Models optimized for real-world tasks reveal the necessity of precise temporal coding in hearing.

Neurons encode information in the timing of their spikes in addition to their firing rates. Spike timing is particularly precise in the auditory nerve, where action potentials phase lock to sound with sub-millisecond precision, but its behavioral relevance is uncertain. To investigate the role of this temporal coding, we optimized machine learning models to perform real-world hearing tasks with simulated cochlear input. We asked how precise auditory nerve spike timing needed to be to reproduce human behavior. Models with high-fidelity phase locking exhibited more human-like sound localization and speech perception than models without, consistent with an essential role in human hearing. Degrading phase locking produced task-dependent effects, revealing how the use of fine-grained temporal information reflects both ecological task demands and neural implementation constraints. The results link neural coding to perception and clarify conditions in which prostheses that fail to restore high-fidelity temporal coding could in principle restore near-normal hearing.

Emerging Activity Patterns and Synaptogenesis in Dissociated Hippocampal Cultures.

Cultures of dissociated hippocampal neurons display a stereotypical development of network activity patterns within the first three weeks of maturation. During this process, network connections develop and the associated spiking patterns range from increasing levels of activity in the first two weeks to regular bursting activity in the third week of maturation. Characterization of network structure is important to examine the mechanisms underlying the emergent functional organization of neural circuits. To accomplish this, confocal microscopy techniques have been used and several automated synapse quantification algorithms based on (co)localization of synaptic structures have been proposed recently. However, these approaches suffer from the arbitrary nature of intensity thresholding and the lack of correction for random-chance colocalization. To address this problem, we developed and validated an automated synapse quantification algorithm that requires minimal operator intervention. Next, we applied our approach to quantify excitatory and inhibitory synaptogenesis using confocal images of dissociated hippocampal neuronal cultures captured at 5, 8, 14 and 20 days in vitro, the time period associated with the development of distinct neuronal activity patterns. As expected, we found that synaptic density increased with maturation, coinciding with increasing spiking activity in the network. Interestingly, the third week of the maturation exhibited a reduction in excitatory synaptic density suggestive of synaptic pruning that coincided with the emergence of regular bursting activity in the network.

Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception.

Perception is thought to be shaped by the environments for which organisms are optimized. These influences are difficult to test in biological organisms but may be revealed by machine perceptual systems optimized under different conditions. We investigated environmental and physiological influences on pitch perception, whose properties are commonly linked to peripheral neural coding limits. We first trained artificial neural networks to estimate fundamental frequency from biologically faithful cochlear representations of natural sounds. The best-performing networks replicated many characteristics of human pitch judgments. To probe the origins of these characteristics, we then optimized networks given altered cochleae or sound statistics. Human-like behavior emerged only when cochleae had high temporal fidelity and when models were optimized for naturalistic sounds. The results suggest pitch perception is critically shaped by the constraints of natural environments in addition to those of the cochlea, illustrating the use of artificial neural networks to reveal underpinnings of behavior.

The dialysis orders objective structured clinical examination (OSCE): a formative assessment for nephrology fellows.

BACKGROUND: Few quantitative nephrology-specific simulations assess fellow competency. We describe the development and initial validation of a formative objective structured clinical examination (OSCE) assessing fellow competence in ordering acute dialysis. METHODS: The three test scenarios were acute continuous renal replacement therapy, chronic dialysis initiation in moderate uremia and acute dialysis in end-stage renal disease-associated hyperkalemia. The test committee included five academic nephrologists and four clinically practicing nephrologists outside of academia. There were 49 test items (58 points). A passing score was 46/58 points. No item had median relevance less than 'important'. The content validity index was 0.91. Ninety-five percent of positive-point items were easy-medium difficulty. Preliminary validation was by 10 board-certified volunteers, not test committee members, a median of 3.5 years from graduation. The mean score was 49 [95% confidence interval (CI) 46-51], κ = 0.68 (95% CI 0.59-0.77), Cronbach's α = 0.84. RESULTS: We subsequently administered the test to 25 fellows. The mean score was 44 (95% CI 43-45); 36% passed the test. Fellows scored significantly less than validators (P < 0.001). Of evidence-based questions, 72% were answered correctly by validators and 54% by fellows (P = 0.018). Fellows and validators scored least well on the acute hyperkalemia question. In self-assessing proficiency, 71% of fellows surveyed agreed or strongly agreed that the OSCE was useful. CONCLUSIONS: The OSCE may be used to formatively assess fellow proficiency in three common areas of acute dialysis practice. Further validation studies are in progress.

Characterizing Chilean blue whale vocalizations with DTAGs: a test of using tag accelerometers for caller identification.

Vocal behavior of blue whales (Balaenoptera musculus) in the Gulf of Corcovado, Chile, was analysed using both audio and accelerometer data from digital acoustic recording tags (DTAGs). Over the course of three austral summers (2014, 2015 and 2016), seventeen tags were deployed, yielding 124 h of data. We report the occurrence of Southeast Pacific type 2 (SEP2) calls, which exhibit peak frequencies, durations and timing consistent with previous recordings made using towed and moored hydrophones. We also describe tonal downswept (D) calls, which have not been previously described for this population. As being able to accurately assign vocalizations to individual whales is fundamental for studying communication and for estimating population densities from call rates, we further examine the feasibility of using high-resolution DTAG accelerometers to identify low-frequency calls produced by tagged blue whales. We cross-correlated acoustic signals with simultaneous tri-axial accelerometer readings in order to analyse the phase match as well as the amplitude of accelerometer signals associated with low-frequency calls, which provides a quantitative method of determining if a call is associated with a detectable acceleration signal. Our results suggest that vocalizations from nearby individuals are also capable of registering accelerometer signals in the tagged whale's DTAG record. We cross-correlate acceleration vectors between calls to explore the possibility of using signature acceleration patterns associated with sounds produced within the tagged whale as a new method of identifying which accelerometer-detectable calls originate from the tagged animal.