Social behavior in context: Hormonal modulation of behavioral plasticity and social competence

In social species animals should fine-tune the expression of their social behavior to social environments in order to avoid the costs of engaging in costly social interactions. Therefore, social competence, defined as the ability of an animal to optimize the expression of its social behavior as a function of the available social information, should be considered as a performance trait that impacts on the Darwinian fitness of the animal. Social competence is based on behavioral plasticity which, in turn, can be achieved by different neural mechanisms of plasticity, namely by rewiring or by biochemically switching nodes of a putative neural network underlying social behavior. Since steroid hormones respond to social interactions and have receptors extensively expressed in the social behavioral neural network, it is proposed that steroids play a key role in the hormonal modulation of social plasticity. Here, we propose a reciprocal model for the action of androgens on short-term behavioral plasticity and review a set of studies conducted in our laboratory using an African cichlid fish (Oreochromis mossambicus) that provide support for it. Androgens are shown to be implicated as physiological mediators in a wide range of social phenomena that promote social competence, namely by adjusting the behavioral response to the nature of the intruder and the presence of third parties (dear enemy and audience effects), by anticipating territorial intrusions (bystander effect and conditioning of the territorial response), and by modifying future behavior according to prior experience of winning (winner effect). The rapid behavioral actions of socially induced short-term transient changes in androgens indicate that these effects are most likely mediated by nongenomic mechanisms. The fact that the modulation of rapid changes in behavior is open to the influence of circulating levels of androgens, and is not exclusively achieved by changes in central neuromodulators, suggests functional relevance of integrating body parameters in the behavioral response. Thus, the traditional view of seeing neural circuits as unique causal agents of behavior should be updated to a brain-body-environment perspective, in which these neural circuits are embodied and the behavioral performance (and outcomes as fitness) depends on a dynamic relationship between the different levels. In this view hormones play a major role as behavioral modulators.     

Deconstructing memory in Drosophila

Unlike most organ systems, which have evolved to maintain homeostasis, the brain has been selected to sense and adapt to environmental stimuli by constantly altering interactions in a gene network that functions within a larger neural network. This unique feature of the central nervous system provides a remarkable plasticity of behavior, but also makes experimental investigations challenging. Each experimental intervention ramifies through both gene and neural networks, resulting in unpredicted and sometimes confusing phenotypic adaptations. Experimental dissection of mechanisms underlying behavioral plasticity ultimately must accomplish an integration across many levels of biological organization, including genetic pathways acting within individual neurons, neural network interactions which feed back to gene function, and phenotypic observations at the behavioral level. This dissection will be more easily accomplished for model systems such as Drosophila, which, compared with mammals, have relatively simple and manipulable nervous systems and genomes. The evolutionary conservation of behavioral phenotype and the underlying gene function ensures that much of what we learn in such model systems will be relevant to human cognition. In this essay, we have not attempted to review the entire Drosophila memory field. Instead, we have tried to discuss particular findings that provide some level of intellectual synthesis across three levels of biological organization: behavior, neural circuitry and biochemical pathways. We have attempted to use this integrative approach to evaluate distinct mechanistic hypotheses, and to propose critical experiments that will advance this field.     

Behavioral,neural and cellular components underlying olfactory learning in the honeybee

A top-down approach as applied to learning and memory in honeybees provides the opportunity of relating different levels of complexity to each other, and of analyzing the rules and mechanisms from the viewpoint of the respective next higher level. Olfactory conditioning of harnessed bees exemplifies essential elements of associative learning and, in general, forms a bridge between the systems and the cellular levels of analysis. Intracellular recordings of identified neurons during olfactory conditioning play a key role in this effort. They allow testing of the assumptions made by modern behavioral theories of associative learning and provide access to cellular and molecular studies, owing to the identification of their transmitters and the peculiarities of the connectivities. Analysis at this intermediate level of complexity is particularly profitable in the bee, because essential neural elements of the associative network are known and can be tested during ongoing learning behavior. In this respect, the honeybee offers unique properties for the building of bridges between the molecular, cellular neuronal, network and behavioral levels of associative learning.     

An observational learning task using Barnes maze in rats

Observational learning, which modulates one’s own behavior by observing the adaptive behavior of others, is crucial for behaving efficiently in social communities. Although many behavioral experiments have reported observational learning in monkeys and humans, its neural mechanisms are still unknown. In order to conduct neuroscientific researches with recording neural activities, we developed an observational learning task for rats. We designed the task using Barnes circular maze and then tested whether rats (observers) could actually improve their learning by observing the behavior of other rats (models) that had already acquired the task. The result showed that the observer rats, which were located in a metal wire mesh cylinder at the center of the maze and allowed to observe model rats escaping to the goal in the maze, demonstrated significantly faster escape behavior than the model rats. Thus, the present study confirmed that rats can efficiently learn the behavioral task by observing the behavior of other rats; this shows that it is conceivable to elucidate the neural mechanisms of social interaction by analyzing neural activity in observer rats performing the observational learning task.     

Techniques for long-term multisite neuronal ensemble recordings in behaving animals.

Advances in our understanding of neural systems will go hand in hand with improvements in the experimental techniques used to study these systems. This article describes a series of methodological developments aimed at enhancing the power of the methods needed to record simultaneously from populations of neurons over broad regions of the brain in awake, behaving animals. First, our laboratory has made many advances in electrode design, including movable bundle and array electrodes and smaller electrode assemblies. Second, to perform longer and more complex multielectrode implantation surgeries in primates, we have modified our surgical procedures by employing comprehensive physiological monitoring akin to human neuroanesthesia. We have also developed surgical implantation techniques aimed at minimizing brain tissue damage and facilitating penetration of the cortical surface. Third, we have integrated new technologies into our neural ensemble, stimulus and behavioral recording experiments to provide more detailed measurements of experimental variables. Finally, new data analytical techniques are being used in the laboratory to analyze increasingly large quantities of data.     

Functional genomics of neural and behavioral plasticity

How does the environment, particularly the social environment, influence brain and behavior and what are the underlying physiologic, molecular, and genetic mechanisms? Adaptations of brain and behavior to changes in the social or physical environment are common in the animal world, either as short-term (i.e., modulatory) or as long-term modifications (e.g., via gene expression changes) in behavioral or physiologic properties. The study of the mechanisms and constraints underlying these dynamic changes requires model systems that offer plastic phenotypes as well as a sufficient level of quantifiable behavioral complexity while being accessible at the physiological and molecular level. In this article, I explore how the new field of functional genomics can contribute to an understanding of the complex relationship between genome and environment that results in highly plastic phenotypes. This approach will lead to the discovery of genes under environmental control and provide the basis for the study of the interrelationship between an individual's gene expression profile and its social phenotype in a given environmental context.     

Adaptation supports short-term memory in a visual change detection task

The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task. Mice were trained to respond to changes in a repeated sequence of natural images while neural activity was recorded using two-photon calcium imaging. We also trained two types of artificial neural networks on the same change detection task as the mice. Following fixed pre-processing using a pretrained convolutional neural network, either a recurrent neural network (RNN) or a feedforward neural network with short-term synaptic depression (STPNet) was trained to the same level of performance as the mice. While both networks are able to learn the task, the STPNet model contains units whose activity are more similar to the in vivo data and produces errors which are more similar to the mice. When images are omitted, an unexpected perturbation which was absent during training, mice often do not respond to the omission but are more likely to respond to the subsequent image. Unlike the RNN model, STPNet produces a similar pattern of behavior. These results suggest that simple neural adaptation mechanisms may serve as an important bottom-up memory signal in this task, which can be used by downstream areas in the decision-making process.     

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental paradigms and analysis tools have been limited to constrained laboratory tasks and contexts due to technical limitations imposed by the available set of measuring and analysis techniques and the age of the subjects. These limitations severely limit the study of developmental neural dynamics and associated neural networks engaged in cognition, perception and action in infants performing “in action and in context”. This protocol presents a novel approach to study infants and young children as they freely organize their own behavior, and its consequences in a complex, partly unpredictable and highly dynamic environment. The proposed methodology integrates synchronized high-density active scalp electroencephalography (EEG), inertial measurement units (IMUs), video recording and behavioral analysis to capture brain activity and movement non-invasively in freely-behaving infants. This setup allows for the study of neural network dynamics in the developing brain, in action and context, as these networks are recruited during goal-oriented, exploration and social interaction tasks.     

Representational drift as a window into neural and behavioural plasticity

Large-scale recordings of neural activity over days and weeks have revealed that neural representations of familiar tasks, precepts and actions continually evolve without obvious changes in behaviour. We hypothesise that this steady drift in neural activity and accompanying physiological changes is due in part to the continuous application of a learning rule at the cellular and population level. Explicit predictions of this drift can be found in neural network models that use iterative learning to optimise weights. Drift therefore provides a measurable signal that can reveal systems–level properties of biological plasticity mechanisms, such as their precision and effective learning rates.     

Behavioural and neurophysiological evidence for face identity and face emotion processing in animals

Visual cues from faces provide important social information relating to individual identity, sexual attraction and emotional state. Behavioural and neurophysiological studies on both monkeys and sheep have shown that specialized skills and neural systems for processing these complex cues to guide behaviour have evolved in a number of mammals and are not present exclusively in humans. Indeed, there are remarkable similarities in the ways that faces are processed by the brain in humans and other mammalian species. While human studies with brain imaging and gross neurophysiological recording approaches have revealed global aspects of the face-processing network, they cannot investigate how information is encoded by specific neural networks. Single neuron electrophysiological recording approaches in both monkeys and sheep have, however, provided some insights into the neural encoding principles involved and, particularly, the presence of a remarkable degree of high-level encoding even at the level of a specific face. Recent developments that allow simultaneous recordings to be made from many hundreds of individual neurons are also beginning to reveal evidence for global aspects of a population-based code. This review will summarize what we have learned so far from these animal-based studies about the way the mammalian brain processes the faces and the emotions they can communicate, as well as associated capacities such as how identity and emotion cues are dissociated and how face imagery might be generated. It will also try to highlight what questions and advances in knowledge still challenge us in order to provide a complete understanding of just how brain networks perform this complex and important social recognition task.     

Social experience organizes parallel networks in sensory and limbic forebrain

Successful social behavior can directly influence an individual's reproductive success. Therefore, many organisms readily modify social behavior based on past experience. The neural changes induced by social experience, however, remain to be fully elucidated. We hypothesize that social modulation of neural systems not only occurs at the level of individual nuclei, but also of functional networks, and their relationships with behavior. We used the green anole lizard (Anolis carolinensis), which displays stereotyped, visually triggered social behaviors particularly suitable for comparisons of multiple functional networks in a social context, to test whether repeated aggressive interactions modify behavior and metabolic activity in limbic-hypothalamic and sensory forebrain regions, assessed by quantitative cytochrome oxidase (a slowly accumulating endogenous metabolic marker) histochemistry. We found that aggressive interactions potentiate aggressive behavior, induce changes in activities of individual nuclei, and organize context-specific functional neural networks. Surprisingly, this experiential effect is not only present in a limbic-hypothalamic network, but also extends to a sensory forebrain network directly relevant to the behavioral expression. Our results suggest that social experience modulates organisms' social behavior via modifying sensory and limbic neural systems in parallel both at the levels of individual regions and networks, potentially biasing perceptual as well as limbic processing.     

Dynamic social adaptation of motion-related neurons in primate parietal cortex

Social brain function, which allows us to adapt our behavior to social context, is poorly understood at the single-cell level due largely to technical limitations. But the questions involved are vital: How do neurons recognize and modulate their activity in response to social context? To probe the mechanisms involved, we developed a novel recording technique, called multi-dimensional recording, and applied it simultaneously in the left parietal cortices of two monkeys while they shared a common social space. When the monkeys sat near each other but did not interact, each monkey's parietal activity showed robust response preference to action by his own right arm and almost no response to action by the other's arm. But the preference was broken if social conflict emerged between the monkeys-specifically, if both were able to reach for the same food item placed on the table between them. Under these circumstances, parietal neurons started to show complex combinatorial responses to motion of self and other. Parietal cortex adapted its response properties in the social context by discarding and recruiting different neural populations. Our results suggest that parietal neurons can recognize social events in the environment linked with current social context and form part of a larger social brain network.     

Encoding time in neural dynamic regimes with distinct computational tradeoffs

Converging evidence suggests the brain encodes time in dynamic patterns of neural activity, including neural sequences, ramping activity, and complex dynamics. Most temporal tasks, however, require more than just encoding time, and can have distinct computational requirements including the need to exhibit temporal scaling, generalize to novel contexts, or robustness to noise. It is not known how neural circuits can encode time and satisfy distinct computational requirements, nor is it known whether similar patterns of neural activity at the population level can exhibit dramatically different computational or generalization properties. To begin to answer these questions, we trained RNNs on two timing tasks based on behavioral studies. The tasks had different input structures but required producing identically timed output patterns. Using a novel framework we quantified whether RNNs encoded two intervals using either of three different timing strategies: scaling, absolute, or stimulus-specific dynamics. We found that similar neural dynamic patterns at the level of single intervals, could exhibit fundamentally different properties, including, generalization, the connectivity structure of the trained networks, and the contribution of excitatory and inhibitory neurons. Critically, depending on the task structure RNNs were better suited for generalization or robustness to noise. Further analysis revealed different connection patterns underlying the different regimes. Our results predict that apparently similar neural dynamic patterns at the population level (e.g., neural sequences) can exhibit fundamentally different computational properties in regards to their ability to generalize to novel stimuli and their robustness to noise—and that these differences are associated with differences in network connectivity and distinct contributions of excitatory and inhibitory neurons. We also predict that the task structure used in different experimental studies accounts for some of the experimentally observed variability in how networks encode time.     

Understanding the neural control of ingestive behaviors: helping to separate cause from effect with dehydration-associated anorexia

Eating and drinking are motivated behaviors that are made up of coordinated sets of neuroendocrine, autonomic, and behavioral motor events. Although the spinal cord, hindbrain, and hypothalamus contain the motor neurons and circuitry sufficient to maintain the reflex parts of these motor events, inputs from the telencephalon are required to furnish the behavioral components with a motivated (goal-directed) character. Each of these motor events derives from the complex interaction of a variety of sensory inputs with groups of neural networks whose components are distributed throughout the brain and collectively support motor expression and coordination. At a first approximation based on a variety of data, these networks can be divided into three groups: networks that stimulate, those that inhibit, and those that disinhibit motor functions. A fourth contributor is the circadian timing signal that originates in the hypothalamic suprachiasmatic nucleus and provides the temporal anchor for the expression of all behaviors. This article discusses the nature of these networks using neuroanatomical (tract-tracing and neuropeptide in situ hybridization), endocrine, and behavioral evidence from a variety of experimental models. A persistent problem when studying the control of food intake from a neural systems perspective has been the difficulty in separating those neuronal changes that result in hunger from those that are as a consequence of eating. To address this problem, dehydration-associated anorexia is presented as a particularly useful experimental model because it can be used to distinguish between neural mechanisms underlying anorexia and those changes that occur as a consequence of anorexia. The article concludes by highlighting the potential role of neuropeptidergic action in the operation of these networks, using forebrain neuropeptidergic innervation of the parabrachial nucleus as an example.     

Steps during the development of the zebrafish locomotor network.

This review summarizes recent data from our lab concerning the development of motor activities in the developing zebrafish. The zebrafish is a leading model for studies of vertebrate development because one can obtain a large number of transparent, externally and rapidly developing embryos with motor behaviors that are easy to assess (e.g. for mutagenic screens). The emergence of embryonic motility was studied behaviorally and at the cellular level. The embryonic behaviors appear sequentially and include an early, transient period of spontaneous, alternating tail coilings, followed by responses to touch, and swimming. Patch clamp recording in vivo revealed that an electrically coupled network of a subset of spinal neurons generates spontaneous tail coiling, whereas a chemical (glutamatergic and glycinergic) synaptic drive underlies touch responses and swimming and requires input from the hindbrain. Swimming becomes sustained in larvae once serotonergic neuromodulatory effects are integrated. We end with a brief overview of the genetic tools available for the study of the molecular determinants implicated in locomotor network development in the zebrafish. Combining genetic, behavioral and cellular experimental approaches will advance our understanding of the general principles of locomotor network assembly and function.     

Calcium imaging of infrared-stimulated activity in rodent brain

Infrared neural stimulation (INS) is a promising neurostimulation technique that can activate neural tissue with high spatial precision and without the need for exogenous agents. However, little is understood about how infrared light interacts with neural tissue on a cellular level, particularly within the living brain. In this study, we use calcium sensitive dye imaging on macroscopic and microscopic scales to explore the spatiotemporal effects of INS on cortical calcium dynamics. The INS-evoked calcium signal that was observed exhibited a fast and slow component suggesting activation of multiple cellular mechanisms. The slow component of the evoked signal exhibited wave-like properties suggesting network activation, and was verified to originate from astrocytes through pharmacology and 2-photon imaging. We also provide evidence that the fast calcium signal may have been evoked through modulation of glutamate transients. This study demonstrates that pulsed infrared light can induce intracellular calcium modulations in both astrocytes and neurons, providing new insights into the mechanisms of action of INS in the brain.     

Persistent sodium current is a nonsynaptic substrate for long-term associative memory

Although synaptic plasticity is widely regarded as the primary mechanism of memory [1], forms of nonsynaptic plasticity, such as increased somal or dendritic excitability or membrane potential depolarization, also have been implicated in learning in both vertebrate and invertebrate experimental systems [2], [3], [4], [5], [6] and [7]. Compared to synaptic plasticity, however, there is much less information available on the mechanisms of specific types of nonsynaptic plasticity involved in well-defined examples of behavioral memory. Recently, we have shown that learning-induced somal depolarization of an identified modulatory cell type (the cerebral giant cells, CGCs) of the snail Lymnaea stagnalis encodes information that enables the expression of long-term associative memory [8]. The Lymnaea CGCs therefore provide a highly suitable experimental system for investigating the ionic mechanisms of nonsynaptic plasticity that can be linked to behavioral learning. Based on a combined behavioral, electrophysiological, immunohistochemical, and computer simulation approach, here we show that an increase of a persistent sodium current of this neuron underlies its delayed and persistent depolarization after behavioral single-trial classical conditioning. Our findings provide new insights into how learning-induced membrane level changes are translated into a form of long-lasting neuronal plasticity already known to contribute to maintained adaptive modifications at the network and behavioral level [8].     

Neural computations underlying the exertion of force: a model

We have developed a model that simulates possible mechanisms by which supraspinal neuronal signals coding forces could converge in the spinal cord and provide an ongoing integrated signal to the motoneuronal pools whose activation results in the exertion of force. The model consists of a three-layered neural network connected to a two-joint-six-muscle model of the arm. The network layers represent supraspinal populations, spinal cord interneurons, and motoneuronal pools. We propose an approach to train the network so that, after the synaptic connections between the layers are adjusted, the performance of the model is consistent with experimental data obtained on different organisms using different experimental paradigms: the stiffness characteristics of human arm; the structure of force fields generated by the stimulation of the frog's spinal cord; and a correlation between motor cortical activity and force exerted by monkey against an immovable object. The model predicts a specific pattern of connections between supraspinal populations coding forces and spinal cord interneurons: the weight of connection should be correlated with directional preference of interconnected units. Finally, our simulations demonstrate that the force generated by the sum of neural signals can be nearly equal to the vector sum of forces generated by each signal independently, in spite of the complex nonlinearities intervening between supraspinal commands and forces exerted by the arm in response to these commands.     

Neuronal firing rates account for distractor effects on mnemonic accuracy in a visuo-spatial working memory task

Persistent neural activity constitutes one neuronal correlate of working memory, the ability to hold and manipulate information across time, a prerequisite for cognition. Yet, the underlying neuronal mechanisms are still elusive. Here, we design a visuo- spatial delayed-response task to identify the relationship between the cue-distractor spatial distance and mnemonic accuracy. Using a shared experimental and computational test protocol, we probe human subjects in computer experiments, and subsequently we evaluate different neural mechanisms underlying persistent activity using an in silico prefrontal network model. Five modes of action of the network were tested: weak or strong synaptic interactions, wide synaptic arborization, cellular bistability and reduced synaptic NMDA component. The five neural mechanisms and the human behavioral data, all exhibited a significant deterioration of the mnemonic accuracy with decreased spatial distance between the distractor and the cue. A subsequent computational analysis revealed that the firing rate and not the neural mechanism per se, accounted for the positive correlation between mnemonic accuracy and spatial distance. Moreover, the computational modeling predicts an inverse correlation between accuracy and distractibility. In conclusion, any pharmacological modulation, pathological condition or memory training paradigm targeting the underlying neural circuitry and altering the net population firing rate during the delay is predicted to determine the amount of influence of a visual distraction.