3 results match your criteria: "USA. Center for Neural Basis of Cognition[Affiliation]"
J Neural Eng
June 2014
Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA. Center for Neural Basis of Cognition, Pittsburgh, PA 15213, USA.
Objective: This study describes results of primary afferent neural microstimulation experiments using microelectrode arrays implanted chronically in the lumbar dorsal root ganglia (DRG) of four cats. The goal was to test the stability and selectivity of these microelectrode arrays as a potential interface for restoration of somatosensory feedback after damage to the nervous system such as amputation.
Approach: A five-contact nerve-cuff electrode implanted on the sciatic nerve was used to record the antidromic compound action potential response to DRG microstimulation (2-15 µA biphasic pulses, 200 µs cathodal pulse width), and the threshold for eliciting a response was tracked over time.
Cereb Cortex
June 2015
Department of Psychiatry and Center for Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Given the importance of gamma oscillations in normal and disturbed cognition, there has been growing interest in their developmental trajectory. In the current study, age-related changes in sensory cortical gamma were studied using the auditory steady-state response (ASSR), indexing cortical activity entrained to a periodic auditory stimulus. A large sample (n = 188) aged 8-22 years had electroencephalography recording of ASSR during 20-, 30-, and 40-Hz click trains, analyzed for evoked amplitude, phase-locking factor (PLF) and cross-frequency coupling (CFC) with lower frequency oscillations.
View Article and Find Full Text PDFJ Neural Eng
December 2013
Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA. Center for Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA.
Objective: Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than 3, thereby preventing direct visualization of the latent space.
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