Chronic stress disrupts neural coherence between cortico-limbic structures

Chronic stress impairs cognitive function, namely on tasks that rely on the integrity of cortico-limbic networks. To unravel the functional impact of progressive stress in cortico-limbic networks we measured neural activity and spectral coherences between the ventral hippocampus (vHIP) and the medial prefrontal cortex (mPFC) in rats subjected to short term stress (STS) and chronic unpredictable stress (CUS). CUS exposure consistently disrupted the spectral coherence between both areas for a wide range of frequencies, whereas STS exposure failed to trigger such effect. The chronic stress-induced coherence decrease correlated inversely with the vHIP power spectrum, but not with the mPFC power spectrum, which supports the view that hippocampal dysfunction is the primary event after stress exposure. Importantly, we additionally show that the variations in vHIP-to-mPFC coherence and power spectrum in the vHIP correlated with stress-induced behavioral deficits in a spatial reference memory task. Altogether, these findings result in an innovative readout to measure, and follow, the functional events that underlie the stress-induced reference memory impairments.

Program slicing by calculation

Program slicing is a well known family of techniques used to identify code fragments which depend on or are depended upon specific program entities. They are particularly useful in the areas of reverse engineering, program understanding, testing and software maintenance. Most slicing methods, usually oriented towards the imperative or object paradigms, are based on some sort of graph structure representing program dependencies. Slicing techniques amount, therefore, to (sophisticated) graph transversal algorithms. This paper proposes a completely different approach to the slicing problem for functional programs. Instead of extracting program information to build an underlying dependencies’ structure, we resort to standard program calculation strategies, based on the so-called Bird-Meertens formalism. The slicing criterion is specified either as a projection or a hiding function which, once composed with the original program, leads to the identification of the intended slice. Going through a number of examples, the paper suggests this approach may be an interesting, even if not completely general, alternative to slicing functional programs

Slicing functional programs by calculation

Program slicing is a well known family of techniques used to identify code fragments which depend on or are depended upon specific program entities. They are particularly useful in the areas of reverse engineering, program understanding, testing and software maintenance. Most slicing methods, usually targeting either the imperative or the object oriented paradigms, are based on some sort of graph structure representing program dependencies. Slicing techniques amount, therefore, to (sophisticated) graph transversal algorithms. This paper proposes a completely different approach to the slicing problem for functional programs. Instead of extracting program information to build an underlying dependencies’ structure, we resort to standard program calculation strategies, based on the so-called Bird- Meertens formalism. The slicing criterion is specified either as a projection or a hiding function which, once composed with the original program, leads to the identification of the intended slice. Going through a number of examples, the paper suggests this approach may be an interesting, even if not completely general alternative to slicing functional programs

Error bounds for low-rank approximations of the first exponential integral kernel

A hierarchical matrix is an efficient data-sparse representation of a matrix, especially useful for large dimensional problems. It consists of low-rank subblocks leading to low memory requirements as well as inexpensive computational costs. In this work, we discuss the use of the hierarchical matrix technique in the numerical solution of a large scale eigenvalue problem arising from a finite rank discretization of an integral operator. The operator is of convolution type, it is defined through the first exponential-integral function and, hence, it is weakly singular. We develop analytical expressions for the approximate degenerate kernels and deduce error upper bounds for these approximations. Some computational results illustrating the efficiency and robustness of the approach are presented.