Faculty Bibliography

To investigate the existence and the characteristics of possible cortical operating points of the primary visual cortex, as manifested by the coherent spontaneous ongoing activity revealed by real-time optical imaging based on voltage-sensitive dyes, we studied numerically a very large-scale ( approximately 5 x 10(5)) conductance-based, integrate-and-fire neuronal network model of an approximately 16-mm(2) patch of 64 orientation hypercolumns, which incorporates both isotropic local couplings and lateral orientation-specific long-range connections with a slow NMDA component. A dynamic scenario of an intermittent desuppressed state (IDS) is identified in the computational model, which is a dynamic state of (i) high conductance, (ii) strong inhibition, and (iii) large fluctuations that arise from intermittent spiking events that are strongly correlated in time as well as in orientation domains, with the correlation time of the fluctuations controlled by the NMDA decay time scale. Our simulation results demonstrate that the IDS state captures numerically many aspects of experimental observation related to spontaneous ongoing activity, and the specific network mechanism of the IDS may suggest cortical mechanisms and the cortical operating point underlying observed spontaneous activity

To address computational 'scale-up' issues in modeling large regions of the cortex, many coarse-graining procedures have been invoked to obtain effective descriptions of neuronal network dynamics. However, because of local averaging in space and time, these methods do not contain detailed spike information and, thus, cannot be used to investigate, e.g., cortical mechanisms that are encoded through detailed spike-timing statistics. To retain high-order statistical information of spikes, we develop a hybrid theoretical framework that embeds a subnetwork of point neurons within, and fully interacting with, a coarse-grained network of dynamical background. We use a newly developed kinetic theory for the description of the coarse-grained background, in combination with a Poisson spike reconstruction procedure to ensure that our method applies to the fluctuation-driven regime as well as to the mean-driven regime. This embedded-network approach is verified to be dynamically accurate and numerically efficient. As an example, we use this embedded representation to construct 'reverse-time correlations' as spiked-triggered averages in a ring model of orientation-tuning dynamics

A coarse-grained representation of neuronal network dynamics is developed in terms of kinetic equations, which are derived by a moment closure, directly from the original large-scale integrate-and-fire (I&F) network. This powerful kinetic theory captures the full dynamic range of neuronal networks, from the mean-driven limit (a limit such as the number of neurons N --> infinity, in which the fluctuations vanish) to the fluctuation-dominated limit (such as in small N networks). Comparison with full numerical simulations of the original I&F network establishes that the reduced dynamics is very accurate and numerically efficient over all dynamic ranges. Both analytical insights and scale-up of numerical representation can be achieved by this kinetic approach. Here, the theory is illustrated by a study of the dynamical properties of networks of various architectures, including excitatory and inhibitory neurons of both simple and complex type, which exhibit rich dynamic phenomena, such as, transitions to bistability and hysteresis, even in the presence of large fluctuations. The implication for possible connections between the structure of the bifurcations and the behavior of complex cells is discussed. Finally, I&F networks and kinetic theory are used to discuss orientation selectivity of complex cells for 'ring-model' architectures that characterize changes in the response of neurons located from near 'orientation pinwheel centers' to far from them

We explain how simple and complex cells arise in a large-scale neuronal network model of the primary visual cortex of the macaque. Our model consists of approximately 4000 integrate-and-fire, conductance-based point neurons, representing the cells in a small, 1-mm(2) patch of an input layer of the primary visual cortex. In the model the local connections are isotropic and nonspecific, and convergent input from the lateral geniculate nucleus confers cortical cells with orientation and spatial phase preference. The balance between lateral connections and lateral geniculate nucleus drive determines whether individual neurons in this recurrent circuit are simple or complex. The model reproduces qualitatively the experimentally observed distributions of both extracellular and intracellular measures of simple and complex response

Proteoglycans are cell surface and extracellular matrix molecules to which long, unbranched glycosaminoglycan side chains are attached. Heparan sulphate, a type of glycosaminoglycan chain, has been proposed as a co-factor necessary for signalling by a range of growth factors. Here we provide evidence that loss of 2-O-sulphation in heparan sulphate leads to a significant reduction in cell proliferation in the developing cerebral cortex. The gene encoding heparan sulphate 2-sulphotransferase (Hs2st) is expressed in embryonic cortex and histological analysis of mice homozygous for a null mutation in Hs2st indicated a reduction in the thickness of the embryonic cerebral cortex. Using 5'-bromodeoxyuridine (BrdU) incorporation assays we found a reduction of approximately 40% in labelling indices of cortical precursor cells at E12. Comparison of the fates of cortical cells born on E13 and E15 in Hs2st(-/-) mutant and wildtype littermate embryos revealed no differences in the pattern of cell migration. Our findings suggest a critical role for 2-O-sulphation of heparan sulphate proteoglycan (HSPG) in regulating cell proliferation during development of the cerebral cortex, perhaps through the modulation of cellular responses to growth factor signalling

BACKGROUND: The purpose of this work was to develop methods for successful cryopreservation of human oocytes. METHODS: Two cryopreservation procedures were used. Method 1 involved use of 1.5 mol/l propanediol (PrOH)-0.1 mol/l sucrose with medium containing sodium (Na) as cryoprotectant medium, seeding at -7 degrees C, and stepwise dilution of cryoprotectant post-thaw. Method 2 used Na-depleted media with 1.5 mol/l PrOH-0.2 mol/l sucrose for freezing, seeding at -6 degrees C, and use of high sucrose (0.5 and 0.2 mol/l) for cryoprotectant removal. RESULTS: The first method was used in seven patients, and gave poor (12.3%) survival results and no pregnancies. The second method was used in 15 patients (16 cycles), and yielded good survival and fertilization rates (74.4 and 59% respectively), with four pregnancies and five healthy infants born to 11 women receiving an embryo transfer. CONCLUSIONS: Using Na-depleted media along with other alterations in freezing and thawing procedures, human oocyte cryopreservation can provide excellent survival and pregnancy rates

A large-scale computational model of a local patch of input layer 4 [Formula: see text] of the primary visual cortex (V1) of the macaque monkey, together with a coarse-grained reduction of the model, are used to understand potential effects of cortical architecture upon neuronal performance. Both the large-scale point neuron model and its asymptotic reduction are described. The work focuses upon orientation preference and selectivity, and upon the spatial distribution of neuronal responses across the cortical layer. Emphasis is given to the role of cortical architecture (the geometry of synaptic connectivity, of the ordered and disordered structure of input feature maps, and of their interplay) as mechanisms underlying cortical responses within the model. Specifically: (i) Distinct characteristics of model neuronal responses (firing rates and orientation selectivity) as they depend upon the neuron's location within the cortical layer relative to the pinwheel centers of the map of orientation preference; (ii) A time independent (DC) elevation in cortico-cortical conductances within the model, in contrast to a 'push-pull' antagonism between excitation and inhibition; (iii) The use of asymptotic analysis to unveil mechanisms which underly these performances of the model; (iv) A discussion of emerging experimental data. The work illustrates that large-scale scientific computation--coupled together with analytical reduction, mathematical analysis, and experimental data, can provide significant understanding and intuition about the possible mechanisms of cortical response. It also illustrates that the idealization which is a necessary part of theoretical modeling can outline in sharp relief the consequences of differing alternative interpretations and mechanisms--with final arbiter being a body of experimental evidence whose measurements address the consequences of these analyses