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Huang, H.-P.

Publications and source records attributed to Huang, H.-P..

2 recordsLinked to original sources
bioRxiv2019

Coordination between stabilizing circuits and updating circuits in spatial orientation working memory

Spatial orientation memory plays a crucial role in animal navigation. Recent studies of tethered Drosophila melanogaster (fruit fly) in a virtual reality setting showed that the head direction is encoded in the form of an activity bump, i.e. localized neural activity, in the torus-shaped ellipsoid body (EB). However, how this system is involved in orientation working memory is not well understood. We investigated this question using free moving flies (Drosophila melanogaster) in a spatial orientation memory task by manipulating two EB subsystems, C and P circuits, which are hypothesized for stabilizing and updating the activity bump, respectively. To this end, we suppressed or activated two types of inhibitory ring neurons (EIP and P) which innervate EB, and we discovered that manipulating the two inhibitory neuron types produced distinct behavioral deficits, suggesting specific roles of the inhibitory neurons in coordinating the stabilization and updating functions of the EB circuits. We further elucidate the neural mechanisms underlying such control circuits using a connectome-constrained spiking neural network model. Significance statementHead-direction (HD) system has been discovered in rodents for decades. But the detailed neural circuit mechanisms underlying the HD system were only described recently by studies of fruit flies on the similar HD system. However, how this fruit fly HD system involves in orientation memory was not well investigated. The present study addresses this question by investigating free moving flies in a spatial orientation working memory task. By combining neural functional experiments and neural circuit modelling, the study shows how disrupting either of the two subcircuits, one stabilizing and the other updating the neural activity, in the HD system leads to different behavioral impairments. The result suggests specific roles of the HD subcircuits in the spatial orientation working memory. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/819185v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@101cb04org.highwire.dtl.DTLVardef@b7ac8borg.highwire.dtl.DTLVardef@a10a41org.highwire.dtl.DTLVardef@a8691e_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗
bioRxiv2019

State Change Probability: A Measure of the Complexity of Cardiac RR Interval Time Series Using Physiological State Change with Statistical Hypothesis Testing

The complexity of biological signals has been proposed to reflect the adaptability of a given biological system to different environments. Two measures of complexity--multiscale entropy (MSE) and entropy of entropy (EoE)--have been proposed, to evaluate the complexity of heart rate signals from different perspectives. The MSE evaluates the information content of a long time series across multiple temporal scales, while the EoE characterizes variation in amount of information, which is interpreted as the \"state changing,\" of segments in a time series. However, both are problematic when analyzing white noise and are sensitive to data size. Therefore, based on the concept of \"state changing,\" we propose state change probability (SCP) as a measure of complexity. SCP utilizes a statistical hypothesis test to determine the physiological state changes between two consecutive segments in heart rate signals. The SCP value is defined as the ratio of the number of state changes to total number of consecutive segment pairs. Two common statistical tests, the t-test and Wilcoxon rank-sum test, were separately used in the SCP algorithm for comparison, yielding similar results. The SCP method is capable of reasonably evaluating the complexity of white noise and other signals, including 1/f noise, periodic signals, and heart rate signals, from healthy subjects, as well as subjects with congestive heart failure or atrial fibrillation. The SCP method is also insensitive to data size. A universal SCP threshold value can be applied, to differentiate between healthy and pathological subjects for data sizes ranging from 100 to 10,000 points. The SCP algorithm is slightly better than the EoE method when differentiating between subjects, and is superior to the MSE method.

physiology↗