Neural systems that facilitate the representation of social rank

Across species, animals organize into social dominance hierarchies that serve to decrease aggression and facilitate survival of the group. Neuroscientists have adopted several model organisms to study dominance hierarchies in the laboratory setting, including fish, reptiles, rodents and primates. We review recent literature across species that sheds light onto how the brain represents social rank to guide socially appropriate behaviour within a dominance hierarchy. First, we discuss how the brain responds to social status signals. Then, we discuss social approach and avoidance learning mechanisms that we propose could drive rank-appropriate behaviour. Lastly, we discuss how the brain represents memories of individuals (social memory) and how this may support the maintenance of unique individual relationships within a social group. This article is part of the theme issue ‘The centennial of the pecking order: current state and future prospects for the study of dominance hierarchies’.

Beyond Broca: Neural Architecture and Evolution of a Dual Motor Speech Coordination System

Classical neural architecture models of speech production propose a single system coordinating all the vocal articulators from lips to larynx. Here we propose a dual coordination system in which laryngeal control of pitch-related aspects of prosody and song are controlled by a dorsal premotor system while supralaryngeal articulation at the phonetic/syllabic level is controlled by a ventral premotor system. The separability of these neural systems supports models of the evolution of speech and language in which song-based communication was an early foundational stage.

NeuroVis: Real-Time Neural Information Measurement and Visualization of Embodied Neural Systems

Understanding the real-time dynamical mechanisms of neural systems remains a significant issue, preventing the development of efficient neural technology and user trust. This is because the mechanisms, involving various neural spatial-temporal ingredients [i.e., neural structure (NS), neural dynamics (ND), neural plasticity (NP), and neural memory (NM)], are too complex to interpret and analyze altogether. While advanced tools have been developed using explainable artificial intelligence (XAI), node-link diagram, topography map, and other visualization techniques, they still fail to monitor and visualize all of these neural ingredients online. Accordingly, we propose here for the first time “NeuroVis,” real-time neural spatial-temporal information measurement and visualization, as a method/tool to measure temporal neural activities and their propagation throughout the network. By using this neural information along with the connection strength and plasticity, NeuroVis can visualize the NS, ND, NM, and NP via i) spatial 2D position and connection, ii) temporal color gradient, iii) connection thickness, and iv) temporal luminous intensity and change of connection thickness, respectively. This study presents three use cases of NeuroVis to evaluate its performance: i) function approximation using a modular neural network with recurrent and feedforward topologies together with supervised learning, ii) robot locomotion control and learning using the same modular network with reinforcement learning, and iii) robot locomotion control and adaptation using another larger-scale adaptive modular neural network. The use cases demonstrate how NeuroVis tracks and analyzes all neural ingredients of various (embodied) neural systems in real-time under the robot operating system (ROS) framework. To this end, it will offer the opportunity to better understand embodied dynamic neural information processes, boost efficient neural technology development, and enhance user trust.

Neuron-Recognizable Characteristics of Peptides Recombined Using A Neuronal Binding Domain of Botulinum Neurotoxin

Recombinant peptides were designed using the C-terminal domain (receptor binding domain, RBD) and its subdomain (peptide A2) of a heavy chain of botulinum neurotoxin A-type 1 (BoNT/A1), which can bind to the luminal domain of synaptic vesicle glycoprotein 2C (SV2C-LD). Peptide A2- or RBD-containing recombinant peptides linked to an enhanced green fluorescence protein (EGFP) were prepared by expression in Escherichia coli. A pull-down assay using SV2C-LD-covered resins showed that the recombinant peptides for CDC5328 BoNT/A1, referred to EGFP-A2ʹ and EGFP-RBDʹ, exhibited ≥ 2.0-times stronger binding affinity to SV2C-LD than those for the wild-type BoNT/A1. Using bio-layer interferometry, an equilibrium dissociation rate constant (KD) of EGFP-RBDʹ to SV2C-LD was determined to be 5.45 mM, which is 33.87- and 15.67-times smaller than the KD values for EGFP and EGFP-A2ʹ, respectively. Based on confocal laser fluorescence micrometric analysis, the adsorption/absorption of EGFP-RBDʹ to/in differentiated PC-12 cells was 2.49- and 1.29-times faster than those of EGFP and EGFP-A2ʹ, respectively. Consequently, the recombinant peptides acquired reasonable neuron-specific binding/internalizing ability through the recruitment of RBDʹ. In conclusion, RBDs of BoNTs are versatile protein domains that can be used to mark neural systems and treat a range of disorders in neural systems.

Biological Prescience: The Role of Anticipation in Organismal Processes

Anticipation is the act of using information about the past and present to make predictions about future scenarios. As a concept, it is predominantly associated with the psychology of the human mind; however, there is accumulating evidence that diverse taxa without complex neural systems, and even biochemical networks themselves, can respond to perceived future conditions. Although anticipatory processes, such as circadian rhythms, stress priming, and cephalic responses, have been extensively studied over the last three centuries, newer research on anticipatory genetic networks in microbial species shows that anticipatory processes are widespread, evolutionarily old, and not simply reserved for neurological complex organisms. Overall, data suggest that anticipatory responses represent a unique type of biological processes that can be distinguished based on their organizational properties and mechanisms. Unfortunately, an empirically based biologically explicit framework for describing anticipatory processes does not currently exist. This review attempts to fill this void by discussing the existing examples of anticipatory processes in non-cognitive organisms, providing potential criteria for defining anticipatory processes, as well as their putative mechanisms, and drawing attention to the often-overlooked role of anticipation in the evolution of physiological systems. Ultimately, a case is made for incorporating an anticipatory framework into the existing physiological paradigm to advance our understanding of complex biological processes.

Behavioural consequences of Setd1a haploinsufficiency in mice: evidence for heightened emotional reactivity and impaired sensorimotor gating

ABSTRACTA number of studies implicate the loss of function (LoF) mutations affecting the histone methyl transferase SETD1A in the aetiology of a range of neurodevelopmental disorders including schizophrenia. Here, we examined the behavioural consequences of haploinsufficiency of Setd1a in a mouse model. We find evidence for changes in a number of phenotypes of relevance to schizophrenia, including increased anxiety-related behaviour, enhanced acoustic startle response, and decreased pre-pulse inhibition of acoustic startle. The sensorimotor gating deficits in Setd1a+/- mice could not be rescued by haloperidol or risperidone, suggesting that these antipsychotics are ineffective for ameliorating schizophrenia-relevant phenotypes in Setd1a+/- mice and point to deficits in neural systems other than the monoamine system. These phenotypes are emerging as key features of a number of other mouse models of rare neurodevelopmental disorders caused by LoF mutations in genes encoding epigenome modifiers suggesting they may act in a network to modulate brain development. Taken together these data strengthen the support for the use of Setd1a haploinsufficient mice as a model for the biological basis of schizophrenia, and point towards possible underpinning neural mechanisms.

Shared mechanisms of auditory and non-auditory vocal learning in the songbird brain

Songbirds and humans share the ability to adaptively modify their vocalizations based on sensory feedback. Prior studies have focused primarily on the role that auditory feedback plays in shaping vocal output throughout life. In contrast, it is unclear whether and how non-auditory information drives vocal plasticity. Here, we first used a reinforcement learning paradigm to establish that non-auditory feedback can drive vocal learning in adult songbirds. We then assessed the role of a songbird basal ganglia-thalamocortical pathway critical to auditory vocal learning in this novel form of vocal plasticity. We found that both this circuit and its dopaminergic inputs are necessary for non-auditory vocal learning, demonstrating that this pathway is not specialized exclusively for auditory-driven vocal learning. The ability of this circuit to use both auditory and non-auditory information to guide vocal learning may reflect a general principle for the neural systems that support vocal plasticity across species.

The Role of Leptin in the Development of Energy Homeostatic Systems and the Maintenance of Body Weight

LEP is a pleiotropic gene and the actions of leptin extend well beyond simply acting as the signal of the size of adipose tissue stores originally proposed. This is a discussion of the multi-system interactions of leptin with the development of the neural systems regulating energy stores, and the subsequent maintenance of energy stores throughout the lifespan. The prenatal, perinatal, and later postnatal effects of leptin on systems regulating body energy stores and on the energy stores themselves are heavily influenced by the nutritional environment which leptin exposure occurs. This review discusses the prenatal and perinatal roles of leptin in establishing the neuronal circuitry and other systems relevant to the adiposity set-point (or “threshold”) and the role of leptin in maintaining weight homeostasis in adulthood. Therapeutic manipulation of the intrauterine environment, use of leptin sensitizing agents, and identification of specific cohorts who may be more responsive to leptin or other means of activating the leptin signaling pathway are ripe areas for future research.

Distributed neural systems enable flexible attention updating during category learning

In order to accurately categorize novel items, humans learn to selectively attend to stimulus dimensions that are most relevant to the task. Models of category learning describe the interconnected cognitive processes that contribute to selective attention as observations of stimuli and category feedback are progressively acquired. The Adaptive Attention Representation Model (AARM), for example, provides an account whereby categorization decisions are based on the perceptual similarity of a new stimulus to stored exemplars, and dimension-wise attention is updated on every trial in the direction of a feedback-based error gradient. As such, attention modulation as described by AARM requires interactions among orienting, visual perception, memory retrieval, error monitoring, and goal maintenance in order to facilitate learning across trials. The current study explored the neural bases of attention mechanisms using quantitative predictions from AARM to analyze behavioral and fMRI data collected while participants learned novel categories. GLM analyses revealed patterns of BOLD activation in the parietal cortex (orienting), visual cortex (perception), medial temporal lobe (memory retrieval), basal ganglia (error monitoring), and prefrontal cortex (goal maintenance) that covaried with the magnitude of model-predicted attentional tuning. Results are consistent with AARM’s specification of attention modulation as a dynamic property of distributed cognitive systems.