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

BACKGROUND: Although the tendon of the long head of the biceps is a well-known source of shoulder pain, the pathophysiological basis of this pain has yet to be explained. The aim of this study was to detect and characterize any nervous element of the tendon and to determine a possible explanation for pain originating from this structure. METHODS: The nature of the neuronal innervation of the tendon of the long head of the biceps was studied immunohistochemically, in four tendons from different human cadavers, with use of neurofilament antibody 2H3, neurofilament-like antibody 3A10, calcitonin gene-related peptide, substance P, and tyrosine hydroxylase. RESULTS: A large neuronal network, asymmetrically distributed along the length of the tendon with a higher degree of innervation at the tendon origin, was identified by the neurofilament and neurofilament-like antibodies 2H3 and 3A10. This innervation was found to be positive for calcitonin gene-related peptide and substance P, suggesting the presence of thinly myelinated or unmyelinated sensory neurons. It was also positive for tyrosine hydroxylase, suggesting a post-ganglionic sympathetic origin. CONCLUSIONS AND CLINICAL RELEVANCE: These findings demonstrate that the tendon of the long head of the biceps is innervated by a network of sensory sympathetic fibers, which may play a role in the pathogenesis of shoulder pain

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

Using a large-scale model of the Macaque Primary Visual Cortex (V1), we offer an explanation for observed and systematic differences in orientation selectivity of Simple and Complex cells. Our network model represents the activity of 4 adjoining orientation hypercolumns in layer 4Calpha. This model reproduces experimentally observed distributions of spatial summation properties, from both extra-and intra-cellular measurements, with Simple cell behavior observed in neurons dominated by geniculate excitation and Complex cell behavior in neurons dominated by cortical excitation (Tao et al 2003). The two neuronal mechanisms behind these differences underlay two different mechanisms of orientation selectivity. In our model, convergent feedforward input from the lateral geniculate nucleus sets up the orientation preference of an individual Simple neuron. Recurrent cortical connections, isotropic and non-specific, then sharpens its selectivity. The most selective Simple cells are found near orientation pinwheel singularities where the cortical inhibition comes from neurons at different orientation preference and thus is "global" in orientation. In contrast, the most selective Complex cells are found in iso-orientation domains away from the pinwheel center, where the cortical excitation comes from neurons preferring similar orientations. This correlation of selectivity with distance to pinwheel centers can be attributed to differing length-scales of coupling within cortex

Using an idealized network model of the mammalian primary visual cortex, we examine the issue of orientation selectivity in the presence of intrinsic cortical fluctuations. The model, containing both Simple & Complex excitatory and inhibitory neurons, has a ring architecture and captures the essential features of cortical interactions. Within this framework, we show that Simple cells and Complex cells rely on different selectivity mechanisms. Simple cells, dominated by feedforward geniculate excitation, are most selective near orientation pinwheel singularities ---There, their selectivity is facilitated by the cortical inhibition coming from neurons with many different orientation preferences, thus, effectively "global" in orientation. In contrast, Complex cells, whose dynamics is induced by strong recurrent excitation, are most selective away from pinwheel centers, in iso-orientation domains ---where the selectivity is due to the cortical excitation coming from neurons preferring similar orientations. Utilizing the recent kinetic-theoretical approach of Cai et al. (2003), we identify possible tuning mechanisms and their experimental consequences: First, individual circular variances and tuning widths are relatively independent of the contrast of the visual stimulus; Second, the selectivity of individual Complex cell is correlated with its contrast response: the most selective Complex cells have the steepest contrast response function (i.e., its firing rate rises rapidly with stimulus contrast). Both consequences are consistent with available experimental data