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Findley, T. M.

Publications and source records attributed to Findley, T. M..

4 recordsLinked to original sources

Visualizing and sonifying neurodata (ViSoND) for enhanced observation

Neuroscience needs observation. Observation lets us evaluate data quality, judge whether models are biologically realistic, and generate new hypotheses. However, high-dimensional behavioral and neural data are too complex to be easily displayed and eye-tested. Computational methods can reduce the dimensionality of data and reveal statistically robust dynamical structure but often yield results that are difficult to relate back to the underlying biology. In addition, the choice of what parameters to quantify may not capture unexpectedly relevant aspects of the data. To supplement quantification with enhanced qualitative observation, we developed Visualization and Sonification of NeuroData (ViSoND), an open-source approach for displaying multiple data streams using video and sonification. Sonification is nothing new to neuroscience. Scientists have sonified their physiological preparations since Lord Adrians earliest recordings. We extend this tradition by mapping multiple physiological datastreams to musical notes using MIDI. Synchronizing MIDI to video provides an opportunity to watch an animals movement while listening to physiological signals such as action potentials. Here we provide two demonstrations of this approach. First, we used ViSoND to interpret behavioral structure revealed by a computational model trained on the breathing rhythms of freely behaving mice. Second, ViSoND revealed patterns of neural activity in mouse visual cortex corresponding to eye blinks, events that were previously filtered out of analysis. These use cases show that ViSoND can supplement quantitative rigor with observational interpretability. Additionally, ViSoND provides an accessible way to display data which may broaden the audience for communication of neuroscientific findings.

neuroscience↗

Challenges in inferring breathing rhythms from olfactory bulb local field potentials

Odors convey useful navigational and episodic information, yet much of the chemical world remains inaccessible without active sampling through sniffing. Respiratory cycles control odor dynamics within the nose, so understanding olfactory bulb (OB) neural dynamics requires accurate respiratory measurements. While respiratory behavior can be measured directly with a variety of chronic methods, these methods are invasive and none are perfectly robust. OB local field potentials (LFPs) have long been known to couple with respiration. Here we investigated whether the precise timing and frequency of respiration can be inferred from OB LFPs. Our results replicate previous findings that OB LFPs across multiple frequency bands align with respiratory cycles. Further, these OB rhythms are locked to time in the respiratory cycle, and not phase. In addition, we show that 2-12 Hz LFP oscillations effectively track sniffing rate. However, a monotonic relationship between LFP-respiratory delay and sniffing rate, which varies across animals, renders the recovery of precise respiratory events challenging. This work underscores the complex and individualized relationship between rodent respiration and OB LFPs, contributing to our understanding of how respiration controls olfaction.

neuroscience↗

Olfactory bulb tracks breathing rhythms and place in freely behaving mice

Vertebrates sniff to control the odor samples that enter their nose. These samples can not only help identify odorous objects, but also locations and events. However, there is no receptor for place or time. Therefore, to take full advantage of olfactory information, an animals brain must contextualize odor-driven activity with information about when, where, and how they sniffed. To better understand contextual information in the olfactory system, we captured the breathing and movements of mice while recording from their olfactory bulb. In stimulus- and task-free experiments, mice structure their breathing into persistent rhythmic states which are synchronous with statelike structure in ongoing neuronal population activity. These population states reflect a strong dependence of individual neuron activity on variation in sniff frequency, which we display using "sniff fields" and quantify using generalized linear models. In addition, many olfactory bulb neurons have "place fields" that display significant dependence of firing on allocentric location, which were comparable with hippocampal neurons recorded under the same conditions. At the population level, a mouses location can be decoded from olfactory bulb with similar accuracy to hippocampus. Olfactory bulb place sensitivity cannot be explained by breathing rhythms or scent marks. Taken together, we show that the mouse olfactory bulb tracks breathing rhythms and self-location, which may help unite internal models of self and environment with olfactory information as soon as that information enters the brain.

neuroscience↗

Sniff-synchronized, gradient-guided olfactory search by freely-moving mice

For many organisms, searching for relevant targets such as food or mates entails active, strategic sampling of the environment. Finding odorous targets may be the most ancient search problem that motile organisms evolved to solve. While chemosensory navigation has been well characterized in micro-organisms and invertebrates, spatial olfaction in vertebrates is poorly understood. We have established an olfactory search assay in which freely-moving mice navigate noisy concentration gradients of airborne odor. Mice solve this task using concentration gradient cues and do not require stereo olfaction for performance. During task performance, respiration and nose movement are synchronized with tens of milliseconds precision. This synchrony is present during trials and largely absent during inter-trial intervals, suggesting that sniff-synchronized nose movement is a strategic behavioral state rather than simply a constant accompaniment to fast breathing. To investigate the spatiotemporal structure of these active sensing movements, we used machine learning methods to parse motion trajectories into elementary movement motifs. Motifs fall into two clusters, which correspond to investigation and approach states. Investigation motifs lock precisely to sniffing, such that the individual motifs preferentially occur at specific phases of the sniff cycle. This work clarifies sensorimotor strategies for mouse olfactory search and guides ongoing work into the underlying neural mechanisms.

neuroscience↗