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Electrocorticography (ECoG) signals can provide high spatio-temporal resolution and high signal to noise ratio recordings of local neural activity from the surface of the brain. Previous studies have shown that broad-band, spatially focal, high-frequency increases in ECoG signals are highly correlated with movement and other cognitive tasks and can be volitionally modulated. However, significant additional information may be present in inter-electrode interactions, but adding additional higher order inter-electrode interactions can be impractical from a computational aspect, if not impossible.In this paper we present a new method of calculating high frequency interactions between electrodes called Short-Time Windowed Covariance (STWC) that builds on mathematical techniques currently used in neural signal analysis, along with an implementation that accelerates the algorithm by orders of magnitude by leveraging commodity, off-the-shelf graphics processing unit (GPU) hardware.Using the hardware-accelerated implementation of STWC, we identify many types of event-related inter-electrode interactions from human ECoG recordings on global and local scales that have not been identified by previous methods. Unique temporal patterns are observed for digit flexion in both low- (10mm spacing) and high-resolution (3mm spacing) electrode arrays.Covariance is a commonly used metric for identifying correlated signals, but the standard covariance calculations do not allow for temporally varying covariance. In contrast STWC allows and identifies event-driven changes in covariance without identifying spurious noise correlations.STWC can be used to identify event-related neural interactions whose high computational load is well suited to GPU capabilities.

Generative models for vision and pattern recognition have been overshadowed in recent years by powerful non-parametric discriminative models. These discriminative models can learn arbitrary decision boundaries between classes and have proved very effective in classification and detection problems. However, unlike generative models, they do not lend themselves naturally to more general vision tasks such as rendering novel images, de-noising, and in-painting. In this paper we introduce Complementary Kernel Density Estimation (CKDE), a new generative model that adopts many of the features of non-parametric discriminative models: (1) CKDE allows complex decision surfaces and arbitrary class conditional distributions to be learned, (2) it is easy to train because the log likelihood of the model is concave, so it has no local maxima, and (3) one can train its class conditional distributions jointly to share information among the different classes. We first demonstrate that CKDE is more accurate in benchmark classification tasks than a purely discriminative method such as the SVM. We then show that the posterior probability of class labels is more accurately estimated than kernelized logistic regression. Our other results demonstrate that partial images can be accurately classified by marginalizing unobserved pixels from the class conditional distributions, and missing parts of the image can be painted in using the learned generative model.

Gaze following is a key component of human social cognition. Gaze following directs attention to areas of high information value and accelerates social, causal, and cultural learning. An issue for both robotic and infant learning is whose gaze to follow. The hypothesis tested in this study is that infants use information derived from an entity's interactions with other agents as evidence about whether that entity is a perceiver. A robot was programmed so that it could engage in communicative, imitative exchanges with an adult experimenter. Infants who saw the robot act in this social-communicative fashion were more likely to follow its line of regard than those without such experience. Infants use prior experience with the robot's interactions as evidence that the robot is a psychological agent that can see. Infants want to look at what the robot is seeing, and thus shift their visual attention to the external target.

High gamma (HG) power changes during motor activity, especially at frequencies above 70 Hz, play an important role in functional cortical mapping and as control signals for BCI (brain computer interface) applications. Most studies of HG activity have used ECoG (electrocorticography) which provides high-quality spatially localized signals, but is an invasive method. Recent studies have shown that non-invasive modalities such as EEG and MEG can also detect task related HG power changes. We show here that a 27 channel EEG (electroencephalography) montage provides high-quality spatially localized signals non-invasively for HG frequencies ranging from 83 to 101 Hz. We used a generic head model, a weighted minimum norm least squares (MNLS) inverse method, and a self-paced finger movement paradigm. The use of an inverse method enables us to map the EEG onto a generic cortex model. We find the HG activity during the task to be well localized in the contralateral motor area. We find HG power increases prior to finger movement, with average latencies of 462 ms and 82 ms before EMG (electromyogram) onset. We also find significant phase-locking between contra- and ipsilateral motor areas over a similar HG frequency range; here the synchronization onset precedes the EMG by 400 ms. We also compare our results to ECoG data from a similar paradigm and find EEG mapping and ECoG in good agreement. Our findings demonstrate that mapped EEG provides information on two important parameters for functional mapping and BCI which are usually only found in HG of ECoG signals: spatially localized power increases and bihemispheric phase-locking.

We demonstrate the feasibility of real-time cortical mapping from arrays of subdural electrodes using the electrocorticographic signal power in the higher spectral frequencies (76-200 Hz, or "chi-index"). Hand area was mapped offline in eight individuals using brief baseline and hand-movement measurements. In one patient, hand sensorimotor cortex was identified online during a handshake. We propose that this high-frequency component of the electrocorticogram provides a generic, reliable, clinically useful correlate of local cortical function.

Medically refractory epilepsy accounts for more than 30% of the epilepsy population. Scalp EEG electrodes have limited ability to localize seizure onset from deep structures and implantation of subdural electrodes with long term monitoring provides additional information. Apart from clinical application, this patient population provides a unique opportunity for acquiring electrocorticography data in research paradigms. We present a method for rapid localization of electrodes using lateral and anterior-posterior X-rays. Skull landmarks and proportions are used for co-registration with the standardized Talairach coordinate system. This MATLAB-based "Location on Cortex" (LOC) package facilitates rapid visualization of clinical and experimental data in a user-friendly manner.

Short-term synaptic plasticity has been proposed as a way for cortical neurons to process temporal information. We present a model network that uses short-term plasticity to implement a temporal clustering algorithm. The model's facilitory synapses learn temporal signals drawn from mixtures of nonlinear processes. Units in the model correspond to populations of cortical pyramidal cells arranged in columns; each column consists of neurons with similar spatiotemporal receptive fields. Clustering is based on mutual inhibition similar to Kohonen's SOMs. A generalized expectation maximization (GEM) algorithm, guaranteed to increase model likelihood with each iteration, learns the synaptic parameters.

Recent experiments have shown that neocortical synapses exhibit both short-term plasticity and spike-timing dependent long-term plasticity. It has been suggested that changes in short-term plasticity are mediated by a redistribution of synaptic efficacy. Here we propose a simple model of the interaction between spike-timing dependent plasticity and short-term plasticity. We show that the model captures the synaptic behavior seen in experiments on redistribution of synaptic efficacy. Results from our simulations suggest that spike-timing dependent redistribution of synaptic efficacy offers neocortical neurons a potentially powerful mechanism for learning spatiotemporal patterns in the input stream.

Visual cognition depends critically on the moment-to-moment orientation of gaze. To change the gaze to a new location in space, that location must be computed and used by the oculomotor system. One of the most common sources of information for this computation is the visual appearance of an object. A crucial question is: How is the appearance information contained in the photometric array is converted into a target position? This paper proposes a such a model that accomplishes this calculation. The model uses iconic scene representations derived from oriented spatiochromatic filters at multiple scales. Visual search for a target object proceeds in a coarse-to-fine fashion with the target's largest scale filter responses being compared first. Task-relevant target locations are represented as saliency maps which are used to program eye movements. A central feature of the model is that it separates the targeting process, which changes gaze, from the decision process, which extracts information at or near the new gaze point to guide behavior. The model provides a detailed explanation for center-of-gravity saccades that have been observed in many previous experiments. In addition, the model's targeting performance has been compared with the eye movements of human subjects under identical conditions in natural visual search tasks. The results show good agreement both quantitatively (the search paths are strikingly similar) and qualitatively (the fixations of false targets are comparable).

The standard cortical model assumes that the meaning of a neuron's signal is contained in its firing rate. While that model has been used to interpret a voluminous amount of experimental data, it does not address the question of timing, or how recipient neurons can decode this signal in time to predict behavioral results. We propose a model based on coincident firing of large groups of neurons. We show using an example of predictive coding, how the cortex can support vast amounts of non-interfering parallel computation.

Active vision systems have the capability of continuously interacting with the environment. The rapidly changing environment of such systems means that it is attractive to replace static representations with visual routines that compute information on demand. Such routines place a premium on image data structures that are easily computed and used. The purpose of this paper is to propose a general active vision architecture based on efficiently computable iconic representations. This architecture employs two primary visual routines, one for identifying the visual image near the fovea (object identification), and another for locating a stored prototype on the retina (object location). This design allows complex visual behaviors to be obtained by composing these two routines with different parameters. The iconic representations are comprised of high-dimensional feature vectors obtained from the responses of an ensemble of Gaussian derivative spatial filters at a number of orientations and scales. These representations are stored in two separate memories. One memory is indexed by image coordinates while the other is indexed by object coordinates. Object location matches a localized set of model features with image features at all possible retinal locations. Object identification matches a foveal set of image features with all possible model features. We present experimental results for a near real-time implementation of these routines on a pipeline image processor and suggest relatively simple strategies for tackling the problems of occlusions and scale variations. We also discuss two additional visual routines, one for top-down foveal targeting using log-polar sensors and another for looming detection, which are facilitated by the proposed architecture.

We investigate the class of sets that form sparse symmetric differences with P-selective sets. Intuitively, this class (denoted by PSEL-close) comprises of sets that can, in a certain sense, be approximated by P-selective sets. A primary motivation behind the introduction of this new class is to unify the separate approaches that have been undertaken for sparse and P-selective sets. In order to establish PSEL-close as a distinct class, we first prove a theorem separating it from both the encompassing class of P/poly and the subclasses of P-selective and sparse sets. We then prove that no ⩽mp-hard set for E can be in PSEL-close. The proof of this theorem relies on techniques from the work of Berman and Hartmanis (1977) and Schoning (1986), and generalizes their results in a straightforward manner.