Scott Gorlin

‘Prakash’ in Sanskrit means light. The goal of Project Prakash is to bring light into the lives of curably blind children and, in so doing, illuminate some of the most fundamental scientific questions about how the brain develops and learns to see.

Understanding how the human brain learns to perceive objects is one of the fundamental challenges in neuroscience. The dominant approach for studying object learning involves experiments with infants. This work has yielded valuable results, but the operational difficulties of working with babies limit the complexity of studies one can conduct.

Project Prakash allows us to adopt a powerful complementary approach. The Prakash initiative is beginning to create a remarkable population of children across a wide age-range who are just setting out on the enterprise of learning how to see. We have begun following the development of visual skills in these unique children to gain insights into fundamental questions regarding object learning and brain plasticity. A particular strength of this approach is that it affords us an opportunity to continuously follow the development of visual skills and associated neural markers from before the sight restoration treatment to after.

PyMVPA is a Python module intended to ease pattern classification analyses of large datasets. In the neuroimaging contexts such analysis techniques are also known as decoding or MVPA analysis. PyMVPA provides high-level abstraction of typical processing steps and a number of implementations of some popular algorithms. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run.

Although a backup of my repository is available here, it is highly recommended that you checkout the full project at www.pymvpa.org.

V1 is an integrated Matlab solution for visual stimulus presentation, data acquisition, and behavioral experiment control. It was primarily created to replicate the functionality of programs such as Cortex, by the Salk Institute, in the much more accessible Matlab environment (hence, V1 is like a ‘Matlab Cortex’). The primary advantages of V1 include:

• Visual stimulus presentation based on the much-used, open source, and well supported Psychtoolbox (www.psychtoolbox.org).
• Data acquisition relying on Mathwork’s Data Acquisition Toolbox, allowing integrated, flexible, realtime data acquisition dependent only on your hardware.
• Experimental control within Matlab, allowing for experimentation and data analysis within the same package – whether post hoc or in realtime.

V1, simply, is a wrapper class and helper functions that facilitate the goals of experimental control while relying on highly established, standard Matlab extensions to provide the greatest control and functionality any experiment might require.

It is publicly available for download from the V1.git repository.  Dependencies: Matlab 7.6+ (earlier versions require the older class model, which is in the development history, but is not recommended), PTB, and optionally the DAQ toolbox.  Installation: add the root directory to your Matlab path, as well as the ‘helpers’, ‘localMachine’ and optionally ‘experiments’ subdirectories.  For HUD functionality you must add the HUD.jar to your Java classpath as noted in java/v1Java.m.  Once installed, start by typing ‘help V1′, follow the instructions to configure your local machine, and begin experimenting!

RISE (Random Image Sequence Evolution) – One of the least understood aspects of mammalian vision is the ability to recognize scenes through significant degradations in image quality. Neural receptive fields have traditionally been described with coherent structures – for example, oriented gratings in V1. However, this does not address how neurons respond to noisy, less coherent visual input, which is arguably more prevalent in the natural world. Previous studies with natural images show that recognition is highly non-linear with respect to noise, and more importantly, that recognition in noise is facilitated by prior experience with the stimuli (Sadr and Sinha, 2004). We extend these studies by using RISE sequences (Random Image Structure Evolution) to present structured images evolving from noise in fMRI, intrinsic optical imaging, and electrophysiological paradigms. Specifically, the direction of RISE evolution – ascending or descending in information content – allows us to control for low-level image features, such as luminance, while trending towards or away from a neuron’s experimentally defined preferred stimulus.  Any difference in response to ascending and descending stimuli thus reflects prior knowledge facilitating neural recognition in noise.  In line with previous behavioral studies, we present evidence for this hysteretic facilitation throughout the visual hierarchy.  Furthermore, we show a graded signature of hysteresis from V1 through IT, suggesting that prior knowledge affects lower and higher visual areas in different ways. With Jitendra Sharma, Hiroki Sugihara, Mriganka Sur, and Pawan Sinha.

Nitric Oxide - Nitric oxide (NO) has recently generated an explosion of interest as it acts as a novel type of neurotransmitter; unlike conventional neurotransmitters, which are released from one neuron to another in a predefined synaptic pathway, NO is produced on demand and theoretically can diffuse between neurons to affect signaling in the surrounding area.  These properties suggest that NO plays a role in widespread signaling, and has specifically been implicated in the light/dark adaptation process in the vertebrate retina.  Though NO synthase (NOS), the producer of NO, is present in all major retinal cell types, the actual production and behavior of NO in vivo has yet to be fully characterized.  By loading retinas with the fluorescent NO-binding dye diaminofluorescein (DAF), NO induced fluorescence (NO-IF) can be imaged in real time.  This study used real-time imaging techniques in turtle (Pseudemys scripta elegans) retinal slices to characterize the production and behavior of NO.  Though specific stimulation patterns could not be replicated or isolated due to methodological constraints, it was found that NO-IF is produced in two discrete kinetic profiles that are not correlated with cell types, and that, contrary to theory, extracellular NO-IF diffusion is highly restrained, or nonexistent.