With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of

We present a scheme for implementing highly-connected, recon®gurable networks of integrate-and-®re neurons in VLSI. Neural activity is encoded by spikes, where the address of an active neuron is communicated through an asynchronous request and acknowledgement cycle. We employ probabilistic transmission of spikes to implement continuous-valued synaptic weights, and memory-based look-up tables to implement arbitrary interconnection topologies. The scheme is modular and scalable, and lends itself to the implementation of multi-chip network architectures. Results from a prototype system with 1024 analog VLSI integrate-and-®re neurons, each with up to 128 probabilistic synapses, demonstrate these concepts in an image processing task. q

Conventional methods widely available for the analysis of spike trains and related neural data include various time- and frequency-domain analyses, such as peri-event and interspike interval histograms, spectral measures, and probability distributions. Information theoretic methods are increasingly recognized as significant tools for the analysis of spike train data. However, developing robust implementations of these methods can be time-consuming, and determining applicability to neural recordings can require expertise. In order to facilitate more widespread adoption of these informative methods by the neuroscience community, we have developed the Spike Train Analysis Toolkit. STAToolkit is a software package which implements, documents, and guides application of several information-theoretic spike train analysis techniques, thus minimizing the effort needed to adopt and use them. This implementation behaves like a typical Matlab toolbox, but the underlying computations are coded in C for portability, optimized for efficiency, and interfaced with Matlab via the MEX framework. STAToolkit runs on any of three major platforms: Windows, Mac OS, and Linux. The toolkit reads input from files with an easy-to-generate text-based, platform-independent format. STAToolkit, including full documentation and test cases, is freely available open source via http://neuroanalysis.org, maintained as a resource for the computational neuroscience and neuroinformatics communities. Use cases drawn from somatosensory and gustatory neurophysiology, and community use of STAToolkit, demonstrate its utility and scope.

In the visual cortex of the cat and ferret, it is established that maturation of orientation selectivity is shaped by experience-dependent plasticity. However, recent experiments indicate that orientation maps are remarkably stable and experience-independent. We present a model to account for these seemingly paradoxical results. In this model, a scaffold consisting of non-isotropic lateral connections is laid down in horizontal circuitry before visual experience. These lateral connections provide an experience-independent framework for the developing orientation maps by inducing a broad orientation tuning bias in the model neurons. Experience-dependent plasticity of the thalamocortical connections sharpens the tuning while the preferred orientation of the neurons remains unchanged. This model is verified by computer simulations in which the scaffolds are generated both artificially and inferred from experimental optical imaging data. The plasticity is modeled by the BCM synaptic plasticity rule, and the input environment consists of natural images. We use this model to provide a possible explanation of the recent observation in which two eyes without common visual experience develop similar orientation maps. Finally, we propose an experiment involving the disruption of lateral connections to distinguish this model from models proposed by others.