A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing

: Neurobiological systems continually interact with the surrounding environment to refine their behaviour toward the best possible reward. Achieving such learning by experience is one of the main challenges of artificial intelligence, but currently it is hindered by the lack of hardware capable of plastic adaptation. Here, we propose a bio-inspired recurrent neural network, mastered by a digital system on chip with resistive-switching synaptic arrays of memory devices, which exploits homeostatic Hebbian learning for improved efficiency. All the results are discussed experimentally and theoretically, proposing a conceptual framework for benchmarking the main outcomes in terms of accuracy and resilience. To test the proposed architecture for reinforcement learning tasks, we study the autonomous exploration of continually evolving environments and verify the results for the Mars rover navigation. We also show that, compared to conventional deep learning techniques, our in-memory hardware has the potential to achieve a significant boost in speed and power-saving

Transcriptional landscape of the human and fly genomes: Nonlinear and multifunctional modular model of transcriptomes

Regions of the genome not coding for proteins or not involved in cis-acting regulatory activities are frequently viewed as lacking in functional value. However, a number of recent large-scale studies have revealed significant regulated transcription of unannotated portions of a variety of plant and animal genomes, allowing a new appreciation of the widespread transcription of large portions of the genome. High-resolution mapping of the sites of transcription of the human and fly genomes has provided an alternative picture of the extent and organization of transcription and has offered insights for biological functions of some of the newly identified unannotated transcripts. Considerable portions of the unannotated transcription observed are developmental or cell-type-specific parts of protein-coding transcripts, often serving as novel, alternative 5′ transcriptional start sites. These distal 5′ portions are often situated at significant distances from the annotated gene and alternatively join with or ignore portions of other intervening genes to comprise novel unannotated protein-coding transcripts. These data support an interlaced model of the genome in which many regions serve multifunctional purposes and are highly modular in their utilization. This model illustrates the underappreciated organizational complexity of the genome and one of the functional roles of transcription from unannotated portions of the genome. Copyright 2006, Cold Spring Harbor Laboratory Press © 2006 Cold Spring Harbor Laboratory Press

Haplotypes versus genotypes on pedigrees

Abstract. Genome sequencing will soon produce haplotype data for individuals. For pedigrees of related individuals, sequencing appears to be an attractive alternative to genotyping. However, methods for pedigree analysis with haplotype data have not yet been developed, and the computational complexity of such problems has been an open question. Furthermore, it is not clear in which scenarios haplotype data would provide better estimates than genotype data for quantities such as recombination rates. To answer these questions, a reduction is given from genotype problem instances to haplotype problem instances, and it is shown that solving the haplotype problem yields the solution to the genotype problem, up to constant factors or coefficients. The pedigree analysis problems we will consider are the likelihood, maximum probability haplotype, and minimum recombination haplotype problems. Two algorithms are introduced: an exponential-time hidden Markov model (HMM) for haplotype data where some individuals are untyped, and a linear-time algorithm for pedigrees having haplotype data for all individuals. Recombination estimates from the general haplotype HMM algorithm are compared to recombination estimates produced by a genotype HMM. Having haplotype data on all individuals produces better estimates. However, having several untyped individuals can drastically reduce the utility of haplotype data. Pedigree analysis, both linkage and association studies, has a long history of important contributions to genetics, including disease-gene finding and some of the first genetic maps for humans. Recent contributions include fine-scale recombination maps in humans [4], regions linked to Schizophrenia that might be missed by genome-wide association studies [11], and insights into the relationship between cystic fibrosis and fertility [13]. Algorithms for pedigree problems are of great interest to the computer science community, in part because of connections to machine learning algorithms, optimization methods, and combinatorics [7, 16

On Counting the Number of Consistent Genotype Assignments for Pedigrees

Consistency checking of genotype information in pedigrees plays an important role in genetic analysis and for complex pedigrees the computational complexity is critical. We present here a detailed complexity analysis for the problem of counting the number of complete consistent genotype assignments. Our main result is a polynomial time algorithm for counting the number of complete consistent assignments for non-looping pedigrees. We further classify pedigrees according to a number of natural parameters like the number of generations, the number of children per individual and the cardinality of the set of alleles. We show that even if we assume all these parameters as bounded by reasonably small constants, the counting problem becomes computationally hard (#P-complete) for looping pedigrees. The border line for counting problems computable in polynomial time (i.e. belonging to the class FP) and #P-hard problems is completed by showing that even for general pedigrees with unlimited number of generations and alleles but with at most one child per individual and for pedigrees with at most two generations and two children per individual the counting problem is in FP

Application of Evolutionary Algorithms to Protein Folding Prediction

. The aim of this paper is to show how evolutionary algorithms can be applied to protein folding prediction. We start reviewing previous similar approaches, that we criticize emphasizing the key issue of representation. A new evolutionary algorithm is described, based on the notion of distance matrix representation, together with a software package that implements it. Finally, experimental results are discussed. 1 Protein folding prediction Proteins are molecules of extraordinary relevance for live beings. They are chains of aminoacids (called also residues) that assume very rich and involved shapes in vivo. The prediction of protein tertiary structure (3D shape) from primary structure (sequence of aminoacids) is a daunting as well as a fundamental task in molecular biology. A large amount of experimental data is available as far as it concerns sequence, and large projects are creating huge amounts of sequence data. But to infer the biological function (the ultimate goal for molecula..

Processing of instrumentation tracks via Recurrent Neural Networks

We outline the capabilities of the paradigm of the Recurrent Neural Networks in processing dynamical signal, coming for instance from continuous measurement instrumentation. The key idea is to compare these tracks with the evolution through time of the state vector of a neural network trained to simulate the process grounding the above signal. The training procedure is described and some examples are supplied. 1. Introduction. Let us consider the signal of fig: 1 which rises from a sinusoid y(t)=A . sin(wt+j) affected by Fig: 1 An example of noised signal random noise both on the frequency w , on the phase j and on the amplitude A, so that the actual process is z(t)= A . sin((w+e 1 )t+j+e 2 )+e 3 . Let our problem be to estimate w, j, A on the basis of the sampled signal. In case that the e 1 , e 2 , e 3 are white noise processes and we are given a periodic sampling of the signal , a good strategy is to attribute to these parameters the values which minimize the mean square error betwe..

A Hybrid Symbolic Subsymbolic Controller for Complex Dynamical Systems

: We present a general procedure for controlling complex dynamical systems according to cost functions sampled along time trajectories. The procedure merges the dynamics of the system with those of a recurrent neural network. The key idea is to use the known part of the symbolic description of the system to both design the lay-out of the neural network and write down the analytical expression of the rules for training it. We use as a benchmark the control of a flexible arm which carries a payload along a horizontal circular trajectory with a perfect damping of the vibration at the stop. The hybrid system is trained to i) rotate the arm by any assigned angle within a wide range tightly tracking a reference trajectory, ii) identify the payload mass at the beginning of the motion and iii) carry out this job with high velocity. Numerical results on a simulated plant denote that these targets are achieved both with a high accuracy and with a great robustness in relation to modelization inad..

Primary sclerosing cholangitis in patient with celiac disease complicated by cholecystic empyema and acute pancreatitis

Background. The association of celiac disease and sclerosing cholangitis is a well known, although unusual, pathologic feature of autoimmunity. Methods. A 64 year old patient presenting with sub-acute cholangitis and pancreatitis, treated with cholecystectomy and endoscopic sphincterotomy. The post-operative course, complicated by cholestatic jaundice, and subsequent clinical complications are described, showing how the diagnosis of sclerosing cholangitis was outlined after the Endoscopic Retrograde Cholangio-Pancreatography (ERCP) and confirmed by liver biopsy. Long term treatment with Ursodeoxycholic acid has gradually normalized bilirubin values, while cholestasis enzymes are gradually decreasing. After 18 months bleeding from oesophageal varices ensued, which was controlled through endoscopic ligation. Conclusions. The diagnosis of primary sclerosing cholangitis should be taken into account when cholangitis is associated with other immunity derangements and segmentary dilatations of the intra-hepatic bile ducts, but no dilatation of the main bile duct is noticed at imaging or endoscopy. Recovery of hepatic function should be always attempted before bringing the patient to surgery, in order to avoid postoperative hepatic decompensation

Neural Cooperating Procedure for Fractal Analysis of Artificial Drawings

We assess a procedure for discovering the mixing ratios of transformations within the bench of affine transformations of a Probabilistic Iterated Fuzzy Functions System which gives rise to artificial 256 colors drawings. A first hypothesis on these ratios is obtained from a pseudo-ergodic estimate of the probabilities of matching special paths during the image generation. The job of the neural network is to remove biases from this hypothesis, after a proper training. The reconstructed images are nearly identical to their original ones also on instances not used to train the network and their representation through the above ratios gains a very high compression rate . 1. Introduction We deal with colored artificial drawings, such as in the figure 1 coming from the implementation of the following routine : [FUZZYCHAOS] DATA: n1,n2= size of the image in pixels IMAGEc 0,0 c 0,n1-1 ; ;c n2-1,0 c n2-1,n1-1 p1 ..p N : probability of calling a pair W,j VARIABLE: pixel= 0,0 color = init c 1,1 ...