University College London

London, Greater London, United Kingdom

Departments View all

Institute of Child Health
10,663
Total Impact Points
370
Members
Institute of Neurology
10,678
Total Impact Points
188
Members
Department of Computer Science
827
Total Impact Points
167
Members

Publication History View all

  • [Show abstract] [Hide abstract]
    ABSTRACT: Hippocampal place neurons not only represent current location, but fire in sequences that appear to simulate past and future spatial trajectories. A recent study has found that the firing sequences match the structure of a complex maze, suggesting that the structure of the environment is encoded by the place system, perhaps to aid navigational planning.
    07/2014; 24(14):R643–R645.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Differential cell death is a common feature of aging and age-related disease. In the retina, 30% of rod photoreceptors are lost over life in humans and rodents. However, studies have failed to show age-related cell death in mouse cone photoreceptors, which is surprising because cone physiological function declines with age. Moreover in human, differential loss of short wavelength cone function is an aspect of age-related retinal disease. Here, cones are examined in young (3-month-old) and aged (12-month-old) C57 mice and also in complement factor H knock out mice (CFH-/-) that have been proposed as a murine model of age-related macular degeneration. In vivo imaging showed significant age-related reductions in outer retinal thickness in both groups over this period. Immunostaining for opsins revealed a specific significant decline of >20% for the medium/long (M/L)-wavelength cones but only in the periphery. S cones numbers were not significantly affected by age. This differential cell loss was backed up with quantitative real-time polymerase chain reaction for the 2 opsins, again showing S opsin was unaffected, but that M/L opsin was reduced particularly in CFH-/- mice. These results demonstrate aged cone loss, but surprisingly, in both genotypes, it is only significant in the peripheral ventral retina and focused on the M/L population and not S cones. We speculate that there may be fundamental differences in differential cone loss between human and mouse that may question the validity of mouse models of human outer retinal aging and pathology.
    Neurobiology of aging. 05/2014;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Data assimilation is a fundamental issue that arises across many scales in neuroscience - ranging from the study of single neurons using single electrode recordings to the interaction of thousands of neurons using fMRI. Data assimilation involves inverting a generative model that can not only explain observed data but also generate predictions. Typically, the model is inverted or fitted using conventional tools of (convex) optimization that invariably extremise some functional - norms, minimum descriptive length, variational free energy, etc. Generally, optimisation rests on evaluating the local gradients of the functional to be optimised. In this paper, we compare three different gradient estimation techniques that could be used for extremising any functional in time - (i) finite differences, (ii) forward sensitivities and a method based on (iii) the adjoint of the dynamical system. We demonstrate that the first-order gradients of a dynamical system, linear or non-linear, can be computed most efficiently using the adjoint method. This is particularly true for systems where the number of parameters is greater than the number of states. For such systems, integrating several sensitivity equations - as required with forward sensitivities - proves to be most expensive, while finite-difference approximations have an intermediate efficiency. In the context of neuroimaging, adjoint based inversion of dynamical causal models (DCMs) can, in principle, enable the study of models with large numbers of nodes and parameters.
    NeuroImage 04/2014;

Information

  • Address
    Gower Street, WC1E 6BT , London, Greater London, United Kingdom
  • Head of Institution
    Malcolm John Grant, CBE
  • Website
    http://www.ucl.ac.uk/
  • Phone
    +44 (0) 20 7679 2000
Information provided on this web page is aggregated encyclopedic and bibliographical information relating to the named institution. Information provided is not approved by the institution itself. The institution’s logo (and/or other graphical identification, such as a coat of arms) is used only to identify the institution in a nominal way. Under certain jurisdictions it may be property of the institution.

6539 Members View all

View all

Top publications last week by downloads

 
Costello, A. and Abbas, M. and Allen, A. and Ball, S. and Bell, S. and Bellamy, R. and Friel, S. and Groce, N. and Johnson, A. and Kett, M. and Lee, M. and Levy, C. and Maslin, M....
183 Downloads
 
Water Research 07/2007; 41(11):2301-24.
128 Downloads

Top Collaborating Institutions

Collaborations

This map visualizes which other institutions researchers from University College London have collaborated with.

Rg score distribution

See how the RG Scores of researchers from University College London are distributed.