Greg Hood

Network anatomy and in vivo physiology of visual cortical neurons

In the cerebral cortex, local circuits consist of tens of thousands of neurons, each of which makes thousands of synaptic connections. Perhaps the biggest impediment to understanding these networks is that we have no wiring diagrams of their interconnections. Even if we had a partial or complete wiring diagram, however, understanding the network would also require information about each neurons function. Here we show that the relationship between structure and function can be studied in the cortex with a combination of in vivo physiology and network anatomy. We used two-photon calcium imaging to characterize a functional property—the preferred stimulus orientation—of a group of neurons in the mouse primary visual cortex. Large-scale electron microscopy of serial thin sections was then used to trace a portion of these neurons’ local network. Consistent with a prediction from recent physiological experiments, inhibitory interneurons received convergent anatomical input from nearby excitatory neurons with a broad range of preferred orientations, although weak biases could not be rejected.

Anatomy and function of an excitatory network in the visual cortex

Circuits in the cerebral cortex consist of thousands of neurons connected by millions of synapses. A precise understanding of these local networks requires relating circuit activity with the underlying network structure. For pyramidal cells in superficial mouse visual cortex (V1), a consensus is emerging that neurons with similar visual response properties excite each other, but the anatomical basis of this recurrent synaptic network is unknown. Here we combined physiological imaging and large-scale electron microscopy to study an excitatory network in V1. We found that layer 2/3 neurons organized into subnetworks defined by anatomical connectivity, with more connections within than between groups. More specifically, we found that pyramidal neurons with similar orientation selectivity preferentially formed synapses with each other, despite the fact that axons and dendrites of all orientation selectivities pass near (<5 μm) each other with roughly equal probability. Therefore, we predict that mechanisms of functionally specific connectivity take place at the length scale of spines. Neurons with similar orientation tuning formed larger synapses, potentially enhancing the net effect of synaptic specificity. With the ability to study thousands of connections in a single circuit, functional connectomics is proving a powerful method to uncover the organizational logic of cortical networks.

Online Analysis of Functional MRI Datasets on Parallel Platforms

We describe a new capability for analyzing and visualizing brain activity while a subject is performing a cognitive or perceptual task in a magnetic resonance scanner. This online capability integrates geographically distributed hardware (scanner, parallel computer, visualization platform) via commodity networking. We describe how we parallelized the existing analysis software and present results for the three main classes of parallel platforms. Finally we discuss some of the new possibilities this online capability presents for scientific studies and clinical intervention.

NEOSIM: Portable large-scale plug and play modelling☆

NEOSIM is a new simulation framework addressed at building large scale and detailed models of the nervous system. Its essence is a set of interfaces and protocols that enable a plug and play architecture for incorporating existing simulation modules such as NEURON [4] and GENESIS [1] as well as future visualisation and data analysis modules. From the start it has been designed to exploit parallel and distributed computers to reduce simulation run times to manageable levels, without the additional modelling e!ort required for earlier publicly-available parallel simulation tools. In this paper, we present the design of the NEOSIM framework, and discuss its applicability to a range of modelling studies. 2001 Published by Elsevier Science B.V.

Parallel genesis for large-scale modeling

Simulations of computational models are limited in size and speed by the power and capacity of the computational platform. We have parallelized the GENESIS object-oriented neural simulator [1] for networked workstations, multiprocessors and massively parallel supercomputers. These can provide two orders of magnitude increase in the size of the models that can be effectively simulated. As larger models are partitioned across many processors, interprocessor communication can limit the effective speedup obtainable. This suggests two classes of problems that may benefit most from parallel simulation: parameter searching and network models.

Large-Scale Simulation Using Parallel GENESIS

PGENESIS is a parallel form of GENESIS that enables simulation of very large models. Simulation models are critical for integration of behavioral data with anatomical and physiological data. Although explanations of behavioral data are possible without resort to neural simulation models (Chomsky 1957, e.g.), those integrative accounts that make contact with the anatomical and physiological data require large-scale simulation models at the neural level. The scale of the models required can be seen in theories about the function of the hippocampus in learning and memory (McClelland and Goddard 1996, Levy 1996). These theories assert that statistical properties of firing rates, synaptic transmission efficiencies, and connection structures are crucial in explaining information processing in the hippocampus. The validity of these statistical properties is conditioned on sufficient sample sizes that cannot hold if the model scales down the real system by more than one or two orders of magnitude. Even scaling down by two orders of magnitude leaves us with very large models that, as we shall see, go beyond the capabilities of existing simulation environments.

Cost-effectiveness of Injectable Preexposure Prophylaxis for HIV Prevention in South Africa

BACKGROUND Long-acting injectable antiretrovirals such as rilpivirine (RPV) could promote adherence to preexposure prophylaxis (PrEP) for human immunodeficiency virus (HIV) prevention. However, the cost-effectiveness of injectable PrEP is unclear. METHODS We constructed a dynamic model of the heterosexual HIV epidemic in KwaZulu-Natal, South Africa, and analyzed scenarios of RPV PrEP scale-up for combination HIV prevention in comparison with a reference scenario without PrEP. We estimated new HIV infections, life-years and costs, and incremental cost-effectiveness ratios (ICERs), over 10-year and lifetime horizons, assuming a societal perspective. RESULTS Compared with no PrEP, unprioritized scale-up of RVP PrEP covering 2.5%-15% of adults prevented up to 9% of new infections over 10 years. HIV prevention doubled (17%) when the same coverage was prioritized to 20- to 29-year-old women, costing

NeuroML for plug and play neuronal modeling

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Deciphering the Effects of Injectable Pre-exposure Prophylaxis for Combination Human Immunodeficiency Virus Prevention

19 213 per infection prevented. Prioritization of PrEP to 80% of individuals at highest behavioral risk achieved comparable prevention (4%-8%) at <1% overall coverage, costing

Large scale simulations of hippocampal-neocortical interactions in a parallel version of genesis

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