Search bioRxiv⌕ Search

Biology subjects

Berman, G. J.

Publications and source records attributed to Berman, G. J..

14 recordsLinked to original sources

Using timescale as a state coordinate reveals the metastable geometry of behavior

Animal behavior unfolds across many timescales, from fast movement patterns to slow changes in internal states such as hunger, arousal, and circadian phase. These slow variables are rarely measured directly and must instead be inferred from their effects on the faster movements that can be observed. Here we propose treating timescale itself as an explicit coordinate of the state representation, constructing a time-frequency state space where fast movements and slow modulations appear simultaneously. We find that slow modes emerge as linear arms radiating from a stationary-weighted hub in the leading non-trivial eigenvectors of the transfer operator, with one arm per metastable basin across three systems of increasing complexity. In a synthetic system, the framework recovers a hidden bistable driver across nearly three decades of dwell time, while a fixed-timescale analysis of the same trajectory finds no separable slow modes. In nematode locomotion, it reproduces the canonical run-pirouette organization. In freely moving fruit flies, where fast leg kinematics are orders of magnitude faster than the behavioral states they compose, the multi-timescale operator identifies four metastable behavioral basins directly from the postural time series, without first decomposing into a sequence of stereotyped actions. We further find that these basins exhibit a broad, heavy-tailed distribution of residence times. Treating timescale as a state coordinate thus exposes a predictable geometric form for the slow organization of behavior, providing a general route for extracting collective modes from partially observed biological time series without first organizing the dynamics into discrete events.

animal behavior and cognition↗

Natural variation in the oxytocin receptor gene predicts social observation in female prairie voles

Genetic variation in the oxytocin receptor gene (Oxtr) has been linked to differences in brain OXTR expression and long-term social bonds, but whether it shapes the moment-to-moment dynamics of early social interactions is unclear. Here we examined how the intronic Oxtr single nucleotide polymorphism (SNP) NT213739 in female prairie voles (Microtus ochrogaster) shapes their dynamic social interactions with an opposite-sex conspecific. Leveraging a computational pipeline to analyze the movements of freely interacting voles, we found that C/C females, which expressed higher OXTR levels in the nucleus accumbens than T/T females, spent more time socially observing novel males from a distance, especially early in interactions. This genotype-phenotype relationship persisted in multiple contexts, including the social preference test. Thus, natural Oxtr variation biases social observation in females toward unfamiliar males before bonds form, consistent with models where accumbens OXTR enhances the salience of social cues. These findings show that SNPs can shape subtle behavioral dimensions in early social encounters, with important implications for the role of oxytocin in the study of social attachment.

neuroscience↗

The Intermediate CA1 and CA3 Subfields of the Hippocampus Differentially Encode Social Recognition

Social recognition requires linking information about others to the spatial contexts in which they are encountered, a computation thought to engage the hippocampus. Although this process depends on coordinated activity across the hippocampal network, how specific subfields differentially contribute to social recognition remains unclear. Of particular interest are the intermediate CA1 (iCA1) and CA3 (iCA3) subfields, which have both been shown to be causally involved in social recognition. Historically, the dense feed-forward projections from CA3 to CA1 led to the view that these subfields form a hierarchical circuit, with CA1 primarily integrating and refining computations from CA3. However, recent causal manipulations of the iCA1 and iCA3 have revealed distinct and separable contributions to approach and avoidance behavior, suggesting functional specialization across the subfields. This functional divergence raises the possibility that iCA1 and iCA3 might differentially encode social recognition information. To test this possibility, we performed one-photon calcium imaging in freely behaving mice to record endogenous activity from the iCA1 and iCA3 during a novel behavioral paradigm that disentangles social identity from spatial position. Notably, we found that iCA3 neurons more robustly discriminated between novel and familiar conspecifics, while iCA1 neurons more strongly encoded spatial position. These results identify the iCA3 as a key locus for social novelty encoding and suggest a functional division of labor within the hippocampus, in which stable spatial frameworks in the iCA1 anchor flexible, context-dependent social representations in the iCA3 that support adaptive social memory.

neuroscience↗

Gait signature changes with walking speed are similar among able-bodied young adults despite persistent individual-specific differences

Understanding individuals distinct movement patterns is crucial for health, rehabilitation, and sports. Recently, we developed a machine learning-based framework to show that "gait signatures" describing the neuromechanical dynamics governing able-bodied and post-stroke gait kinematics remain individual-specific across speeds. However, we only evaluated gait signatures within a limited speed range and number of participants, using only sagittal plane (i.e., 2D) joint angles. Here we characterized changes in gait signatures across a wide range of speeds, from very slow (0.3 m/s) to exceptionally fast (above the walk-to-run transition speed) in 17 able-bodied young adults. We further assessed whether 3D kinematic and/or kinetic (ground reaction forces, joint moments, and powers) data would improve the discrimination of gait signatures. Our study showed that gait signatures remained individual-specific across walking speeds: Notably, 3D kinematic signatures achieved exceptional accuracy (99.8%, confidence interval (CI): 99.1-100%) in classifying individuals, surpassing both 2D kinematics and 3D kinetics. Moreover, participants exhibited consistent, predictable linear changes in their gait signatures across the entire speed range. These changes were associated with participants preferred walking speeds, balance ability, cadence, and step length. These findings support gait signatures as a tool to characterize individual differences in gait and predict speed-induced changes in gait dynamics.

systems biology↗

Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics

Locomotion results from the interactions of highly nonlinear neural and biomechanical dynamics. Accordingly, understanding gait dynamics across behavioral conditions and individuals based on detailed modeling of the underlying neuromechanical system has proven difficult. Here, we develop a data-driven and generative modeling approach that recapitulates the dynamical features of gait behaviors to enable more holistic and interpretable characterizations and comparisons of gait dynamics. Specifically, gait dynamics of multiple individuals are predicted by a dynamical model that defines a common, low-dimensional, latent space to compare group and individual differences. We find that highly individualized dynamics - i.e., gait signatures - for healthy older adults and stroke survivors during treadmill walking are conserved across gait speed. Gait signatures further reveal individual differences in gait dynamics, even in individuals with similar functional deficits. Moreover, components of gait signatures can be biomechanically interpreted and manipulated to reveal their relationships to observed spatiotemporal joint coordination patterns. Lastly, the gait dynamics model can predict the time evolution of joint coordination based on an initial static posture. Our gait signatures framework thus provides a generalizable, holistic method for characterizing and predicting cyclic, dynamical motor behavior that may generalize across species, pathologies, and gait perturbations. Author SummaryIn this manuscript, we introduce a novel, machine learning-based framework for quantifying, characterizing, and modifying the underlying neuromechanical dynamics that drive unique gait patterns. Standard methods for evaluating movement typically focus on extracting discrete gait variables ignoring the complex inter-limb and inter-joint spatiotemporal dependencies that occur during gait. Popular physiologically realistic modeling approaches encode these spatiotemporal dependencies but are too complex to characterize individual differences in the factors driving unique gait patterns or disorders. To circumvent these modeling complications, we develop a phenomenological model of gait that enables more holistic and interpretable characterizations of gait, encoding these complex spatiotemporal dependencies between humans joint angles arising from joint neural and biomechanical constraints. Our coined gait signature framework provides a path towards understanding the neuromechanics of locomotion. This framework has potential utility for clinical researchers prescribing individualized therapies for pathologies or biomechanists interested in animal locomotion or other periodic movements assessed across different pathologies, neural perturbations, and or conditions.

neuroscience↗

Driving singing behaviour in songbirds using multi-modal, multi-agent virtual reality

Interactive biorobotics provides unique experimental potential to study the mechanisms underlying social communication but is limited by our ability to build expressive robots that exhibit the complex behaviours of birds and small mammals. An alternative to physical robots is to use virtual reality (VR). Here, we designed and built a modular, audio-visual virtual reality environment that allows online, multi-modal, multi-agent interaction for social communication. We tested this system in songbirds, which provide an exceptionally powerful and tractable model system to study social communication. We show that zebra finches (Taeniopygia guttata) communicating through the VR environment exhibit normal call timing behaviour, males sing female directed song and both males and females display high-intensity courtship behaviours to their mates. These results suggest that the VR system provides a sufficiently natural environment to elicit normal social communication behaviour. Furthermore, we developed a fully unsupervised online song motif detector and used it to manipulate the virtual social environment of male zebra finches based on the number of motifs sung. Our VR setup represents a first step in taking automatic behaviour annotation into the online domain and allows for animal-computer interaction using higher level behaviours such as song. Our unsupervised acoustic analysis eliminates the need for annotated training data thus reducing labour investment and experimenter bias.

animal behavior and cognition↗

Modeling bottlenecks, modularity, and context-dependency in behavioral control

In almost all animals, the transfer of information from the brain to the motor circuitry is facilitated by a relatively small number of neurons, leading to a constraint on the amount of information that can be transmitted. Our knowledge of how animals encode information through this pathway, and the consequences of this encoding, however, is limited. In this study, we use a simple feed-forward neural network to investigate the consequences of having such a bottleneck and identify aspects of the network architecture that enable robust information transfer. We are able to explain some recently observed properties of descending neurons - that they exhibit a modular pattern of connectivity and that their excitation leads to consistent alterations in behavior that are often dependent upon the prior behavioral state (context-dependency). Our model predicts that in the presence of an information bottleneck, such a modular structure is needed to increase the efficiency of the network and to make it more robust to perturbations. However, it does so at the cost of an increase in context-dependency. Despite its simplicity, our model is able to provide intuition for the trade-offs faced by the nervous system in the presence of an information processing constraint and makes predictions for future experiments.

animal behavior and cognition↗

Social experience alters oxytocinergic modulation in the nucleus accumbens of female prairie voles

Social relationships are dynamic and evolve with shared and personal experiences. Whether the functional role of social neuromodulators also evolves with experience to shape the trajectory of relationships is unknown. We utilized pair bonding in the socially monogamous prairie voles as an example of socio-sexual experience that dramatically alters behaviors displayed toward other individuals. We investigated oxytocin-dependent modulation of excitatory synaptic transmission in the nucleus accumbens as a function of pair bonding status. We found that an oxytocin receptor agonist decreases the amplitude of spontaneous Excitatory Postsynaptic Currents (EPSCs) in sexually naive virgin, but not pair-bonded, female voles, while it increases the amplitude of electrically evoked EPSCs in paired voles, but not in virgins. This oxytocin-dependent potentiation of synaptic transmission relies on the de novo coupling between oxytocin receptor signaling and endocannabinoid CB1 receptor signaling in pair bonded voles. Blocking CB1 receptors after pair bond formation increases the occurrence of a specific form of social rejection - defensive upright response - that is displayed towards the partner but not towards a novel individual. Altogether, our results demonstrate that oxytocins action in the nucleus accumbens is changed through social experience in a way that regulates the trajectory of social interactions as the relationship with the partner unfolds, potentially promoting the maintenance of a pair bond by inhibiting aggressive responses. These results provide a mechanism by which social experience and context shift oxytocinergic signaling to impact neural and behavioral responses to social cues.

neuroscience↗

Measuring the repertoire of age-related behavioral changes in Drosophila melanogaster

Aging affects almost all aspects of an organism - its morphology, its physiology, its behavior. Isolating which biological mechanisms are regulating these changes, however, has proven difficult, potentially due to our inability to characterize the full repertoire of an animals behavior across the lifespan. Using data from fruit flies (D. melanogaster) we measure the full repertoire of behaviors as a function of age. We observe a sexually dimorphic pattern of changes in the behavioral repertoire during aging. Although the stereotypy of the behaviors and the complexity of the repertoire overall remains relatively unchanged, we find evidence that the observed alterations in behavior can be explained by changing the flys overall energy budget, suggesting potential connections between metabolism, aging, and behavior.

animal behavior and cognition↗

A framework for studying behavioral evolution by reconstructing ancestral repertoires

Although extensive behavioral changes often exist between closely related animal species, our understanding of the genetic basis underlying the evolution of behavior has remained limited. Here, we propose a new framework to study behavioral evolution by computational estimation of ancestral behavioral repertoires. We measured the behaviors of individuals from six species of fruit flies using unsupervised techniques and identified suites of stereotyped movements exhibited by each species. We then fit a Generalized Linear Mixed Model to estimate the suites of behaviors exhibited by ancestral species, as well as the intra- and inter-species behavioral covariances. We found that much of intraspecific behavioral variation is explained by differences between individuals in the status of their behavioral hidden states, what might be called their "mood." Lastly, we propose a method to identify groups of behaviors that appear to have evolved together, illustrating how sets of behaviors, rather than individual behaviors, likely evolved. Our approach provides a new framework for identifying co-evolving behaviors and may provide new opportunities to study the genetic basis of behavioral evolution.

animal behavior and cognition↗

Application of the hierarchical bootstrap to multi-level data in neuroscience

AO_SCPLOWBSTRACTC_SCPLOWA common feature in many neuroscience datasets is the presence of hierarchical data structures, most commonly recording the activity of multiple neurons in multiple animals across multiple trials. Accordingly, the measurements constituting the dataset are not independent, even though the traditional statistical analyses often applied in such cases (e.g. Students t-test) treat them as such. The hierarchical bootstrap has been shown to be an effective tool to accurately analyze such data and while it has been used extensively in the statistical literature, its use is not widespread in neuroscience - despite the ubiquity of hierarchical datasets. In this paper, we illustrate the intuitiveness and utility of this approach to analyze hierarchically nested datasets. We use simulated neural data to show that traditional statistical tests can result in a false positive rate of over 45%, even if the Type-I error rate is set at 5%. While summarizing data across non-independent points (or lower levels) can potentially fix this problem, this approach greatly reduces the statistical power of the analysis. The hierarchical bootstrap, when applied sequentially over the levels of the hierarchical structure, keeps the Type-I error rate within the intended bound and retains more statistical power than summarizing methods. We conclude by demonstrating the effectiveness of the method in two real-world examples, first analyzing singing data in male Bengalese finches (Lonchura striata var. domestica) and second quantifying changes in behavior under optogenetic control in flies (Drosophila melanogaster).

neuroscience↗

Dopamine depletion affects vocal acoustics and disrupts sensorimotor adaptation in songbirds

Dopamine is hypothesized to convey important error information in reinforcement learning tasks with explicit appetitive or aversive cues. However, during motor skill learning the only available feedback signal is typically an animals evaluation of the sensory feedback arising from its own behavior, rather than any external reward or punishment. It has previously been shown that intact dopaminergic signaling from the ventral tegmental area - substantia nigra compacta complex (VTA/SNc) is necessary for vocal learning in response to an external aversive auditory cue in songbirds. However, the role of dopamine in learning in the absence of explicit external cues is still unclear. Here we used male Bengalese finches (Lonchura striata var. domestica) to test the hypothesis that dopamine signaling is necessary for self-evaluation driven sensorimotor learning. We combined 6-hydroxydopamine (6-OHDA) lesions of dopaminergic terminals within Area X, a songbird basal ganglia nucleus critical for vocal learning, with a headphones learning paradigm that shifted the birds auditory feedback and compared their learning to birds without lesions. We found that 6-OHDA lesions affected song behavior in two ways. First, over a period of days lesioned birds systemically lowered their pitch regardless of the presence or absence of auditory errors. Second, 6-OHDA lesioned birds also displayed severe deficits in sensorimotor learning as measured by their adaptive change in pitch in response to the pitch-shifted auditory error. Our results suggest roles for dopamine both in motor production and in auditory error processing during vocal learning.\n\nSignificance StatementDopamine has been hypothesized to convey a reward prediction error signal in learning tasks involving external reinforcement. However the role dopamine plays in tasks involving self-guided error correction in the absence of external reinforcement is much less clear. To address this question, we studied the role of dopamine in sensorimotor adaptation using male Bengalese finches, which spontaneously produce a complex motor behavior (song) and are capable of modulating their behavioral output in response to induced auditory errors. Our results reveal that in addition to conveying errors in motor performance, dopamine may also have a role in modulating effort and in choosing a corrective response to the auditory error.

neuroscience↗

Neural Changes Underlying Rapid Fly Song Evolution

The neural basis for behavioural evolution is poorly understood. Functional comparisons of homologous neurons may reveal how neural circuitry contributes to behavioural evolution, but homologous neurons cannot be identified and manipulated in most taxa. Here, we compare the function of homologous courtship song neurons by exporting neurogenetic reagents that label identified neurons in Drosophila melanogaster to D. yakuba. We found a conserved role for a cluster of brain neurons that establish a persistent courtship state. In contrast, a descending neuron with conserved electrophysiological properties drives different song types in each species. Our results suggest that song evolved, in part, due to changes in the neural circuitry downstream of this descending neuron. This experimental approach can be generalized to other neural circuits and therefore provides an experimental framework for studying how the nervous system has evolved to generate behavioural diversity.

evolutionary biology↗

Optogenetic dissection of descending behavioral control in Drosophila

In most animals, the brain makes behavioral decisions that are transmitted by descending neurons to the nerve cord circuitry that produces behaviors. In insects, only a few descending neurons have been associated with specific behaviors. To explore how these neurons control an insects movements, we developed a novel method to systematically assay the behavioral effects of activating individual neurons on freely behaving terrestrial D. melanogaster. We calculated a two-dimensional representation of the entire behavior space explored by these flies and associated descending neurons with specific behaviors by identifying regions of this space that were visited with increased frequency during optogenetic activation. Applying this approach across a population of descending neurons, we found, that (1) activation of most of the descending neurons drove stereotyped behaviors, (2) in many cases multiple descending neurons activated similar behaviors, and (3) optogenetically-activated behaviors were often dependent on the behavioral state prior to activation.

neuroscience↗