Frontiers in Systems Neuroscience

ContextGrid cells in the medial entorhinal cortex (MEC) of head-fixed mice exhibit ultraslow (<0.01 Hz) oscillations (USO) during walking in a 1D running wheel in darkness. It was proposed that these oscillations may have a connection with navigational behavior. ProblemThere is no clear link between the functional role of these oscillations and path integration, a fundamental navigation strategy used by animals to calculate their current position and orientation by continuously summing self-motion cues. HypothesisGiven the synaptic projections from MEC to the hippocampus, we hypothesized that ultraslow oscillations have a role in linking spatiotemporal memories acquired during navigation. MethodologyA realistic computational model of entorhinal-grid with ultraslow oscillations and hippocampal-place cells is proposed using synaptic plasticity between cell types, sustaining path integration of a rodent-like simulated animal. ResultsUltraslow oscillations induced persistent changes in the grid cell dynamics, represented as a positional drift of grid fields. Such drift resulted in position estimation errors but generated new grid-place cell associations when combined with synaptic plasticity. >DiscussionsUltraslow entorhinal oscillations were found to shape spatial memory through grid cell drifting, which could serve as a mechanism for flexibly accessing different spatial memories during navigation. HIGHLIGHTSO_LIPath integration dynamics hide ultraslow oscillations despite coexistence. C_LIO_LIUltraslow oscillations significantly degrade position estimation in path integration. C_LIO_LIGrid and place fields drift after the effect of ultraslow oscillations. C_LIO_LINew spatial memories were created as a result of the ultraslow oscillation drift. C_LIO_LIUltraslow oscillations enable dynamic access of different spatial memories C_LI

Glutamatergic neuronal synapses in the mouse neocortex mature during the first two months after birth. A key event during synaptic maturation is a change in short-term synaptic plasticity (STP), i.e. a switch from strong synaptic depression to a weaker depression or even facilitation. Glutamatergic pyramidal neurons located in the cortical layers II/III, layer V, and layer VI project axons through the corpus callosum where they release glutamate along their shafts and form glutamatergic synapses with oligodendrocyte precursor cells (OPCs). Here, we used single-cell electrophysiological recordings in brain slices to investigate synaptic plasticity at neuron-OPC synapses along axonal shafts in the white matter, and applied computation approaches to pinpoint the mechanisms of this plasticity. We found that during postnatal development of mice, there is a switch from short-term synaptic depression to short-term synaptic facilitation at glutamatergic neuron-OPC synapses in the corpus callosum. Synaptic delay of phasic neuron-OPC excitatory postsynaptic current shortens, and the amount of asynchronous release at neuron-OPC synapses decrease as animals mature, indicating that glutamate release becomes more synchronized. Our computational modelling suggests that both pre- and postsynaptic changes may contribute to the functional development and changes of plasticity at neuron-OPC synapses in the white matter. Taking together, our findings indicate that synaptic release machineries located at different sites along the same axon (i.e. axonal shaft in the white matter vs synaptic boutons in the grey matter) mature in a very similar fashion, STP occurs at both synaptic sites, and STP dynamics represent an important event during brain maturation.

Voltage perturbations to a repetitively firing Hodgkin-Huxley (HH) model of neuronal spiking in the bistable regime with coexisting limit cycle and stable steady node can either lead to the spikes phase resetting or collapse to the stable steady state. The latter describes a non-firing hyperpolarized quiescent state of the neuron despite the presence of constant external current. Using asymptotic phase response curve (PRC), the impact of voltage perturbations on a repetitively firing HH model is studied here while it is diffusively coupled to another HH model under identical external stimulation. It is observed that the pre-perturbation state of synchronization and the coupling strength critically determine the PRC response of the perturbed HH dynamics. Higher coupling strengths of perfectly in-phase (anti-phase) synchronized HH models shrink (expand) the combinatorial space of perturbation strengths and the oscillation phases causing collapse to the quiescent state. This indicates reduced (enlarged) basin of attraction, viz. the null space, associated with the steady state in the HH phase space. The findings bear important implications to the spiking dynamics of diverse interneurons, as well as special cases of pyramidal neurons, coupled through electrical synapses via. gap junctions, and suggest the role of gap junction plasticity in tuning vulnerability to quiescent state in the presence of biological noise and spikelets.

Adolescence is an important developmental period during which there are diverse changes in the brain and behavior. Goal-directed behaviors and the component processes underlying those behaviors improve during adolescence, including working memory, response inhibition, and reinforcement learning. At the same time there is substantial pruning of excitatory connections in prefrontal cortex and ongoing myelination of axons. However, psychiatric disorders also become increasingly prevalent in late adolescence and early adulthood. In this study, we develop computational models that suggest a hypothesis for how the ongoing changes in the brain can give rise to the increased prevalence of psychiatric disorders. We show that both myelination and pruning during adolescence lead to attractor landscapes in which strongly encoded memories, driven by three-factor learning rules that modulate Hebbian plasticity, come to dominate the landscape of brain activity, at the expense of weakly encoded memories. Pruning and myelination lead to large, strong attractors which, if they are related to aversive emotions, can drive intrusive thoughts and compulsions in obsessive compulsive disorder, rumination in depression, and aversive memories in post-traumatic stress disorder. The link between pruning, myelination and the emergence of dominant attractors for emotionally salient memories is well supported by the models. The way these effects map onto forebrain circuits requires more work.

In this study, resource-constrained learning methods were developed as a model for the learning behavior of the fly brain, specifically the mushroom body. Recent research on the mushroom bodies of flies shows that unfamiliar odors activate certain output neurons (MBONs); however, these effects are rapidly suppressed upon repeated exposure to the same odor. Such MBON behaviors appear to reflect odor learning. We investigated how flies continue learning about odors throughout their lives despite their small brains. Researchers have suggested that learning about new odors can help flies forget existing memories. Therefore, we hypothesized that the main reason for continual learning is that it serves as a strategy for forgetting. To test the validity of this hypothesis, we designed three models using a kernel perceptron. This approach is suitable for estimating ongoing learning capacity within a budget. According to the results of computer simulations and theoretical analysis, the model demonstrated the importance of forgetting mechanisms for two reasons: first, to prepare for subsequent learning sessions, and second, to reduce the negative effects of deleting memories. Author summaryDrosophila mushroom body output neurons (MBONs) in the 3 compartment of the fruit fly brain are highly activated by novel odors, and their activation triggers alerting behavior. Interestingly, these specific neurons react only to unfamiliar odor information, suggesting they constantly undergo incremental learning of new odors. This study was aimed at constructing three incremental learning models of the MBON 3 neurons. Although there have been numerous studies on complex circuit designs to reproduce activation waveforms, herein we constructed a fundamental learning model based on a kernelized learning method. Since kernelized learning models interpret Hebbian learning as the addition or subtraction of kernel functions, the model is easy to analyze theoretically. Consequently, we conclude that the forgetting property observed in the MBON 3 neurons is essential for reducing error when learning occurs within a brain of limited capacity.

Human attention is inherently transient and limited in span to only a few moments without lapsing. The intrinsic dynamics of large-scale neurocognitive networks are thought to contribute to these lapses and result in the unavoidable fluctuations in attention that constrain its span. However, it remains unclear how the millisecond temporal dynamics of specific electrophysiological brain states contribute to the endogenous maintenance of attention or the onset of attentional lapses. In the present study, we investigated whether the strength and millisecond dynamics of brain electric microstates differentiate states of focus from inattention and contribute to the endogenous maintenance of attention over short and long timescales. We recorded 128-channel EEG while participants maintained their attention during the wait time delay of trials in the Sustained Attention to Cue Task (SACT) and segmented the EEG into a categorized time series of microstates based on data-driven clustering of topographic voltage patterns. The findings revealed that the prevalence and rate of occurrence of microstates C and E in the wait time delay of trials differentiated trials in which the target stimulus was correctly detected from incorrectly detected. These same microstates were also implicated in the maintenance of attention over short and long timescales, with their time-varying dynamics changing systematically during the wait time delay of trials and over the course of the task session. Together, these findings demonstrate the sensitivity of microstates to variation in attentional states and suggest that the millisecond dynamics of these brain states contribute to the maintenance of attention over time.

As social beings, humans make decisions partly based on social interaction. Observing the behavior of others can lead to learning from and about them, potentially increasing trust and prompting trust-based behavioral changes. Observation-based decision making involves different neural structures. The orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) are known as neural structures mainly involved in processing emotional and cognitive decision values, respectively, while the anterior cingulate cortex (ACC) plays a pivotal role as a social hub, integrating the afferent expectancy signals from OFC and LPFC. This paper presents a neurocomputational model of the interplay between observational learning and trust, as well as their role in individual decision-making. Our model elucidates and predicts the emotional and rational behavioral changes of an individual influenced by observing the action-outcome association of an alleged expert. We have modeled the neurodynamics of three cortical structures (OFC, LPFC, and ACC) and their interactions, where the neural oscillatory properties, modeled with Dynamic Bayesian Probability, represent the observers attitude towards the expert and the decision options. As an example of an everyday behavioral situation related to climate change, we use the choice of transportation between home and work. The EEG-like simulation outputs from our model represent the presumed brain activity of an individual making such a choice, assuming the decision-maker is exposed to social information.

Vocal communication involves a series of cognitive processes, which can be broadly categorized into three components: perceiving communicative signals; deciding whether and how to respond; and generating vocal motor output. These processes must work harmoniously, with integration and bridging between components being crucial for effective communication. Previous research on vocal communication has typically focused on specific brain regions or isolated cognitive functions, often lacking a holistic perspective of macro-scale, whole-cortical dynamics and their role in the complete communication process. Therefore, although the cortical areas associated with each cognitive component have been localized in humans, the macro-scale cortical dynamics underlying the integration of these cognitive processes remain unknown. Building on recent findings linking macro-scale cortical dynamics to behavioral performance, we hypothesized that traveling wave like cross-areal interactions play a role in integrating the three communicative components. To test this hypothesis, we recorded whole-cortical activity using epidural electrocorticography (ECoG) while subject marmosets vocally interacted with partners. We found theta-band activation in several cortical areas, including the parietal and auditory cortices, while listening to partners calls. This activity was further modulated depending on whether the subjects engaged in vocal interactions, potentially representing the transformation of sensory processing into decision-making and vocal motor preparation. Given the widespread nature of this modulation, we next characterized whole-brain activity patterns by employing a novel analytical method, Weakly Orthogonal Conjugate Contrast Analysis (WOCCA). This analysis revealed that cortical activity could be decomposed into two distinct traveling wave like propagation patterns, a rotational and a translational wave, and both waves discriminated communicative conditions consistent with localized activity. The rotational wave further represented vocal motor preparation through trigger-like temporal pattern. In addition, the magnitude of the translational wave immediately before subjects vocal production correlated with the vocal production-induced suppression of high-gamma-band activity, particularly in the prefrontal and auditory cortices. As vocalization-induced suppression is believed to reflect sensory prediction, the translational wave may propagate specific decision-related or acoustic information necessary for subsequent vocal production to local cortical areas. These findings suggest that the brain orchestrates the sequential cognitive processes underlying vocal communication through macro-scale traveling waves.

Hippocampal area CA2 has recently emerged as a critical region for social recognition memory. Furthermore, this understudied region has been implicated in psychiatric diseases and neurodegenerative diseases. There has been accumulating evidence indicating that the pyramidal neurons (PNs) in area CA2 exhibit functional specializations that correlate with somatic position in stratum pyramidale (sp). In this study, we investigated the morphological differences in dendritic architecture of CA2 PNs with a focus on the radial gradient, i.e., along the deep-superficial axis of the sp. We conducted a comprehensive morphological analysis including Sholl intersection profiles, branching order distributions, root angle distributions, and dendritic cable lengths. We found that CA2 PNs have fewer oblique dendrites and a larger number of tuft-like dendrites as compared to CA1 PNs. Furthermore, within the CA2 population, we found that many of the dendritic structural features gradually changed along the radial axis from deep to superficial somatic location, indicating a continuum of dendritic morphology rather than two sharply defined subtypes of pyramidal neurons. This morphological characterization may serve as a starting point to better understand the corresponding functional organization of CA2. The gradual difference between deeper and superficial CA2 PNs suggests a continuum of their computational capabilities beyond two binary functional classes. In briefUsing several methods, we examine the dendritic morphology of over 130 CA2 and CA1 pyramidal neurons and find that many properties such as the cable length and terminal numbers of the dendritic arbors vary as a with the location of the soma in the pyramidal layer. HighlightsO_LIWe use scholl analysis, graph theory and machine learning techniques to quantify the different dendritic morphologies of CA2 pyramidal neurons. C_LIO_LIMany properties of CA2 pyramidal neuron apical dendrites vary as a function of somatic location in the pyramidal layer. C_LIO_LIMore superficial CA2 pyramidal neurons have longer oblique apical dendrites, and shorter tuft dendrites. C_LI

The phenomenon of splitting was originally observed in hamsters which, after prolonged exposure to constant light, exhibit two rest/wake cycles within a subjective day. Splitting is a consequence of the left and right suprachiasmatic nuclei (SCN) falling out of synchrony. While it is known that split activity is characterized by an antiphase relationship between the left and right SCN and between the core and shell within each hemisphere, the role of the commissural projections that connect the right and left SCN is not known. In the present study, we investigate the impact of the inter-hemispheric connections on the split and unsplit dynamics of a computational model of the bilateral SCN. Our model has 4 nodes corresponding to each right and left core and shell. We simulated our bilateral model under different lighting conditions and measured its period and the phase relationships among the 4 nodes. To further characterize the dynamics of the system, we performed a bifurcation analysis. We found that the bilateral model automatically splits unless entrained by bright light/dark cycles, or unless it has excitatory inter-hemispheric connections. This suggests that excitatory cross-connections may be important for freerunning behavior. We found that constant light of varying intensities transitions the model between split and unsplit activity only in very limited conditions, but the strength and polarity of the contralateral connections play a much greater role in this dynamical transition. These findings suggest that splitting may involve plasticity of the inter-hemispheric connections of the SCN.

The primary objective of system neuroscience is to understand the functional mapping and its causation in the dynamics of the brain network. Some experimental and methodological studies suggest that functional modularity and its hierarchical information processing in the brain network are crucial to understanding the functional role of task-specific or state-specific information flow in the brain. However, because most of the established techniques for detecting effective network structures in the neuroscience research field are strongly based on the "Granger causality" perspective, existing causal discovery methods specified for brain network analysis cannot identify the causal hierarchy in the modular network in the brain due to spurious correlation issues and indistinguishability of causal direction under the Gaussianity of observational noise in a linear system. To address the issues, we developed a causal discovery method for synchronous neural dynamics, called the Jacobian-informed linear non-Gaussian acyclic model, "j-VAR-LiNGAM", by incorporating the information of the Jacobian matrix determined from a phase-coupled oscillator model estimated from observed neural data into the VAR-LiNGAM algorithms. The method was validated by showing that it could extract causal ordering in both synthetic data and empirical neural observed data. Moreover, by analyzing the observed neural oscillatory signals obtained from mice and humans, we confirmed that our method identified causally hierarchical structures in the brain, which aligned with the neurophysiological interpretations. These findings suggested that our proposed method can reveal the neural basis of hierarchical information processing in the brain network.

Homeostatic plasticity of intrinsic excitability (IE) in the visual system has been essentially shown at the cortical level but whether thalamic nuclei also express homeostatic plasticity of IE is unknown. We show here that 4 days of monocular deprivation (MD) at eye opening induces a homeostatic change in IE in dorsal lateral geniculate nucleus (dLGN) neurons. Neurons recorded in the dLGN region activated by the deprived eye are more excitable than neurons recorded in the dLGN region activated by the open eye. No significant changes were observed following 7 days of MD, however. Enhanced excitability in neurons from the deprived side after 4 days of MD was associated with a reduced Kv1-dependent LTP-IE, a smaller voltage ramp, and a reduced inter-spike interval, suggesting that Kv1 channels are down-regulated in deprived dLGN neurons. Furthermore, the ankyrin G signal of the axon initial segment was larger in deprived dLGN neurons compared with open ones, indicating that Nav1 channel number also undergoes homeostatic regulation, and Kv1.1 channel signals were lower in deprived neurons compared to open ones. In addition, electrical coupling was found to be strengthened in neurons displaying enhanced IE following either brief (4 days) or long (10 days) MD. These results suggest that homeostatic and Hebbian plasticity in the dLGN share common expression mechanisms involving the regulation of Kv1 channels, Nav1 channels and electrical coupling between relay neurons.

Vestibular neurons are core elements of the pathways involved in vestibulo-motor functions, such as vestibulo-spinal and vestibulo-ocular reflexes. To meet behavioral needs, electrophysiological neuronal properties are adequately adapted to the sensory-motor computation sustaining these distinct vestibular reflexes. During frog metamorphosis, there is a complete reorganization of the posturo-locomotor system while the oculomotor system remains minimally changed, probably associated to so far unknown changes in vestibular neuronal properties. We used this unique model to investigate the central developmental mechanisms underlying such a reconfiguration of vestibular-associated behaviors. Central vestibular neurons exhibit two types of electrophysiological phenotypes: tonic neurons with a continuous discharge and phasic neurons with a transitory discharge mainly due to the activation of Kv1.1 channel. Electrophysiological recordings and Kv1.1 immunolabeling of vestibulospinal (VS) and vestibulo-ocular (VO) neurons at both larval and juvenile stages revealed that the majority of VS neurons exhibited a tonic discharge in larvae but a phasic discharge in juvenile, while VO neurons remained mainly tonic throughout development. Changes in phasic and tonic neurons proportions in VS population are partly explained by neurogenesis. But we provide evidences that an electrophysiological phenotype switch is a concomitant developmental mechanism participating in the maturation of these central vestibular neurons. All together our results showed that the maturation process in central vestibular neuronal groups is highly related to the metamorphosis-induced remodeling of vestibulo-motor functions they are involved in, with the ultimate purpose of ensuring an adequate adaptation of neuronal elements properties to the developmental changes of behavioral constrains.

Growing interest to describe the electrical behavior of glial cells, mainly astrocytes, in intact brain tissue poses more and more challenges to commonly accepted belief they only respond in a linear manner in uptake of the excess of extracellular potassium and maintenance of their network equipotentiality. Their highly conductive mutual interconnections via gap junction (GJ) connections introduce yet another class of nonlinear elements. As more studies report nonlinearities in membrane voltage Vm dependence of both, the membrane and junctional conductances, the need to formulate minimal dynamical models of their transient behavior is getting more acute. Since ODE models of coupled cells, even in simplest 1-d arrays, require simplified descriptions and small set of parameters, rare quantitative studies on glia makes the task even more difficult. This study attempts to qualify a self-coupled cell, or a glial cell coupled to fixed voltage as useful system for detecting the nature of instabilities and transitions coming from coupling. In a novel biophysical model of coupled astrocyte, we introduce nonlinear kinetics of deactivation for large junctional voltages for the first time. We found that N-shaped nonlinearities and corresponding fold structure in the vector field of isolated cell serves as a baseline on top of which coupling nonlinearities enrich the bifurcation picture. Numerical simulations of 1-d array of coupled astrocytes show that coupling increases the propensity of astrocytic Vm to bistability and front propagation. We believe that presented illustrations of possible effects of coupling nonlinearities will motivate neurobiologists to further explore their impact in disease. Significance statementTransient changes in membrane voltage of glial cells may produce significant transient voltage difference between directly coupled cells. Nonlinear steady-state conductance of their interconnection elements, the gap junctions, introduce nonlinear current profiles which are very difficult to measure and quantitate using the available methods due to marked permeability of the junctions and leakiness of glial membrane in general. We propose a minimal model of glial membrane extended with a self-coupled feedback loop, which under realistic simplifying assumptions could serve for qualitative analysis of the impact of coupling, on the stability of resting membrane voltage. Neuronal cells of the brain and spinal cord cannot exist and function without supportive and neuromodulatory functions of the diverse population of glial cells. This applies to virtually all physiological processes on cell level - from cell development, metabolic support, membrane signaling, slow molecular signal transduction, ion homeostasis, neurovascular coupling, myelination, to mention only a few, manifest neuro-glial interaction. Even though all glial cell types are interconnected, the most abundant ones, the astrocytes are massively interconnected by gap junctions to form ordered networks. Electrically, astrocytic networks display membrane voltage equipotentiality, which is considered system-wide resting state for given neuro-glial circuit or unit. With molecular and cellular substrates of glial connectivity being slowly elucidated, network science and dynamical modeling are slowly "invading" that area with many important issues left open. In this study using classical dynamical systems approaches we give indications how nonlinear intercellular coupling between astrocytes affects physiological resting state and its instabilities compared to isolated, uncoupled cell. We strongly believe the suggested minimal model could fill the gap in ODE modeling of neuro-glial circuits, within broadest scope of hypothesis-driven research in cell-level neuroscience.

Flow, as defined by Mihalyi Csikszentmihalyi (1975), is a holistic sensation experienced when individuals are fully immersed in an activity, resulting in a mental state characterized by a diminished sense of self and altered perception of time. To investigate the global neural dynamics underlying flow, we employed EEG microstate analysis to capture the spatial and temporal properties of dominant transient global brain states (Lehmann et al., 1998). In a study involving 43 participants playing the video game Thumper for 25 minutes, we extracted three four-minute EEG segments from each session corresponding to reported experiences of flow, boredom, and frustration, as determined by self-reports and performance metrics. Across conditions, six distinct microstate topographies (A-F) accounted for most of the global variance. Given that reduced self-referential processing is a key feature of flow, we hypothesized that flow would modulate the properties of microstates C and E, which have been associated with brain regions resembling the default mode network (DMN). Compared to boredom and frustration, the flow condition showed significantly decreased global explained variance, mean duration, time coverage, and occurrence frequency of microstate E, as well as reduced mean duration and time coverage of microstate C. These findings suggest that microstates associated with self-referential processing are shorter and less frequent during flow than during boredom and frustration. This supports the notion that the flow experience modulates global brain dynamics, particularly within the DMN. Furthermore, our results align with previous research reporting reduced DMN activity during meditative and psychedelic states, reinforcing the idea of diminished self-awareness in such conditions.

Hippocampus is known to replay activity patterns to recall and process memories, which is often related to Hopfield-type attractor dynamics. Another line of theoretical studies suggests that hippocampal replay prioritizes replay of experiences to accelerate value learning for efficient decision making. It is unknown how hippocampal attractor dynamics perform prioritized memory sampling, and more broadly, how we can consistently relate dynamical (bottom-up) and functional (top-down) theories of hippocampal replay. In this paper, we propose an extended Hopfield-type attractor network model with momentum, kinetic energy, and conservation of the total energy, which is called momentum Hopfield model. We show that our model can be interpreted as CA3-CA1 network model with intrinsic oscillation, and such network model reproduces hippocampal replay in 1-D and 2-D spatial structures. We also prove that our model functionally works as Markov-chain Monte Carlo sampling in which recall frequencies of memory patterns can be arbitrarily biased. Using this property, we implemented prioritized experience replay using our model, which actually accelerated reinforcement learning for spatial navigation. Our model explains how dynamics of hippocampal circuits realize efficient memory sampling, providing a theoretical link between dynamics and functions of hippocampal replay.

The hippocampus participates in crucial functions such as memory consolidation, spatial processing and emotional regulation that require diverse input from multiple cortical areas that is funneled through the upper layers of the entorhinal cortex (EC), mostly from layer II to the dentate gyrus (DG). Traditional models described 200,000 EC layer II neurons projecting to 1 million granule cells (GCs) in the rat, rendering low divergence (1:5), with each EC neuron establishing about 18,000 synapses with GCs and each GC receiving about 4,000 synapses from EC neurons. In this manuscript, we update this model of connectivity incorporating new features described in the last three decades that include updated populations of EC layer II neurons obtained with design-based stereology, a revised definition of EC layer II based on molecular criteria and selecting reelin expressing neurons as the only layer II neurons projecting to the hippocampus. The updated model shows [~]80,000 neurons from EC layer II projecting to the DG, [~]45,000 from the medial entorhinal cortex (MEC) and [~]35,000 from the lateral entorhinal cortex (LEC) with high divergence of 1:20 and 1:30. We also show that EC layer II neurons may establish [~]90,000-115,000 synapses on GCs, while GCs receive about 8,000 synapses from EC layer II neurons. We estimate a [~]25% redundancy in the connectivity, so each EC neuron may contact [~]68,000-86,000 GCs and each GC would be contacted by [~]3000 neurons from MEC and 3,000 from LEC. In addition, we quantitatively assess a potential projection of mossy cells to the medial molecular layer described in mice, which could have a potential impact on GC inhibition. Overall, we produced a detailed, complete, and updated quantitative model of EC projections to the DG that reveals a much more divergent and richer projection than previously described, with implications for functional models (e.g.: pattern separation) and more widely for building realistic hippocampal models or establishing comparisons across species.

Cooperative behavior is a cornerstone of human interaction. Although both "betrayal aversion" (the affective cost of being betrayed) and "loss aversion" (the financial detriment incurred from betrayal) are established determinants of cooperative behavior, their relative potency remains undetermined. Here, we investigated these effects by integrating computational modeling and event-related potential (ERP) techniques. In two tasks involving risk and cooperation, participants decided whether to take financial risks or to cooperate under possible betrayal. Our results showed that betrayal aversion had a stronger effect on reducing cooperation compared to loss aversion. Furthermore, ERP data demonstrated sequential processing: betrayal was encoded early in decision-making, reflected by increased P3 with weaker betrayal aversion, whereas loss aversion manifested later, marked by increased LPP. By dissociating the contributions of betrayal and loss, our finding provides novel insights into the cognitive and neural mechanisms underlying cooperative behavior.

Concept cells are neurons in the medial temporal lobe that represent an abstract concept, such as a familiar person, in a context-independent way. They are activated by heterogeneous and potentially multi-modal sensory input related to the concept, such as viewing a photo of the person, reading the persons name, or hearing the persons voice. Learning the concept implies connecting the cortical cells that are triggered when such features are perceived with the cells of the concept, resulting in an associative memory engram. Traditional models explain the formation of such an engram with Hebbian learning through synaptic plasticity, the strengthening of existing synapses in response to co-stimulation. However, the low edge density of the brain suggests that direct connections in the form of preexisting synapses between these cells are relatively unlikely, rendering such constructs inefficient and volatile. Instead, it is plausible that more persistent concept engrams rely on structural plasticity, involving the creation of synapses de novo. Yet, it has still been unclear how neurons can project their axons across such a considerable distance, finding a target they do not know in advance. In this paper, we simulate the formation of such structural engrams in the connectomes of healthy subjects, offering a model hypothesis of how such engrams can be formed on a structural level. Based on our model, we further demonstrate how activating a concept can trigger related concepts through overlapping sensory associations, in a process we call a percept-concept loop.

Elite athletes exhibit sport-specific neural adaptations, yet it remains unclear whether such changes reflect general effects of training or the unique demands of individual sports. Skiing requires postural control and whole-body coordination under dynamically unstable environments, placing high demands on somatosensory processing and sensorimotor integration. The present study aimed to identify structural brain characteristics specific to elite skiers by comparing them with athletes from other sports disciplines and non-athletes. T1-weighted MRI data were analyzed using voxel-based morphometry in 13 skiers, 23 non-ski control athletes and 25 non-athletes. Whole-brain analysis comparing skiers with non-ski athletes revealed a significant cluster showing greater gray matter volume in skiers compared with non-ski athletes in the left postcentral gyrus, extending into the superior parietal lobule. The identified cluster primarily encompassed cytoarchitectonic Areas 2 and 5L. These regions are involved in higher-order somatosensory processing and multisensory integration. Importantly, region-of-interest analysis demonstrated that gray matter volume within this cluster was greater in skiers compared with non-ski athletes and non-athletes, with no difference between non-ski athletes and non-athletes. These findings highlight the relative prominence of structural adaptations within somatosensory-parietal networks, reflecting the unique integration of proprioceptive and other sensory information required for elite skiing. Overall, these findings provide evidence for sport-specific structural brain differences in elite athletes and highlight the importance of somatosensory and parietal regions in sensorimotor integration relevant to skiing. These findings may have implications for understanding neural markers of expertise and may inform future approaches to training and performance evaluation in skiing.