Frontiers in Systems Neuroscience

Neurons couple to various degrees to the activity level of the local neighboring population whereby strongly coupled choristers and weakly coupled soloists have been identified as two extremes of a continuous spectrum. At the same time neuronal populations undergo coordinated ON and OFF cortical state activity fluctuations, which are locally modulated by attention. The population coupling of soloists and choristers suggests that soloists should show limited alignment with cortical state fluctuations, while choristers should exhibit profound alignment. To test this, we recorded neurons across cortical layers in macaque areas V1 and V4, while animals performed a feature based spatial attention task. As expected, we found a wide range of population coupling strength of neurons. In line with our prediction, coupling of choristers to cortical state changes (ON-OFF transitions) was generally stronger than that of soloists. The strength of population coupling of neurons was similar during spontaneous and stimulus driven activity. Allocation of attention to the receptive field reduced the population coupling strength. Attentional modulation of neurons was positively correlated with population coupling strength. While neurons on average retained their coupling strengths across conditions, some neurons change coupling strength condition dependent, thereby potentially enhancing the coding abilities of cortical circuits.

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.

Neuronal avalanches, a hallmark of self-organized criticality, represent cascading activity among interconnected neurons. Despite their hypothesized role in neuronal maturation, developmental patterns of neuronal avalanches across interconnected brain regions in vivo remain unelucidated. We applied avalanche analysis to investigate the emergent features of functional connectivity in the developing medial prefrontal cortex-basolateral amygdala (mPFC-BLA) circuitry, using simultaneous electrophysiological recordings from the BLA and mPFC in juvenile and young adult anaesthetized rats. The avalanches were mainly confined to either mPFC or BLA, a small number spanned both regions. Prefrontal avalanches exhibited scale-free behavior with branching ratio close to one and decreasing over development; amygdaloid avalanche size distributions displayed a sharp peak at the maximum size, more pronounced in adulthood, which together with branching ratios over one suggests a deviation from criticality toward a supercritical state. Different dynamical regimes in the BLA and mPFC fit distinct functional demands of these regions.

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.

The brain is never truly at rest. Even in the absence of external stimuli or cognitive tasks, the central nervous system continuously receives peripheral signals from visceral organs, such as the heart, lungs, and stomach, and sends motor commands to regulate organ physiology. This bidirectional brain-body interaction, also known as interoception, engages neural pathways via a hierarchical set of brain regions, including the nucleus of the solitary tract, hypothalamus, paraventricular nuclei of the thalamus, insular cortex, and anterior cingulate cortex, among others. However, it is largely unclear to what extent interoceptive signaling shapes ongoing fluctuations and correlations of brain activity. To address this question, we recorded resting state functional magnetic resonance imaging data from 34 anesthetized rats, examined intrinsic correlations (or functional connectivity), and tested their dependence on the bodys digestive state and peripheral nerve integrity. We observed reciprocal functional connectivity among brain regions situated along established interoceptive pathways, revealing a cohesive network, which we refer to as the interoception network. This network showed stronger functional connectivity in the fed condition (digestive phase) compared to the fasted condition (inter-digestive phase), suggesting its dependence on distinct states of gastrointestinal interoception without apparent cardiac or respiratory confounds. Importantly, we found that the interoception network relied on the integrity of the vagus nerve, a primary component of the peripheral nervous system for visceral sensation and parasympathetic control. When vagal signaling was surgically severed by bilateral cervical vagotomy, functional connectivity within the interoception network was notably reduced. Given these findings, we conclude that resting state functional connectivity is not sustained by the central nervous system alone, but relies on interoceptive signaling mediated through peripheral nerves that connect the brain and viscera.

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.

Left-right asymmetry of the brain is well recognized in various animals including C. elegans, drosophila and zebrafish. In primates, most of the brain studies describe side of the brain. However, in spite of huge amounts of accumulating rodent studies on neuroscience, most of rodent studies do not distinguish the brain side. The pig brain is considered to occupy an intermediate position between primates and rodents in terms of structural complexity and brain function. Moreover, the numbers of studies using genetic manipulation of pigs are drastically increasing. So, we investigated microminipig (MMP) brain mesoscopic anatomy focusing on left-right differences of its morphology. Here, we show the anterior cingulate cortex, perirhinal cortex, and cerebellum of male and female MMPs, are structurally asymmetrical. The cerebellar vermis, paravermis is tilted from the midline and the consequently the cerebellar cortex exhibits asymmetrical morphology. The anterior cingulate gurus exhibited protrusion and invagination toward the midline on the right and left side, respectively. The left perirhinal lobe exhibited distinct patterns of cortical gyration between left and right side. These data demonstrate that MMPs are one of the suitable model animals for investigating cerebral and cerebellar asymmetry.

Comparative studies have established differences between the electrophysiology and anatomy of human and rodent cortical circuits. A consistent finding is that human neuronal morphologies display more elaborate neurite shapes than those of rodents, a feature that cannot be accounted for merely by their larger size according to recent findings. Here, we study the impact of these neurite shapes on the structure of synaptic connectivity in their local microcircuitry. Our approach is based on the idea that axonal and dendritic geometries constrain the locations of afferent and efferent synaptic contacts (potential connectivity). Although the mechanisms by which potential connectivity translates into actual synaptic connectivity are manifold and complex, the potential connectivity is nevertheless highly informative for the final structure of a biological connectome. We found that connectomes predicted from human reconstructed morphologies have higher complexity according to several measures that have been demonstrated to be functionally relevant. Going beyond a simple comparison, we demonstrate mechanistically how the shapes of neuron morphologies give rise to non-random and clustered structures observed in experimentally measured connectomes, and how the specific shapes of human neurons strengthen the process. Finally, we conceptually examine how synapse formation processes may interact with potential connectivity, showing that a process compatible with Hebbian plasticity leads to the highest complexity and best match experimentally observed patterns.

In our daily lives we encounter a myriad of things with which we might need to interact as we navigate our environment. Mental representations of these things must be computed and stored in our brains to be manipulated to support cognition. How are such representations organized in the brain? Several proposals have been put forth on what the principles of organization of object information in the brain might be: within ventral temporal cortex - regions thought to support object recognition - possible dimensions include the animacy status of target stimuli, their real size, their texture and material properties, and potentially their graspable status, amongst others. Here we used functional magnetic resonance imaging (fMRI) and multivariate approaches to discriminate patterns of activation for different categories of objects to test the role of these dimensions as organizing principles of object information in the brain. We show that pattern discriminability between different categories of objects does not seem to follow differences in their animacy status in any continuous way. Moreover, graspability of the target stimuli and their haptic texture properties are better predictors of representational content within ventral temporal cortex than animacy and real size. These results are in line with recent studies demonstrating the importance of computational contingencies superimposed by bi-directional functional coupling between parietal regions dedicated to the processing of object manipulation and grasping and ventral temporal regions responsible for object recognition, potentially involving material and texture processing.

Neural activity in mouse primary visual cortex (V1) correlates strongly with orofacial movements. Such strong modulation has not been found in the primate visual cortex during eye fixation [1], which led to the suggestion that the modulation may primarily depend on eye movements in both species [2]. Here we examined the influence of eye movements on neural activity in mouse visual cortex both in complete darkness and in the presence of different types of visual input. In all cases, we found that eye movements explain a smaller fraction of neural activity variance compared to orofacial behaviors. Additionally, we found that eye movements were correlated to orofacial movements, such as whisking and sniffing, and thus may be indirectly correlated to neural activity. These results further emphasize the impact of movement signals on mouse visual cortex during free viewing behavior.

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.

How does the functionality of the cortex change from infancy to adulthood to support the developmental cognitive shift from learners to performers? Cortical adaptation is a simple neural mechanism which plays a key role in learning and memory encoding, but little is known about how it develops across the lifespan. Both infants and adults have been found to respond differently to repeating audio and visual stimuli, suggesting differences in cortical adaptation throughout development. However, studies typically approach these populations through different paradigms and interpret the results in terms of different cognitive models. To overcome these issues, we implemented an identical paradigm across all age groups to examine cortical adaptation and its developmental trajectory. We used functional near infra-red spectroscopy (fNIRS) to chart how different regions in the infant, child and adult brain respond to repeating audiovisual stimuli at varying inter-stimulus intervals (ISIs), using cortical adaptation as a proxy for implicit memory dynamics. We found faster recovery from adaptation in infants compared to children and adults. Specifically, there was an interaction between stimulus presentation rate and age in the right temporal, left parietal and occipital cortical areas. There was also a developmental progression in functional connectivity, with infants displaying significantly lower correlations between regions of interest than children and adults. Taken together, we suggest these findings may reflect the developmental trajectory of cortical adaptation from a learning system optimized for maximal information intake and minimal filtering of stimuli to a specialized integrative system that efficiently filters and adapts to information. HighlightsO_LICortical adaptation is a fundamental mechanism involved in memory and learning, but not much is known about how it develops throughout the lifespan. C_LIO_LIAn identical fNIRS paradigm across 3 different age groups reveals significant differences in cortical adaptation between infants, children and adults. C_LIO_LIFunctional connectivity revealed foundational connections present from infancy, growing stronger and into a specialized adaptation system with age. C_LI These findings suggest a developmental transition from a system optimized for maximal information intake to a specialized learning system, capable of filtering redundant information.

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.

Motor learning alters activities in multiple brain areas. While learning-induced activity change in these areas has been investigated, how the information flow changes in the network across those areas remains to be thoroughly examined. We analysed wide-field calcium imaging data spanning from the premotor cortex to the parietal cortex of marmosets while they learned a two-target forelimb-reaching task. We applied non-negative matrix factorization (NMF) to the activity data and extracted about 30 localized activity components. Encoding model analysis indicated that learning was associated with a decrease in activity components related to hand movements, and an increase in those related to external and reward signals. Causality analysis by embedding entropy (EE) revealed increases in causal links across activity components in different areas and stabilization of the network structure with behavioural improvements. These results indicate that motor learning entails both a redistribution of task-related activity and a reorganization of large-scale cortical network interactions.

A central question in neuroscience is how neural processing generates or encodes behavior. Caenorhabditis elegans is well suited to addressing this question, given its compact nervous system and near-complete structural connectome. Despite this, findings from previous studies remain inconclusive. While some have shown that the connectome can robustly encode specific behaviors such as locomotion, others report that functional connectivity can be reconfigured across behaviors. We aim to understand the relationship between structural connectivity, functional connectivity and biological behavior in silico by using an experimentally motivated computational model leveraging the structural connectome. Stimulation of specific neurons in the model induces oscillatory neural responses, enabling us to infer neuronal functional connectivity. Functional connectivity is found to be stronger among some neurons, allowing us to identify functional communities. We find that electrical synapses play a critical role in determining functional communities, and the resulting mesoscale functional architecture is predominantly gap junctionally assortative. Furthermore, comparison with behavioral circuits shows that locomotion circuits are largely segregated into distinct functional communities while other circuits are more distributed across multiple functional communities. We also observe that stimulation of neurons belonging to these distributed circuits elicits a more synchronized neuronal response compared to stimulation of neurons within the more segregated circuits. This is consistent with the presence of behavioral patterns that originate in one circuit and terminate in another (e.g., chemosensation leading to locomotion), such that stimulation of one circuit can activate the other and eventually result in a synchronized response. We also find a large repertoire of chimera-like synchronization patterns upon stimulation of certain behavioral circuits (chemosensation, mechanosensation) indicating high dynamical flexibility. Overall, our results demonstrate that while certain behaviors are governed by functionally segregated circuits, others emerge from the synchronization of multiple functional communities, which are, to begin with, influenced by the underlying structural connectivity. Author summaryAnimals constantly transform sensory inputs into actions, but it is still unclear how this mapping from neural activity to behavior is implemented in a real nervous system. Caenorhabditis elegans offers a unique testbed for this question because its entire wiring diagram is nearly completely mapped. Yet, previous works have reached mixed conclusions about how well this anatomical circuit diagram predicts actual patterns of activity and behavior. Here, we use a biologically inspired computational model of the C. elegans nervous system to bridge this gap between structure, function, and behavior. By virtually stimulating individual neurons and observing the resulting network-wide oscillations, we infer how strongly different pairs and groups of neurons interact in functional terms. We then use network analysis tools to identify groups of neurons that tend to co-activate, and relate these functional communities to known behavioral circuits for locomotion and sensory processing. We find that gap junctions play a key role in shaping functional communities, and that locomotion-related neurons are more functionally segregated than neurons involved in other behaviors, which are more functionally distributed. Our results suggest that some behaviors rely on specialized, functionally isolated circuits, whereas others emerge from the coordinated activity of multiple functional communities.

The neocortex underlies cognitive abilities that set humans apart from other species. Although Ramon y Cajal initiated its study in the 19th century, much about its fundamental properties remain poorly understood. Biologically detail modeling, has been shown to serve as a tool to understand the modeled system better. By comparing computational models for different species we can highlight functional differences between them, find their anatomical or physiological basis and thus improve our understanding of cortical function. In this study we built a detailed model of a human cortical microcircuit following an established workflow. We compared the human data and results against a previously published reconstruction of rat cortical circuitry. To parametrize the human model, we gathered new original data on human morphological reconstructions, axonal bouton densities, single cell and synaptic recordings. We combined them with data available in the literature and open-sourced databases. We also developed various strategies to overcome the missing data, such as generalizing or adapting data from rodents. The resulting model consists of seven columnar units with similar characteristics. Each column has a radius of 476 {micro}m, a height of 2622 {micro}m, a volume of 1.86 mm3, a total cell density of 24,186 cells/mm3, on the order of 35,000 cells, around 12 million connections and approximately 47 million synapses. Comparing the rat and the human model showed that the human cortex is less dense in terms of cell bodies than the rodent cortex. Human cells have more complex branching, but lower bouton densities than rodent cells. However, the number of connections between cell types is similar.

Hierarchy in the brain emerges across spatial and temporal scales, enabling transformations from rapid sensory encoding to sustained cognitive control. Hierarchical gradients are well established in sensory systems. In contrast, the hierarchical organization of the primate motor cortex remains debated, partly due to its agranular architecture and the absence of clear laminar input-output projections, that obscures the distinction between feedforward and feedback pathways. In particular, the relative hierarchical position of the dorsal premotor cortex (PMd) and the primary motor cortex (M1) cannot be resolved from anatomy alone. To investigate their relative organization, we here adopted a multimodal approach using intrinsic timescales derived from both single-unit spiking activity (SUA) and local field potentials (LFPs) in macaques performing a delayed-match-to-sample reaching task. We found convergent evidence for inter-areal temporal hierarchy, with longer spiking timescales and smaller LFP aperiodic spectral exponents in M1. Across cortical depth, however, temporal organization depended on signal modality. LFP spectral exponents were significantly smaller in deep than superficial layers in both areas, and LFP-autocorrelation timescales were longer in deep layers in M1. In contrast, spiking activity did not show significant laminar differences in intrinsic timescales. Functionally, neurons with longer timescales exhibited more stable representations of the planned movement direction during motor preparation in PMd and slower temporal evolution of movement encoding during execution in both areas. In conclusion, multimodal temporal measures converge on the same hierarchical organization across these two motor areas, with M1 placed higher than PMd. Our study provides the first characterization of intrinsic spiking timescales across cortical layers in any cortical area and shows that laminar temporal organization depends on the neural signal analyzed. This divergence likely reflects their distinct physiological origins. Spikes capture neuronal output, whereas LFPs primarily reflect synaptic and dendritic population activity, potentially integrating differential contributions from apical and basal dendritic inputs.

The hippocampus (HPC), prefrontal cortex (PFC), and thalamic nuclei, such as reuniens (Re), form a reciprocally connected circuit that plays a critical role in processing hippocampus-dependent memory. Accumulating evidence suggests that this triangular modular circuit is crucial for performing cognitive tasks that require context-dependent memory, which belong to a class of behavioral tasks difficult for animals to learn. Experiments are gradually revealing what behavioral information these brain regions represent, but how the triangular circuit gives rise to the observed divisions of labor remains unknown. It is also unclear whether the triangular modular circuit brings any advantage in solving such tasks. Here, we addressed these questions by constructing a prefrontal-thalamo-hippocampal circuit model comprising interconnected long-short-term memory (LSTM) units and training it on contextual memory-dependent spatial navigation tasks. Our model revealed the critical roles of the distinct brain modules. The HPC module encoded spatial information, whereas the PFC module represented the spatiotemporal task structure in a context-dependent manner. The Re module integrated task-relevant information to facilitate learning in the PFC and HPC modules, dynamically harmonizing these modules. The thalamic coordination of the other modules enhanced the systems robustness in learning to navigate complex environments. This division of labor between the HPC, PFC, and Re modules was not specified a priori but emerged through learning, showing an interesting coincidence with the task-related activities of the prefrontal-thalamo-hippocampal circuit. Our results demonstrate that the multi-modular network structure is crucial for robust processing of context-dependent memory.