Combined local search strategy for learning in networks of binary synapses

Learning in networks of binary synapses is known to be an NP-complete problem. A combined stochastic local search strategy in the synaptic weight space is constructed to further improve the learning performance of a single random walker. We apply two correlated random walkers guided by their Hamming distance and associated energy costs (the number of unlearned patterns) to learn a same large set of patterns. Each walker first learns a small part of the whole pattern set (partially different for both walkers but with the same amount of patterns) and then both walkers explore their respective weight spaces cooperatively to find a solution to classify the whole pattern set correctly. The desired solutions locate at the common parts of weight spaces explored by these two walkers. The efficiency of this combined strategy is supported by our extensive numerical simulations and the typical Hamming distance as well as energy cost is estimated by an annealed computation.Comment: 7 pages, 4 figures, figures and references adde

Counting solutions from finite samplings

We formulate the solution counting problem within the framework of inverse Ising problem and use fast belief propagation equations to estimate the entropy whose value provides an estimate on the true one. We test this idea on both diluted models (random 2-SAT and 3-SAT problems) and fully-connected model (binary perceptron), and show that when the constraint density is small, this estimate can be very close to the true value. The information stored by the salamander retina under the natural movie stimuli can also be estimated and our result is consistent with that obtained by Monte Carlo method. Of particular significance is sizes of other metastable states for this real neuronal network are predicted.Comment: 9 pages, 4 figures and 1 table, further discussions adde

Tfb5 interacts with Tfb2 and facilitates nucleotide excision repair in yeast

TFIIH is indispensable for nucleotide excision repair (NER) and RNA polymerase II transcription. Its tenth subunit was recently discovered in yeast as Tfb5. Unlike other TFIIH subunits, Tfb5 is not essential for cell survival. We have analyzed the role of Tfb5 in NER. NER was deficient in the tfb5 deletion mutant cell extracts, and was specifically complemented by purified Tfb5 protein. In contrast to the extreme ultraviolet (UV) sensitivity of rad14 mutant cells that lack any NER activity, tfb5 deletion mutant cells were moderately sensitive to UV radiation, resembling that of the tfb1-101 mutant cells in which TFIIH activity is compromised but not eliminated. Thus, Tfb5 protein directly participates in NER and is an accessory NER protein that stimulates the repair to the proficient level. Lacking a DNA binding activity, Tfb5 was found to interact with the core TFIIH subunit Tfb2, but not with other NER proteins. The Tfb5–Tfb2 interaction was correlated with the cellular NER function of Tfb5, supporting the functional importance of this interaction. Our results led to a model in which Tfb5 acts as an architectural stabilizer conferring structural rigidity to the core TFIIH such that the complex is maintained in its functional architecture