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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.

Cost-based metrics formalize notions of distance, or dissimilarity, between two spike trains, and are applicable to single- and multineuronal responses. As such, these metrics have been used to characterize neural variability and neural coding. By examining the structure of an efficient algorithm [Aronov D, 2003. Fast algorithm for the metric-space analysis of simultaneous responses of multiple single neurons. J Neurosci Methods 124(2), 175-79] implementing a metric for multineuronal responses, we determine criteria for its generalization, and identify additional efficiencies that are applicable when related dissimilarity measures are computed in parallel. The generalized algorithm provides the means to test a wide range of coding hypotheses.

We present a low-power VLSI wake-up detector for a sensor network that uses acoustic signals to localize ground-base vehicles. The detection criterion is the degree of low-frequency periodicity in the acoustic signal, and the periodicity is computed from the "bumpiness" of the autocorrelation of a one-bit version of the signal. We then describe a CMOS ASIC that implements the periodicity estimation algorithm. The ASIC is functional and its core consumes 835 nanowatts. It was integrated into an acoustic enclosure and deployed in field tests with synthesized sounds and ground-based vehicles. Fil: Goldberg, David H.. Johns Hopkins University; Estados Unidos Fil: Andreou, Andreas. Johns Hopkins University; Estados Unidos Fil: Julian, Pedro Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina Fil: Pouliquen, Philippe O.. Johns Hopkins University; Estados Unidos Fil: Riddle, Laurence. Signal Systems Corporation; Estados Unidos Fil: Rosasco, Rich. Signal Systems Corporation; Estados Unidos

We compare two methods for extracting information about a dynamic analog stimulus from a spike train: a rate decoder, which is based on the number of spikes in a counting window, and a temporal decoder, which is based on interspike intervals. We derive analytical expressions for the distortion between a stimulus current that drives an integrate-and-fire spike generator and a reconstruction of that current based on the spike train. We consider intrinsic distortion, which arises due to the limitations of spike coding, and extrinsic distortion, which is due to explicit alterations of the spike train. We find that in the absence of extrinsic distortion, the temporal decoder is superior to the rate decoder. In the presence of extrinsic distortion, the temporal decoder is preferred when low latency is required, but the rate decoder is preferred when the latency requirement is relaxed.

We examine the spiking axon as a communication channel. We develop a first principles channel model that encompasses the noise in the axon, which manifests itself as spike jitter, and the power consumption, which arises from the activity of the Na + –K + pump. This model enables us to examine the trade-off between the information rate and power consumption. Using parameters from the frog myelinated axon, we determine the spike rate that corresponds to the maximum energy efficiency. This spike rate is consistent with experimental observations, which suggests that neural communication may have developed to maximize energy efficiency rather than information rate alone.

We present a scheme for implementing highly-connected, reconfigurable networks of integrate-and-fire 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-fire neurons, each with up to 128 probabilistic synapses, demonstrate these concepts in an image processing task.

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.

In the visual cortex of the cat and ferret, it is established that orientation selectivity is the consequence of experience-dependent plasticity. Recent experiments, however, indicate that the layout of orientation maps is remarkably stable and experience-independent. We present a model to account for these seemingly paradoxical results. In this model, a scaffold consisting of anisotropic lateral connections is laid down in horizontal circuitry prior to visual experience. These lateral connections provide an experience-independent framework for the developing orientation map by inducing broad orientation tuning. Adult-like, sharp orientation tuning emerges as the result of experience-dependent plasticity of the feedforward connections. This plasticity is modeled by the BCM synaptic plasticity rule in a natural scene environment. This model simultaneously satisfies the requirements of plasticity and stability that are seen in experiment. In addition, this model provides an explanation of the phenomenon where two eyes without common visual experience develop similar orientation maps.

Augmented reality systems hold great promise, but as they become more complex they can become more challenging to use. Incorporating neural interfaces into augmented reality systems can dramatically increase usability and utility. We explore these issues in the context of Equinox Corporation's Night REAPER™ system--an augmented reality system for dismounted warfighters. We describe the current Night REAPER system and then survey some of the potential enhancements and unique design challenges associated with the addition of a neural interface. Signals, sensors, and decoding techniques for the system's brain-machine interface are discussed.

Sound localization using energy-aware hardware for sensor networks nodes is a problem with many applications in surveillance and security. In this paper, we evaluate four algorithms for sound localization using signals recorded in a natural environment with an array of commercial off-the-shelf microelectromechanical systems microphones and a specially designed compact acoustic enclosure. We evaluate performance of the algorithms and their hardware complexity which relates directly to energy consumption. Fil: Julian, Pedro Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. The Johns Hopkins University; Estados Unidos. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina Fil: Andreou, Andreas. The Johns Hopkins University; Estados Unidos Fil: Riddle, Larry. Signal Systems Corporation; Estados Unidos Fil: Shamma, Shihab. University of Maryland; Estados Unidos Fil: Goldberg, David H.. The Johns Hopkins University; Estados Unidos Fil: Cauwenberghs, Gert. The Johns Hopkins University; Estados Unidos

We discuss biologically inspired devices and architectures for acoustic processing microsystems. We exploit state of the art integrated microsystems technologies to heterogeneously integrate electronics and micromechanical structures for acoustic sensing and signal processing.

We present an analog VLSI address-event transceiver containing an array of integrate-and-fire neurons and a scheme for implementing a reconfigurable neural network with probabilistic synapses. Neural "spikes" are transmitted through address-event representation-the address of the sending neuron is communicated through an asynchronous request and acknowledgment cycle. Continuous-valued synaptic weights are implemented by probabilistically routing address events. Results from a prototype system with 1024 analog VLSI integrate-and-fire neurons, each with up to 128 probabilistic synapses, demonstrate these concepts in an image processing application.

Multielectrode recordings now reliably deliver simultaneous signals from a hundred or more neurons or networks. However, many analytic techniques are presently computationally limited to smaller numbers of signals, severely limiting our ability to relate these neural signals to brain functions including sensation, perception, decision, and action.To address this imbalance, we are developing a new open source Neurophysiology Extended Analysis Tool. NEAT leverages existing code bases and new massively-parallel computational technology to enable any multielectrode lab to perform high-throughput informative analyses. We begin with linked evaluations of: 1) computational bottlenecks in analytic routines, many information-theoretic, developed for our Spike Train Analysis Toolkit and distributed via neuroanalysis.org to over 1,600 sites [Goldberg et al. Neuroinformatics 7, 165-178, 2009], and 2) specific capabilities and restrictions of new graphics-processor-derived computational engines supplied on inexpensive drop-in cards.We project a greater than order of magnitude speedup that will allow many offline analyses to be performed in real time during experiments, and now-impractical questions to be explored offline in reasonable compute times. For example, pairwise analyses now possible on 10 or fewer neurons may be extendible to 50 or more. We focus on information-theoretic measures and standard pair-wise correlations, JPSTHs, spectra, coherences, and new and significant analyses that present significant loads for multineuron recordings.To aid communities planning similar GPU-enabled analyses, we note that the complex, structured, and hierarchic GPU architecture requires special optimization strategies:• decomposing code into >1,000 simultaneous threads is needed to efficiently use the 448 cores on new GPUs,• data should be loaded into on-chip memory once and re-used, avoiding transfers to other memory layers,• kernel processes must optimize thread/kernel and thread/block instruction execution in few clock cycles,• flow control code should control multi-thread warps, not individual threads.Support: MH057153/MH068012.

Analyses of neural coding-the representation and processing of spike train information-require multiple methods, because neural systems use many kinds of representations, and many analytic methods require specific types or amounts of data. Complementing widely-available conventional methods, we developed and released open source the following via neuroanalysis.org (Goldberg et al. Neuroinformatics 7, 165-178, 2009):• the downloadable STAToolkit suite of information-theoretic algorithms,• guidance for algorithmic development, use, and applicability to neural systems, and• an in-development AnalysisServer.Toward aiding formulation and tests of hypotheses about neural coding, sensory discrimination, firing pattern variability, synchrony and other relations characterizing multineuronal recordings, we now report expanding capabilities of neuroanalysis.org. Utilizing algorithms, code, and/or insight from twelve collaborators, we expand our set of complementary analytic methods, including additional information measures, dimensional reduction, distinguishing information from purely biophysical variation, and generation of surrogate multineuronal data sets. These include a new entropy estimator (our eighth) the ‘NSB’ method, aiding analyses of highly undersampled data. Examples and demonstrations serve to inform and guide neurophysiologists to select STAToolkit methods.Recent publications and presentations describe STAToolkit methodologies, report a new index characterizing how population activity deviates from maximum-entropy models, and discuss applicability of metrics to synaptic processing and large numbers of neurons.To assist computationally-intensive explorations of databased spike trains, we also offer an in-development AnalysisServer, an open-access dedicated large-scale computational array. ‘Analyze’ links will enable dataset grouping, parsing, concatenation, and entropy and information determination from data at neurodatabase.org.Parallel enhancements to neurodatabase.org are intended to aid understanding of neural function in both normal and disease states. We continue to accommodate and solicit an expanding set of data types including recordings from multiple preparations.Supported by Human Brain Project/Neuroinformatics MH068012 from NIMH, NINDS, NIA, NIBIB and NSF.

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 neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience’s Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant- collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov, http://neurogateway.org, and other sites as they come on line.

We describe a low-power VLSI wake-up detector for use in an acoustic surveillance sensor network. The detection criterion is based on the degree of low-frequency periodicity in the acoustic signal. To this end, we have developed a periodicity estimation algorithm that maps particularly well to a low-power VLSI implementation. The time-domain algorithm is based on the "bumpiness" of the autocorrelation of one-bit version of the signal. We discuss the relationship of this algorithm to the maximum-likelihood estimator for periodicity. We then describe a full-custom CMOS ASIC that implements this algorithm. This ASIC is fully functional and its core consumes 835 nano-Watts. The ASIC was integrated into an acoustic enclosure and tested outdoors on synthesized sounds. This unit was also deployed in a three-node sensor network and tested on ground-based vehicles.