Increasing the performance of neural prostheses is necessary for assuring their clinical viability. One performance limitation is the presence of correlated trial-to-trial variability that can cause neural responses to wax and wane in concert as the subject is, for example, more attentive or more fatigued. We report here the design and characterization of a Factor-Analysis-based decoding algorithm that is able to contend with this confound. We characterize the decoder (classifier) on a previously reported dataset where monkeys performed both a real reach task and a prosthetic cursor movement task while we recorded from 96 electrodes implanted in dorsal premotor cortex. In principle, the decoder infers the underlying factors that co-modulate the neurons ’ responses and can use this information to function with reduced error rates (1 of 8 reach target prediction) of up to ∼75 % (∼20 % total prediction error using independent Gaussian or Poisson models became ∼5%). Such Factor-Analysis based methods appear to be effective when attempting to combat directly unobserved trial-by-trial neural variabiliy. Index Terms — Factor analysis, premotor cortex, brainmachine and brain-computer interfaces, neural prostheses

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Abstract — We have developed a virtual integration environment (VIE) for the development of neural prosthetic systems. This paper, the second of two companion articles, describes the use of the VIE as a common platform for the implementation of neural decode algorithms. In this paper, a linear filter decode and a recursive Bayesian algorithm are implemented as separate signal analysis modules of the VIE for the real-time decode of end effector trajectory. The process of implementing each algorithm is described and the real-time behavior as well as computational cost for each algorithm is examined. This is the first report of the real-time implementation of the Mixture of Trajectory Models decode [10]. These real-time algorithms can be easily interfaced with pre-existing modules of the VIE to control simulated and real devices. I.

Changes in membrane protein localization are critical to establishing cell polarity and regulating cell signaling. Fluorescence microscopy of labeled proteins allows visualization of these changes, but quantitative analysis is needed to study this aspect of cell signaling in full mechanistic detail. We have developed a novel approach for quantitative assessment of membrane protein redistribution based on four-dimensional video microscopy of fluorescently labeled proteins. Our analytic system provides robust automated methods for cell surface reconstruction, cell shape tracking, cell-surface distance measurement, and cluster formation analysis. These methods permit statistical analyses and testing of mechanistic hypotheses regarding cell signaling. We have used this approach to measure antigen-dependent clustering of signaling molecules in CD4 + T lymphocytes, obtaining clustering velocities consistent with single-particle tracking data. Our system captures quantitative differences in clustering between signaling proteins with distinct biological functions. Our methods can be generalized to a range of cell-signaling phenomena and enable novel applications not feasible with single-particle studies, such as analysis of subcellular protein localization in live organ culture.

Some movements that animals and humans make are highly stereotyped, repeated with little variation. The patterns of neural activity associated with repeats of a movement may be highly similar, or the same movement may arise from different patterns of neural activity, if the brain exploits redundancies in the neural projections to muscles. We examined the stability of the relationship between neural activity and behavior. We asked whether the variability in neural activity that we observed during repeated reaching was consistent with a noisy but stable relationship, or with a changing relationship, between neural activity and behavior. Monkeys performed highly similar reaches under tight behavioral control, while many neurons in the dorsal aspect of premotor cortex and the primary motor cortex were simultaneously monitored for several hours. Neural activity was predominantly stable over time in all measured properties: firing rate, directional tuning, and contribution to a decoding model that predicted kinematics from neural activity. The small changes in neural activity that we did observe could be accounted for primarily by subtle changes in behavior. We conclude that the relationship between neural activity and practiced behavior is reasonably stable, at least on timescales of minutes up to 48 h. This finding has significant implications for the design of neural prosthetic systems because it suggests that device recalibration need not be overly frequent, It also has implications for studies of neural plasticity because a stable baseline permits identification of nonstationary shifts. Key words: premotor; arm; macaque; multielectrode array; decoding; brain machine interface

movement preparation and execution. J Neurophysiol 104: 799–810, 2010. First published June 10, 2010; doi:10.1152/jn.00231.2009. Dorsal premotor cortex (PMd) is known to be involved in the planning and execution of reaching movements. However, it is not understood how PMd plan activity—often present in the very same neurons that respond during movement—is prevented from itself producing movement. We investigated whether inhibitory interneurons might “gate” output from PMd, by maintaining high levels of inhibition during planning and reducing inhibition during execution. Recently developed methods permit distinguishing interneurons from pyramidal neurons using extracellular recordings. We extend these methods here for use with chronically implanted multi-electrode arrays. We then applied these methods to single- and multi-electrode recordings in PMd of two monkeys performing delayed-reach tasks. Responses of putative interneurons were not generally in agreement with the hypothesis

Aged rats are known to have deficits in spatial learning behavior in the Morris water maze. We have found that aged rats also have deficits in NR2B protein expression and that the protein expression deficit is correlated with their performance in the Morris water maze. To test whether this NR2B deficit was sufficient to account for the behavioral deficit, we used antisense oligonucleotides to specifically knock down NR2B subunit expression in the hippocampus of young rats. NR2B antisense treatment diminished NMDA receptor responses, abolished NMDA-dependent long-term potentiation (LTP), and impaired spatial learning. These data demonstrate the important role of NR2B in LTP and learning and memory and suggest a role for reduced NR2B expression in age-related cognitive decline. Key words: NMDA; NR2B; aging; LTP; learning; antisense It has been known for some time that aged rats have deficits in spatial learning tasks (Barnes, 1979; Barnes et al., 1980; deToledo-Morrell et al., 1988; Ward et al., 1999a,b) and in longterm potentiation (LTP), a form of synaptic plasticity, which displays many of the characteristics thought to be required for a molecular mechanism of memory formation (Landfield and

Neuronal populations throughout the brain achieve levels of synchronous electrophysiological activity as a consequence of both normal brain function as well as during pathological states such as in epileptic seizures. Understanding this synchrony and being able to quantitatively assess the dynamics with which neuronal oscillators across the brain couple their activity is a critical component toward decoding such complex behavior. Commonly applied techniques to resolve relationships between oscillators typically make assumptions of linearity and stationarity that are not likely to be valid for complex neural signals. In this study, intracranial electroencephalographic activity was recorded bilaterally in both hippocampi and in anteromedial thalamus of rat under normal conditions and during hypersynchronous seizure activity induced by focal injection of the epileptogenic agent kainic acid. Nonlinear oscillators were first extracted using empirical mode decomposition. The technique of eigenvalue decomposition was used to assess global phase synchrony of the highest energy oscillators. The Hilbert analytical technique was then used to measure instantaneous phase synchrony of these oscillators as they evolved in time. The application of these analytical techniques provides a means for assessing how complex oscillatory behavior in the brain evolves and changes during both normal activity and as a consequence of diseased states without making restrictive and possibly erroneous assumptions of the linearity and stationarity of the underlying oscillatory activity.

doi:10.1093/nar/gkr022 Comprehensive computational design of mCreI homing endonuclease cleavage specificity for genome engineering

M.S. and K.V.S. contributed equally to this work. Running head: Neural decoding using a mixture of trajectory models Correspondence: