Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Neural Analyzers”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13Linked to original sources
PubMed2004

Dynamic analyses of information encoding in neural ensembles.

Neural spike train decoding algorithms and techniques to compute Shannon mutual information are important methods for analyzing how neural systems represent biological signals. Decoding algorithms are also one of several strategies being used to design controls for brain-machine interfaces. Developing optimal strategies to design decoding algorithms and compute mutual information are therefore important problems in computational neuroscience. We present a general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. We derive from the algorithm new instantaneous estimates of the entropy, entropy rate, and the mutual information between the signal and the ensemble spiking activity. We assess the accuracy of the algorithm by computing, along with the decoding error, the true coverage probability of the approximate 0.95 confidence regions for the individual signal estimates. We illustrate the new algorithm by reanalyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from two rats foraging in an open circular environment. We compare the performance of this algorithm with a linear filter constructed by the widely used reverse correlation method. The median decoding error for Animal 1 (2) during 10 minutes of open foraging was 5.9 (5.5) cm, the median entropy was 6.9 (7.0) bits, the median information was 9.4 (9.4) bits, and the true coverage probability for 0.95 confidence regions was 0.67 (0.75) using 34 (32) neurons. These findings improve significantly on our previous results and suggest an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted.

Action Potentials↗
PubMed1998

Expression and regulation of kainate and AMPA receptors in the rat neural tube.

We analyzed the expression and regulation of glutamate receptor subunits in the rat neural tube (10 day embryos) and in cell cultures derived from this tissue. In the cultures, all cells were stained with antibodies against the neural progenitor marker nestin. More than 50% of the cells were also stained by the monoclonal antibodies LB1 or A2B5, which bind to neuronal and glial progenitors. Approximately 6% of the cells were stained with antibodies for the low affinity NGF receptor, a neural crest cell marker. A small percentage of cells differentiated to neurons or astrocytes, as determined by staining with anti-neurofilament and anti-GFAP antibodies, respectively. RT-PCR analysis of neural tube tissue and culture mRNAs demonstrated that the AMPA receptor subunits GluR3 and 4 and the kainate receptor subunits GluR6, 7, KA1 and KA2 were detectable at E10. The kainate receptor subunits GluR6 and KA2 were upregulated by culture conditions which stimulated cell differentiation, as determined by concomitant downregulation of nestin mRNA. Both in neural tube tissue and in cultured cells, GluR6 was 100% unedited. Finally, both GluR6 and KA2 proteins could be detected in subpopulations of neural progenitors and differentiated neurons. Our data indicate that kainate receptor genes are expressed in undifferentiated progenitor cells of the neural tube at E10, and are upregulated during neural cell differentiation.

Animals↗
PubMed2006

Neural basis of individual differences in impulsivity: contributions of corticolimbic circuits for behavioral arousal and control.

The objective of the current study was to analyze the neural correlates of behavioral arousal and inhibitory control as they relate to individual differences in impulsivity via well-established functional MRI amygdala reactivity and prefrontal inhibitory control paradigms in healthy adult subjects. Impulsivity correlated positively with activity of the bilateral ventral amygdala, parahippocampal gyrus, dorsal anterior cingulate gyrus (BA 32), and bilateral caudate. Conversely, impulsivity correlated negatively with activity of the dorsal amygdala and ventral prefrontal cortex (BA 47). Together, these findings suggest that dispositional impulsivity is influenced by the functional interplay of corticolimbic behavioral arousal and control circuits.

Adult↗