent mechanisms. HIF-1α could serve as a novel target in the treatment of hemangiomas

The development of a fisheries information monitoring system (FIMS) for north east Nigeria

Appendix 4 to CEMARE report no. 43Available from British Library Document Supply Centre-DSC:7755.0105(122) / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Optimal Hebbian Learning: A Probabilistic Point of View

Abstract. Up to now, many activity dependent learning rules have been proposed in order to model long-term potentiation (LTP). Our aim is to derive a spike time dependent learning rule from a probabilistic optimality criterion. The idea is to create a model in order to obtain quantitative results in terms of a learning window. This is done by maximising a given likelihood function with respect to the synaptic weights. The results are consistent with the actual knowledge of back-propagating action potentials