Sensory Feedback Reduces Individuality by Increasing Variability within Subjects

A computational neural model that incorporates both intrinsic dynamics and sensory feedback in the Aplysia feeding network

Studying the nervous system underlying animal motor control can shed light on how animals can adapt flexibly to a changing environment. We focus on the neural basis of feeding control in Aplysia californica. Using the Synthetic Nervous System framework, we developed a model of Aplysia feeding neural circuitry that balances neurophysiological plausibility and computational complexity. The circuitry includes neurons, synapses, and feedback pathways identified in existing literature. We organized the neurons into three layers and five subnetworks according to their functional roles. Simulation results demonstrate that the circuitry model can capture the intrinsic dynamics at neuronal and network levels. When combined with a simplified peripheral biomechanical model, it is sufficient to mediate three animal-like feeding behaviors (biting, swallowing, and rejection). The kinematic, dynamic, and neural responses of the model also share similar features with animal data. These results emphasize the functional roles of sensory feedback during feeding.

Scaling of buccal mass growth and muscle activation determine the duration of feeding behaviours in the marine mollusc Aplysia californica

The mechanical forces experienced during movement and the time constants of muscle activation are important determinants of the durations of behaviours, which may both be affected by size-dependent scaling. The mechanics of slow movements in small animals are dominated by elastic forces and are thus quasistatic (i.e. always near mechanical equilibrium). Muscular forces producing movement and elastic forces resisting movement should scale identically (proportional to mass2/3), leaving the scaling of the time constant of muscle activation to play a critical role in determining behavioural duration. We tested this hypothesis by measuring the duration of feeding behaviours in the marine mollusc Aplysia californica whose body sizes spanned three orders of magnitude. The duration of muscle activation was determined by measuring the time it took for muscles to produce maximum force as A. californica attempted to feed on tethered inedible seaweed, which provided an in vivo approximation of an isometric contraction. The timing of muscle activation scaled with mass0.3. The total duration of biting behaviours scaled identically, with mass0.3, indicating a lack of additional mechanical effects. The duration of swallowing behaviour, however, exhibited a shallower scaling of mass0.17. We suggest that this was due to the allometric growth of the anterior retractor muscle during development, as measured by micro-computed tomography (micro-CT) scans of buccal masses. Consequently, larger A. californica did not need to activate their muscles as fully to produce equivalent forces. These results indicate that muscle activation may be an important determinant of the scaling of behavioural durations in quasistatic systems.

Regional differences in the biological variability of plantar pressure as a basis for refining diagnostic gait analysis

The variability of movement plays a crucial role in shaping individual's gait pattern and could, therefore, potentially serve diagnostic purposes. Nevertheless, existing concepts for the use of variability in diagnosing gait present a challenge due to the lack of adequate benchmarks and methods for comparison. We assessed the individuality of contribution of foot parts that directly mediate the transmission of forces between the foot and the ground in body weight shifting during walking based on 200 pedobarometric measurements corresponding to the analysed foot parts for each of 19 individuals in a homogeneous study group. Our results show a degree of individualisation of the contribution of particular foot parts in the weight-shift high enough to justify the need to consider it in the diagnostic analysis. Furthermore they reveal noticeable, functionally driven differences between plantar areas most apparent between the lowest individuality for the first foot ray and the highest for second one and metatarsus. The diagnostic reference standard in pedobarometry should describe the contribution in the shift of body weight during walking for each area of the foot separately and include information on the intra-individual variation and individualisation of descriptors of the contribution. Such a comprehensive standard has the potential to increase the diagnostic value of pedobarometry through enrichment of the assessment description.

Non-Contact Assessment of Swallowing Dysfunction Using Smartphone Captured Skin Displacements

Early and accurate dysphagia diagnosis is essential for reducing the risk of associated co-morbidities and mortalities. Barriers to current evaluation methods may alter the effectiveness of identifying at-risk patients. This preliminary study evaluates the feasibility of using iPhone X-captured videos of swallowing as a non-contact dysphagia screening tool. Video recordings of the anterior and lateral necks were captured simultaneously with videofluoroscopy in dysphagic patients. Videos were analyzed using an image registration algorithm (phase-based Savitzky-Golay gradient correlation (P-SG-GC)) to determine skin displacements over hyolaryngeal regions. Biomechanical swallowing parameters of hyolaryngeal displacement and velocity were also measured. Swallowing safety and efficiency were assessed by the Penetration Aspiration Scale (PAS), Residue Severity Ratings (RSR), and the Normalized Residue Ratio Scale (NRRS). Anterior hyoid excursion and horizontal skin displacements were strongly correlated with swallows of a 20 mL bolus (r_s = 0.67). Skin displacements of the neck were moderately to very strongly correlated with scores on the PAS (r_s = 0.80), NRRS (r_s = 0.41-0.62), and RSR (r_s = 0.33). This is the first study to utilize smartphone technology and image registration methods to produce skin displacements indicating post-swallow residual and penetration-aspiration. Enhancing screening methods provides a greater chance of detecting dysphagia, reducing the risk of negative health impacts.

Heteroclinic cycling and extinction in May-Leonard models with demographic stochasticity

May and Leonard (SIAM J Appl Math 29:243-253, 1975) introduced a three-species Lotka-Volterra type population model that exhibits heteroclinic cycling. Rather than producing a periodic limit cycle, the trajectory takes longer and longer to complete each "cycle", passing closer and closer to unstable fixed points in which one population dominates and the others approach zero. Aperiodic heteroclinic dynamics have subsequently been studied in ecological systems (side-blotched lizards; colicinogenic Escherichia coli), in the immune system, in neural information processing models ("winnerless competition"), and in models of neural central pattern generators. Yet as May and Leonard observed "Biologically, the behavior (produced by the model) is nonsense. Once it is conceded that the variables represent animals, and therefore cannot fall below unity, it is clear that the system will, after a few cycles, converge on some single population, extinguishing the other two." Here, we explore different ways of introducing discrete stochastic dynamics based on May and Leonard's ODE model, with application to ecological population dynamics, and to a neuromotor central pattern generator system. We study examples of several quantitatively distinct asymptotic behaviors, including total extinction of all species, extinction to a single species, and persistent cyclic dominance with finite mean cycle length.

Biomechanical and Sensory Feedback Regularize the Behavior of Different Locomotor Central Pattern Generators

This work presents an in-depth numerical investigation into a hypothesized two-layer central pattern generator (CPG) that controls mammalian walking and how different parameter choices might affect the stepping of a simulated neuromechanical model. Particular attention is paid to the functional role of features that have not received a great deal of attention in previous work: the weak cross-excitatory connectivity within the rhythm generator and the synapse strength between the two layers. Sensitivity evaluations of deafferented CPG models and the combined neuromechanical model are performed. Locomotion frequency is increased in two different ways for both models to investigate whether the model's stability can be predicted by trends in the CPG's phase response curves (PRCs). Our results show that the weak cross-excitatory connection can make the CPG more sensitive to perturbations and that increasing the synaptic strength between the two layers results in a trade-off between forced phase locking and the amount of phase delay that can exist between the two layers. Additionally, although the models exhibit these differences in behavior when disconnected from the biomechanical model, these differences seem to disappear with the full neuromechanical model and result in similar behavior despite a variety of parameter combinations. This indicates that the neural variables do not have to be fixed precisely for stable walking; the biomechanical entrainment and sensory feedback may cancel out the strengths of excitatory connectivity in the neural circuit and play a critical role in shaping locomotor behavior. Our results support the importance of including biomechanical models in the development of computational neuroscience models that control mammalian locomotion.

Salience of multisensory feedback regulates behavioral variability

Many animal behaviors are robust to dramatic variations in morphophysiological features, both across and within individuals. The control strategies that animals use to achieve such robust behavioral performances are not known. Recent evidence suggests that animals rely on sensory feedback rather than precise tuning of neural controllers for robust control. Here we examine the structure of sensory feedback, including multisensory feedback, for robust control of animal behavior. We re-examined two recent datasets of refuge tracking responses ofEigenmannia virescens, a species of weakly electric fish.Eigenmanniarely on both the visual and electrosensory cues to track the position of a moving refuge. The datasets include experiments that varied the strength of visual and electrosensory signals. Our analyses show that increasing the salience (perceptibility) of visual or electrosensory signals resulted in more robust and precise behavioral responses. Further, we find that robust performance was enhanced by multisensory integration of simultaneous visual and electrosensory cues. These findings suggest that engineers may achieve better system performance by improving the salience of multisensory feedback rather than solely focusing on precisely tuned controllers.

Organelle calcium-derived voltage oscillations in pacemaker neurons drive the motor program for food-seeking behavior in Aplysia

The expression of motivated behaviors depends on both external and internally arising neural stimuli, yet the intrinsic releasing mechanisms for such variably occurring behaviors remain elusive. In isolated nervous system preparations of Aplysia, we have found that irregularly expressed cycles of motor output underlying food-seeking behavior arise from regular membrane potential oscillations of varying magnitude in an identified pair of interneurons (B63) in the bilateral buccal ganglia. This rhythmic signal, which is specific to the B63 cells, is generated by organelle-derived intracellular calcium fluxes that activate voltage-independent plasma membrane channels. The resulting voltage oscillation spreads throughout a subset of gap junction-coupled buccal network neurons and by triggering plateau potential-mediated bursts in B63, can initiate motor output driving food-seeking action. Thus, an atypical neuronal pacemaker mechanism, based on rhythmic intracellular calcium store release and intercellular propagation, can act as an autonomous intrinsic releaser for the occurrence of a motivated behavior.

The brain as a dynamically active organ

Nervous systems are typically described as static networks passively responding to external stimuli (i.e., the 'sensorimotor hypothesis'). However, for more than a century now, evidence has been accumulating that this passive-static perspective is wrong. Instead, evidence suggests that nervous systems dynamically change their connectivity and actively generate behavior so their owners can achieve goals in the world, some of which involve controlling their sensory feedback. This review provides a brief overview of the different historical perspectives on general brain function and details some select modern examples falsifying the sensorimotor hypothesis.

Control for multifunctionality: bioinspired control based on feeding in Aplysia californica

Animals exhibit remarkable feats of behavioral flexibility and multifunctional control that remain challenging for robotic systems. The neural and morphological basis of multifunctionality in animals can provide a source of bioinspiration for robotic controllers. However, many existing approaches to modeling biological neural networks rely on computationally expensive models and tend to focus solely on the nervous system, often neglecting the biomechanics of the periphery. As a consequence, while these models are excellent tools for neuroscience, they fail to predict functional behavior in real time, which is a critical capability for robotic control. To meet the need for real-time multifunctional control, we have developed a hybrid Boolean model framework capable of modeling neural bursting activity and simple biomechanics at speeds faster than real time. Using this approach, we present a multifunctional model of Aplysia californica feeding that qualitatively reproduces three key feeding behaviors (biting, swallowing, and rejection), demonstrates behavioral switching in response to external sensory cues, and incorporates both known neural connectivity and a simple bioinspired mechanical model of the feeding apparatus. We demonstrate that the model can be used for formulating testable hypotheses and discuss the implications of this approach for robotic control and neuroscience.

Rapid Adaptation to Changing Mechanical Load by Ordered Recruitment of Identified Motor Neurons

As they interact with their environment and encounter challenges, animals adjust their behavior on a moment-to-moment basis to maintain task fitness. This dynamic process of adaptive motor control occurs in the nervous system, but an understanding of the biomechanics of the body is essential to properly interpret the behavioral outcomes. To study how animals respond to changing task conditions, we used a model system in which the functional roles of identified neurons and the relevant biomechanics are well understood and can be studied in intact behaving animals: feeding in the marine mollusc Aplysia We monitored the motor neuronal output of the feeding circuitry as intact animals fed on uniform food stimuli under unloaded and loaded conditions, and we measured the force of retraction during loaded swallows. We observed a previously undescribed pattern of force generation, which can be explained within the appropriate biomechanical context by the activity of just a few key, identified motor neurons. We show that, when encountering load, animals recruit identified retractor muscle motor neurons for longer and at higher frequency to increase retraction force duration. Our results identify a mode by which animals robustly adjust behavior to their environment, which is experimentally tractable to further mechanistic investigation.

Individuals have unique muscle activation signatures as revealed during gait and pedaling

Although it is known that the muscle activation patterns used to produce even simple movements can vary between individuals, these differences have not been considered to prove the existence of individual muscle activation strategies (or signatures). We used a machine learning approach (support vector machine) to test the hypothesis that each individual has unique muscle activation signatures. Eighty participants performed a series of pedaling and gait tasks, and 53 of these participants performed a second experimental session on a subsequent day. Myoelectrical activity was measured from eight muscles: vastus lateralis and medialis, rectus femoris, gastrocnemius lateralis and medialis, soleus, tibialis anterior, and biceps femoris -long head. The classification task involved separating data into training and testing sets. For the within-day classification, each pedaling/gait cycle was tested using the classifier, which had been trained on the remaining cycles. For the between-day classification, each cycle from day 2 was tested using the classifier, which had been trained on the cycles from day 1. When considering all eight muscles, the activation profiles were assigned to the corresponding individuals with a classification rate of up to 99.28% (2,353/2,370 cycles) and 91.22% (1,341/1,470 cycles) for the within-day and between-day classification, respectively. When considering the within-day classification, a combination of two muscles was sufficient to obtain a classification rate >80% for both pedaling and gait. When considering between-day classification, a combination of four to five muscles was sufficient to obtain a classification rate >80% for pedaling and gait. These results demonstrate that strategies not only vary between individuals, as is often assumed, but are unique to each individual.

NEW & NOTEWORTHY We used a machine learning approach to test the uniqueness and robustness of muscle activation patterns. We considered that, if an algorithm can accurately identify participants, one can conclude that these participants exhibit discernible differences and thus have unique muscle activation signatures. Our results show that activation patterns not only vary between individuals, but are unique to each individual. Individual differences should, therefore, be considered relevant information for addressing fundamental questions about the control of movement.

Output variability across animals and levels in a motor system

Rhythmic behaviors vary across individuals. We investigated the sources of this output variability across a motor system, from the central pattern generator (CPG) to the motor plant. In the bilaterally symmetric leech heartbeat system, the CPG orchestrates two coordinations in the bilateral hearts with different intersegmental phase relations (Δϕ) and periodic side-to-side switches. Population variability is large. We show that the system is precise within a coordination, that differences in repetitions of a coordination contribute little to population output variability, but that differences between bilaterally homologous cells may contribute to some of this variability. Nevertheless, much output variability is likely associated with genetic and life history differences among individuals. Variability of Δϕ were coordination-specific: similar at all levels in one, but significantly lower for the motor pattern than the CPG pattern in the other. Mechanisms that transform CPG output to motor neurons may limit output variability in the motor pattern.

Neuromodulatory Regulation of Behavioral Individuality in Zebrafish

Inter-individual behavioral variation is thought to increase fitness and aid adaptation to environmental change, but the underlying mechanisms are poorly understood. We find that variation between individuals in neuromodulatory input contributes to individuality in short-term habituation of the zebrafish (Danio Rerio) acoustic startle response (ASR). ASR habituation varies greatly between individuals, but differences are stable over days and are heritable. Acoustic stimuli that activate ASR-command Mauthner cells also activate dorsal raphe nucleus (DRN) serotonergic neurons, which project to the vicinity of the Mauthner cells and their inputs. DRN neuron activity decreases during habituation in proportion to habituation and a genetic manipulation that reduces serotonin content in DRN neurons increases habituation, whereas serotonergic agonism or DRN activation with ChR2 reduces habituation. Finally, level of rundown of DRN activity co-segregates with extent of behavioral habituation across generations. Thus, variation between individuals in neuromodulatory input contributes to individuality in a core adaptive behavior. VIDEO ABSTRACT.

Sensory-Motor Integration: More Variability Reduces Individuality

Motor neural networks and muscles produce identifiably common outputs, such as a trot or gallop, despite varations in intrinsic properties across individuals. New work shows that sensory input can induce the requisite decrease in across-individual variability even as it increases within-individual variability.