2 Clinical Trials for Various Conditions
A fundamental problem in neuroscience is how the brain computes with noisy neurons. An advantage of population codes is that downstream neurons can pool across multiple neurons to reduce the impact of noise. However, this benefit depends on the noise associated with each neuron being independent. Noise correlations refer to the covariance of noise between pairs of neurons, and such correlations can limit the advantages gained from pooling across large neural populations. Indeed, a large body of theoretical work argues that positive noise correlations between similarly tuned neurons reduce the representational capacity of neural populations and are thus detrimental to neural computation. Despite this apparent disadvantage, such noise correlations are observed across many different brain regions, persist even in well-trained subjects, and are dynamically altered in complex tasks. The investigators have advanced the hypothesis that noise correlations may be a neural mechanism for reducing the dimensionality of learning problems. The viability of this hypothesis has been demonstrated in neural network simulations where noise correlations, when embedded in populations with fixed signal-to-noise ratio, enhance the speed and robustness of learning. Here the investigators aim to empirically test this hypothesis, using a combination of computational modeling, fMRI and pupillometry. Establishing a link between noise correlations and learning would open the door to an investigation into how brains navigate a tradeoff between representational capacity and the speed of learning.
There is limited data regarding sound levels and burden in the pediatric cardiac critical care unit and how this compares with WHO standards. We seek to record this data and correlate sound level with bolus sedation administration, patient delirium scores, and patient heart rate trends. Primary Outcomes * 1 peak sound level in cardiac ICU in decibels * 2 Mean sound level in cardiac ICU in decibels * 3 Compare sound levels to WHO recommendations Secondary Outcomes * 1 To explore patient and unit factors that might influence these levels * 2 To analyze sound levels in post-operative neonates, versus infants, versus children * 3 To analyze patients on invasive versus non-invasine versus no ventilation