Computational neuroscience is a subfield of neuroscience that develops models to integrate complex experimental data in order to understand brain function. To constrain and test computational models, researchers need access to a wide variety of experimental data. Much of those data are not readily accessible because neuroscientists fall into separate communities that study the brain at different levels and have not been motivated to provide data to researchers outside their community. To foster sharing of neuroscience data, a workshop was held in 2007, bringing together experimental and theoretical neuroscientists, computer scientists, legal experts and governmental observers. Computational neuroscience was recommended as an ideal field for focusing data sharing, and specific methods, strategies and policies were suggested for achieving it. A new funding area in the NSF/NIH Collaborative Research in Computational Neuroscience (CRCNS) program has been established to support data sharing, guided in part by the workshop recommendations. The new funding area is dedicated to the dissemination of high quality data sets with maximum scientific value for computational neuroscience. The first round of the CRCNS data sharing program supports the preparation of data sets which will be publicly available in 2008. These include electrophysiology and behavioral (eye movement) data described towards the end of this article.

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A model is presented which accounts for many characteristic response properties used to classify anuran ganglion cell types while being consistent with data concerning interneurons. In the model color is ignored and input stimuli are assumed to be only black and white at high contrast. We show that accurate ganglion cell responses are obtained even with simplified receptors and horizontal cells: Receptors are modeled as responding with a step change, while horizontal cells respond only to global changes in intensity brought about by full field illumination changes. A hyperpolarizing and depolarizing bipolar cell are generated by subtracting local receptor and horizontal potentials. Two transient amacrine cells (On and Off) are generated using a high-pass filter like mechanism with a thresholded output which responds to positive going changes in the corresponding bipolar cell potentials. The model shows how a selective combination of bipolar and amacrine channels can account for many of the response properties used to classify the anuran ganglion cell types (class-0 through 4) and makes several experimental predictions.

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A wide range of experimental data characterizing properties of individual salamander retinal cells and synaptic interactions are integrated to form a quantitative computational model of visual function in the salamander retina. The model is used to show how specific interactions between neurons and between networks of neurons can lead-to the integrated response behavior of individual cells deep in the retina. The model is also used to illustrate how the representation of moving and stationary stimuli is encoded in a series of layer-by-layer transformations leading to the final retinal output at the ganglion cell layer.