Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines, a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. Synaptic sampling machines perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate & fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based synaptic sampling machines outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware

Feeding congregation of ribbonfish, Trichiurus lepturus juveniles in oceanic waters and their targeted exploitation by deepsea trawlers along the Tuticorin coast in Gulf of Mannar

Ribbonfishes supported commercial fishery at Tuticorin prior to nineteen nineties. But reports suggested that their stock and fishery declined considerably thereafter. Their fishery during 2000- 2006 was represented by occasional landings of small quantity of ribbonfish by trawls. Average production by trawls at Tuticorin fishing harbour during the period was 250 t. They represent about 0.7% of the total fish catch of the period

मान्नार की खाडी में टूटिकोरिन तट पर सागरी जलक्षेत्रों में फीतामीन ट्राइक्यूरस लेप्टुयूरस किशोरों का जमाव और गभीर सागर आनायकों द्वारा इसका लक्षित विदोहन

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Pressure dependent electronic properties of MgO polymorphs: A first-principles study of Compton profiles and autocorrelation functions

The first-principles periodic linear combination of atomic orbitals method within the framework of density functional theory implemented in the CRYSTAL06 code has been applied to explore effect of pressure on the Compton profiles and autocorrelation functions of MgO. Calculations are performed for the B1, B2, B3, B4, B8_1 and h-MgO polymorphs of MgO to compute lattice constants and bulk moduli. The isothermal enthalpy calculations predict that B4 to B8_1, h-MgO to B8_1, B3 to B2, B4 to B2 and h-MgO to B2 transitions take place at 2, 9, 37, 42 and 64 GPa respectively. The high pressure transitions B8_1 to B2 and B1 to B2 are found to occur at 340 and 410 GPa respectively. The pressure dependent changes are observed largely in the valence electrons Compton profiles whereas core profiles are almost independent of the pressure in all MgO polymorphs. Increase in pressure results in broadening of the valence Compton profiles. The principal maxima in the second derivative of Compton profiles shifts towards high momentum side in all structures. Reorganization of momentum density in the B1 to B2 structural phase transition is seen in the first and second derivatives before and after the transition pressure. Features of the autocorrelation functions shift towards lower r side with increment in pressure.Comment: 19 pages, 8 figures, accepted for publication in Journal of Materials Scienc