An Efficient Simulation Environment for Modeling Large-Scale Cortical Processing

We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. It uses a standard network construction interface. The simulator allows for execution on either GPUs or CPUs. The simulator, which is written in C/C++, allows for both fine grain and coarse grain specificity of a host of parameters. We demonstrate the ease of use and computational efficiency of this model by implementing a large-scale model of cortical areas V1, V4, and area MT. The complete model, which has 138,240 neurons and approximately 30 million synapses, runs in real-time on an off-the-shelf GPU. The simulator source code, as well as the source code for the cortical model examples is publicly available

An Efficient Simulation Environment for Modeling Large-Scale Cortical Processing

We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. It uses a standard network construction interface. The simulator allows for execution on either GPUs or CPUs. The simulator, which is written in C/C++, allows for both fine grain and coarse grain specificity of a host of parameters. We demonstrate the ease of use and computational efficiency of this model by implementing a large-scale model of cortical areas V1, V4, and area MT. The complete model, which has 138,240 neurons and approximately 30 million synapses, runs in real-time on an off-the-shelf GPU. The simulator source code, as well as the source code for the cortical model examples is publicly available

Classical and surround receptive field structure in cortical area MT as revealed by reverse correlation

This dissertation is organized into two main parts: physiology and new techniques. The physiology is organized into classical receptive fields, surround properties and global object selectivity of area MT. The first two physiology chapters used a novel sparse motion stimulus to drive MT cells and used reverse correlation to analyze the data. For the classical receptive field the spike triggered average was used as an estimate of the field. For the surround, maximally informative dimension analysis as originally developed by Sharpee at el. (2004) and further optimized for the stimulus distribution was used in these studies (see Chapter 5). Chapter 6 covers a circular statistic derived for use in Chapter 2. Using a new motion reverse correlation technique, we obtained high -resolution measurements of receptive field properties - spatial and directional selectivity - within cortical visual area MT. The standard, often implicit, view of these receptive fields is that they cover a contiguous area of the visual field and that the preferred direction is homogenous across this field. Contrary to this, we found that the receptive fields of many (19%) neurons were significantly patchy and that only half (48%) preferred only a single direction of motion within their receptive field. Both the spatial patchiness and directional preference variation were validated in single cells using more traditional stimuli. In the second chapter we used a variant of Maximally Informative Dimension (MID; Sharpee et al. 2004; Chapter 5) in order to find mechanisms that are not directly implied by the Spike Triggered Average (STA). Using MID we found a second kernel (receptive field, RF2) that interacted with the STA. We found 3 categories for RF2 structure : Surround Suppression, Off Preferred Direction Suppression and Omni-modulation. A divisive nonlinearity is found to fit the surround and off preferred direction suppression data well. In the third chapter we studied the response of area MT to a motion capture illusion. We first demonstrated a large psychophysical effect for humans, and then tested area MT's response to this stimulus. We found no modulation in area MT consistent with the global object motion, i.e. consistent with the illusio

An efficient simulation environment for modeling large-scale cortical processing.

We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. It uses a standard network construction interface. The simulator allows for execution on either GPUs or CPUs. The simulator, which is written in C/C++, allows for both fine grain and coarse grain specificity of a host of parameters. We demonstrate the ease of use and computational efficiency of this model by implementing a large-scale model of cortical areas V1, V4, and area MT. The complete model, which has 138,240 neurons and approximately 30 million synapses, runs in real-time on an off-the-shelf GPU. The simulator source code, as well as the source code for the cortical model examples is publicly available