Training Very Deep Networks via Residual Learning with Stochastic Input Shortcut Connections

Many works have posited the benefit of depth in deep networks. However, one of the problems encountered in the training of very deep networks is feature reuse; that is, features are ’diluted’ as they are forward propagated through the model. Hence, later network layers receive less informative signals about the input data, consequently making training less effective. In this work, we address the problem of feature reuse by taking inspiration from an earlier work which employed residual learning for alleviating the problem of feature reuse. We propose a modification of residual learning for training very deep networks to realize improved generalization performance; for this, we allow stochastic shortcut connections of identity mappings from the input to hidden layers.We perform extensive experiments using the USPS and MNIST datasets. On the USPS dataset, we achieve an error rate of 2.69% without employing any form of data augmentation (or manipulation). On the MNIST dataset, we reach a comparable state-of-the-art error rate of 0.52%. Particularly, these results are achieved without employing any explicit regularization technique

Anti-angiogenic alternatives to VEGF blockade

Angiogenesis is a major requirement for tumour formation and development. Anti-angiogenic treatments aim to starve the tumour of nutrients and oxygen and also guard against metastasis. The main anti-angiogenic agents to date have focused on blocking the pro-angiogenic vascular endothelial growth factors (VEGFs). While this approach has seen some success and has provided a proof of principle that such anti-angiogenic agents can be used as treatment, the overall outcome of VEGF blockade has been somewhat disappointing. There is a current need for new strategies in inhibiting tumour angiogenesis; this article will review current and historical examples in blocking various membrane receptors and components of the extracellular matrix important in angiogenesis. Targeting these newly discovered pro-angiogenic proteins could provide novel strategies for cancer therapy

Extending the effects of spike-timing-dependent plasticity to behavioral timescales

Activity-dependent modification of synaptic strengths due to spike-timing-dependent plasticity (STDP) is sensitive to correlations between pre- and postsynaptic firing over timescales of tens of milliseconds. Temporal associations typically encountered in behavioral tasks involve times on the order of seconds. To relate the learning of such temporal associations to STDP, we must account for this large discrepancy in timescales. We show that the gap between synaptic and behavioral timescales can be bridged if the stimuli being associated generate sustained responses that vary appropriately in time. Synapses between neurons that fire this way can be modified by STDP in a manner that depends on the temporal ordering of events separated by several seconds even though the underlying plasticity has a much smaller temporal window