Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System.

Smart algorithms for gait kinematic motion prediction in wearable assistive devices including prostheses, bionics, and exoskeletons can ensure safer and more effective device functionality. Although embedded systems can support the use of smart algorithms, there are important limitations associated with computational load. This poses a tangible barrier for models with increased complexity that demand substantial computational resources for superior performance. Forecasting through Recurrent Topology (FReT) represents a computationally lightweight time-series data forecasting algorithm with the ability to update and adapt to the input data structure that can predict complex dynamics. Here, we deployed FReT on an embedded system and evaluated its accuracy, computational time, and precision to forecast gait kinematics from lower-limb motion sensor data from fifteen subjects. FReT was compared to pretrained hyperparameter-optimized NNET and deep-NNET (D-NNET) model architectures, both with static model weight parameters and iteratively updated model weight parameters to enable adaptability to evolving data structures. We found that FReT was not only more accurate than all the network models, reducing the normalized root-mean-square error by almost half on average, but that it also provided the best balance between accuracy, computational time, and precision when considering the combination of these performance variables. The proposed FReT framework on an embedded system, with its improved performance, represents an important step towards the development of new sensor-aided technologies for assistive ambulatory devices.

Hypothalamic CRH neurons represent physiological memory of positive and negative experience.

Recalling a salient experience provokes specific behaviors and changes in the physiology or internal state. Relatively little is known about how physiological memories are encoded. We examined the neural substrates of physiological memory by probing CRHPVN neurons of mice, which control the endocrine response to stress. Here we show these cells exhibit contextual memory following exposure to a stimulus with negative or positive valence. Specifically, a negative stimulus invokes a two-factor learning rule that favors an increase in the activity of weak cells during recall. In contrast, the contextual memory of positive valence relies on a one-factor rule to decrease activity of CRHPVN neurons. Finally, the aversive memory in CRHPVN neurons outlasts the behavioral response. These observations provide information about how specific physiological memories of aversive and appetitive experience are represented and demonstrate that behavioral readouts may not accurately reflect physiological changes invoked by the memory of salient experiences.

High-throughput gait acquisition system for freely moving mice.

Normal and pathological locomotion can be discriminated by analyzing an animal's gait on a linear walkway. This step is labor intensive and introduces experimental bias due to the handling involved while placing and removing the animal between trials. We designed a system consisting of a runway embedded within a larger arena, which can be traversed ad libitum by unsupervised, freely moving mice, triggering the recording of short clips of locomotor activity. Multiple body parts were tracked using DeepLabCut and fed to an analysis pipeline (GaitGrapher) to extract gait metrics. We compared the results from unsupervised against the standard experimenter-supervised approach and found that gait parameters analyzed via the new approach were similar to a previously validated approach (Visual Gait Lab). These data show the utility of incorporating an unsupervised, automated, approach for collecting kinematic data for gait analysis.NEW & NOTEWORTHY The acquisition and analysis of walkway data is a time-consuming task. Here, we provide an unmonitored approach for collecting gait metrics that reduces the handling and stress of mice and saves time. A detailed pipeline is outlined that provides for the collection and analysis of data using an integrated suite of tools.

A versatile computational algorithm for time-series data analysis and machine-learning models.

Here we introduce Local Topological Recurrence Analysis (LoTRA), a simple computational approach for analyzing time-series data. Its versatility is elucidated using simulated data, Parkinsonian gait, and in vivo brain dynamics. We also show that this algorithm can be used to build a remarkably simple machine-learning model capable of outperforming deep-learning models in detecting Parkinson's disease from a single digital handwriting test.

Visual Gait Lab: A user-friendly approach to gait analysis.

BACKGROUND: Gait analysis forms a critical part of many lab workflows, ranging from those interested in preclinical neurological models to others who use locomotion as part of a standard battery of tests. Unfortunately, while paw detection can be semi-automated, it becomes generally a time-consuming process with error corrections. Improvement in paw tracking would aid in better gait analysis performance and experience. NEW METHOD: Here we show the use of Visual Gait Lab (VGL), a high-level software with an intuitive, easy to use interface, that is built on DeepLabCut™. VGL is optimized to generate gait metrics and allows for quick manual error corrections. VGL comes with a single executable, streamlining setup on Windows systems. We demonstrate the use of VGL to analyze gait. RESULTS: Training and evaluation of VGL were conducted using 200 frames (80/20 train-test split) of video from mice walking on a treadmill. The trained network was then used to visually track paw placements to compute gait metrics. These are processed and presented on the screen where the user can rapidly identify and correct errors. COMPARISON WITH EXISTING METHODS: Gait analysis remains cumbersome, even with commercial software due to paw detection errors. DeepLabCut™ is an alternative that can improve visual tracking but is not optimized for gait analysis functionality. CONCLUSIONS: VGL allows for gait analysis to be performed in a rapid, unbiased manner, with a set-up that can be easily implemented and executed by those without a background in computer programming.

Vision and Locomotion Combine to Drive Path Integration Sequences in Mouse Retrosplenial Cortex.

The retrosplenial cortex (RSC) is involved in a broad range of cognitive functions, integrating rich sensory, motor, and spatial signals from multiple brain areas, including the hippocampal system. RSC neurons show hippocampus-dependent activity reminiscent of place cell sequences. Using cellular calcium imaging in a virtual reality (VR)-based locomotion task, we investigate how the integration of visual and locomotor inputs may give rise to such activity in RSC. A substantial population shows neural sequences that track position in the VR environment. This activity is driven by the conjunction of visual stimuli sequences and active movement, which is suggestive of path integration. The activity is anchored to a reference point and predominantly follows the VR upon manipulations of optic flow against locomotion. Thus, locomotion-gated optic flow, combined with the presence of contextual cues at the start of each trial, is sufficient to drive the sequential activity. A subpopulation shows landmark-related visual responses that are modulated by animal's position in the VR. Thus, rather than fragmenting the spatial representation into equivalent locomotion-based ensemble versus optic-flow-based ensemble, in RSC, optic flow appears to override locomotion signals coherently in the population, when the gain between the two signals is altered.

An economical solution to record and control wheel-running for group-housed mice.

BACKGROUND: The effects of exercise on brain function are widely known; however, there is a need for inexpensive, practical solutions for monitoring and metering the activity of multiple mice. NEW METHOD: A contoured running wheel that has a built-in radio-frequency identification (RFID) receiver to monitor the activity of several mice in a single cage is presented. This system is scalable , the interface is easy to use, and the wheel can be dynamically locked so that each group-housed mouse receives a set exercise regimen. RESULTS: We were able to reliably monitor three mice that were group-housed. We were able to reliably meter the amount of exercise performed by the mice using the servo-controlled lock. COMPARISON WITH EXISTING METHODS: Current methods allow a wheel to be locked when a set distance is reached. However, an issue with this method is that the set distance includes the cumulative activity of all mice in the cage so one mouse could contribute a disproportionate amount to the total distance. Our solution ensures that the wheel is locked when an individual mouse reaches the target distance, but remains unlocked for individuals that have not reached the programmed distance. CONCLUSIONS: The dynamic locking wheel (DynaLok) is designed to allow a researcher to provide individually designed exercise plans for multi-housed mice; therefore, users are able to house mice conventionally rather than in individual cages. DynaLok reduces animal housing costs, allows for new experimental exercise regimens to be developed, and is scalable and cost-effective.

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments.

Closed-loop neurophysiological systems use patterns of neuronal activity to trigger stimuli, which in turn affect brain activity. Such closed-loop systems are already found in clinical applications, and are important tools for basic brain research. A particularly interesting recent development is the integration of closed-loop approaches with optogenetics, such that specific patterns of neuronal activity can trigger optical stimulation of selected neuronal groups. However, setting up an electrophysiological system for closed-loop experiments can be difficult. Here, a ready-to-apply Matlab code is provided for triggering stimuli based on the activity of single or multiple neurons. This sample code can be easily modified based on individual needs. For instance, it shows how to trigger sound stimuli and how to change it to trigger an external device connected to a PC serial port. The presented protocol is designed to work with a popular neuronal recording system for animal studies (Neuralynx). The implementation of closed-loop stimulation is demonstrated in an awake rat.

Lesions of dorsal striatum eliminate lose-switch responding but not mixed-response strategies in rats.

We used focal brain lesions in rats to examine how dorsomedial (DMS) and dorsolateral (DLS) regions of the striatum differently contribute to response adaptation driven by the delivery or omission of rewards. Rats performed a binary choice task under two modes: one in which responses were rewarded on half of the trials regardless of choice; and another 'competitive' one in which only unpredictable choices were rewarded. In both modes, control animals were more likely to use a predictable lose-switch strategy than animals with lesions of either DMS or DLS. Animals with lesions of DMS presumably relied more on DLS for behavioural control, and generated repetitive responses in the first mode. These animals then shifted to a random response strategy in the competitive mode, thereby performing better than controls or animals with DLS lesions. Analysis using computational models of reinforcement learning indicated that animals with striatal lesions, particularly of the DLS, had blunted reward sensitivity and less stochasticity in the choice mechanism. These results provide further evidence that the rodent DLS is involved in rapid response adaptation that is more sophisticated than that embodied by the classic notion of habit formation driven by gradual stimulus-response learning.

Acute NMDA receptor antagonism disrupts synchronization of action potential firing in rat prefrontal cortex.

Antagonists of N-methyl-D-aspartate receptors (NMDAR) have psychotomimetic effects in humans and are used to model schizophrenia in animals. We used high-density electrophysiological recordings to assess the effects of acute systemic injection of an NMDAR antagonist (MK-801) on ensemble neural processing in the medial prefrontal cortex of freely moving rats. Although MK-801 increased neuron firing rates and the amplitude of gamma-frequency oscillations in field potentials, the synchronization of action potential firing decreased and spike trains became more Poisson-like. This disorganization of action potential firing following MK-801 administration is consistent with changes in simulated cortical networks as the functional connections among pyramidal neurons become less clustered. Such loss of functional heterogeneity of the cortical microcircuit may disrupt information processing dependent on spike timing or the activation of discrete cortical neural ensembles, and thereby contribute to hallucinations and other features of psychosis induced by NMDAR antagonists.