Neuronal Populations Research Articles

Probabilistic neural transfer function estimation with Bayesian system identification.

Neural population responses in sensory systems are driven by external physical stimuli. This stimulus-response relationship is typically characterized by receptive fields, which have been estimated by neural system identification approaches. Such models usually require a large amount of training data, yet, the recording time for animal experiments is limited, giving rise to epistemic uncertainty for the learned neural transfer functions. While deep neural network models have demonstrated excellent power on neural prediction, they usually do not provide the uncertainty of the resulting neural representations and derived statistics, such as most exciting inputs (MEIs), from in silico experiments. Here, we present a Bayesian system identification approach to predict neural responses to visual stimuli, and explore whether explicitly modeling network weight variability can be beneficial for identifying neural response properties. To this end, we use variational inference to estimate the posterior distribution of each model weight given the training data. Tests with different neural datasets demonstrate that this method can achieve higher or comparable performance on neural prediction, with a much higher data efficiency compared to Monte Carlo dropout methods and traditional models using point estimates of the model parameters. At the same time, our variational method provides us with an effectively infinite ensemble, avoiding the idiosyncrasy of any single model, to generate MEIs. It allows to estimate the uncertainty of stimulus-response function, which we have found to be negatively correlated with the predictive performance at model level and may serve to evaluate models. Furthermore, our approach enables us to identify response properties with credible intervals and to determine whether the inferred features are meaningful by performing statistical tests on MEIs. Finally, in silico experiments show that our model generates stimuli driving neuronal activity significantly better than traditional models in the limited-data regime.

Cross-regional coordination of activity in the human brain during autobiographical self-referential processing

For the human brain to operate, populations of neurons across anatomical structures must coordinate their activity within milliseconds. To date, our understanding of such interactions has remained limited. We recorded directly from the hippocampus (HPC), posteromedial cortex (PMC), ventromedial/orbital prefrontal cortex (OFC), and the anterior nuclei of the thalamus (ANT) during two experiments of autobiographical memory processing that are known from decades of neuroimaging work to coactivate these regions. In 31 patients implanted with intracranial electrodes, we found that the presentation of memory retrieval cues elicited a significant increase of low frequency (LF < 6 Hz) activity followed by cross-regional phase coherence of this LF activity before select populations of neurons within each of the four regions increased high-frequency (HF > 70 Hz) activity. The power of HF activity was modulated by memory content, and its onset followed a specific temporal order of ANT→HPC/PMC→OFC. Further, we probed cross-regional causal effective interactions with repeated electrical pulses and found that HPC stimulations cause the greatest increase in LF-phase coherence across all regions, whereas the stimulation of any region caused the greatest LF-phase coherence between that particular region and ANT. These observations support the role of the ANT in gating, and the HPC in synchronizing, the activity of cortical midline structures when humans retrieve self-relevant memories of their past. Our findings offer a fresh perspective, with high temporal fidelity, about the dynamic signaling and underlying causal connections among distant regions when the brain is actively involved in retrieving self-referential memories from the past.

Investigations on the Ability of the Insular Cortex to Process Peripheral Immunosuppression

The brain and immune system communicate through complex bidirectional pathways, but the specificity by which the brain perceives or even remembers alterations in immune homeostasis is still poorly understood. Recent data revealed that immune-related information under peripheral inflammatory conditions, termed as “immunengram”, were represented in specific neuronal ensembles in the insular cortex (IC). Chemogenetic reactivation of these neuronal ensembles was sufficient to retrieve the inflammatory stages, indicating that the brain can store and retrieve specific immune responses. Against this background, the current approach was designed to investigate the ability of the IC to process states of immunosuppression pharmacologically induced by the mechanistic target of rapamycin (mTOR) inhibitor rapamycin. We here show that the IC perceives the initial state of immunosuppression, reflected by increased deep-brain electroencephalography (EEG) activity during acute immunosuppressive drug treatment. Following an experienced period of immunosuppression, though, diminished splenic cytokine production as formerly induced by rapamycin could not be reinstated by nonspecific chemogenetic activation or inhibition of the IC. These findings suggest that the information of a past, or experienced status of pharmacologically induced immunosuppression is not represented in the IC. Together, the present work extends the view of immune-to-brain communication during the states of peripheral immunosuppression and foster the prominent role of the IC for interoception.

Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes

Biological visual systems have evolved to process natural scenes. A full understanding of visual cortical functions requires a comprehensive characterization of how neuronal populations in each visual area encode natural scenes. Here, we utilized widefield calcium imaging to record V4 cortical response to tens of thousands of natural images in male macaques. Using this large dataset, we developed a deep-learning digital twin of V4 that allowed us to map the natural image preferences of the neural population at 100-µm scale. This detailed map revealed a diverse set of functional domains in V4, each encoding distinct natural image features. We validated these model predictions using additional widefield imaging and single-cell resolution two-photon imaging. Feature attribution analysis revealed that these domains lie along a continuum from preferring spatially localized shape features to preferring spatially dispersed surface features. These results provide insights into the organizing principles that govern natural scene encoding in V4.

Irrational choices via a curvilinear representational geometry for value

We make decisions by comparing values, but it is not yet clear how value is represented in the brain. Many models assume, if only implicitly, that the representational geometry of value is linear. However, in part due to a historical focus on noisy single neurons, rather than neuronal populations, this hypothesis has not been rigorously tested. Here, we examine the representational geometry of value in the ventromedial prefrontal cortex (vmPFC), a part of the brain linked to economic decision-making, in two male rhesus macaques. We find that values are encoded along a curved manifold in vmPFC. This curvilinear geometry predicts a specific pattern of irrational decision-making: that decision-makers will make worse choices when an irrelevant, decoy option is worse in value, compared to when it is better. We observe this type of irrational choices in behavior. Together, these results not only suggest that the representational geometry of value is nonlinear, but that this nonlinearity could impose bounds on rational decision-making.

Flexible neural population dynamics govern the speed and stability of sensory encoding in mouse visual cortex

Time courses of neural responses underlie real-time sensory processing and perception. How these temporal dynamics change may be fundamental to how sensory systems adapt to different perceptual demands. By simultaneously recording from hundreds of neurons in mouse primary visual cortex, we examined neural population responses to visual stimuli at sub-second timescales, during different behavioural states. We discovered that during active behavioural states characterised by locomotion, single-neurons shift from transient to sustained response modes, facilitating rapid emergence of visual stimulus tuning. Differences in single-neuron response dynamics were associated with changes in temporal dynamics of neural correlations, including faster stabilisation of stimulus-evoked changes in the structure of correlations during locomotion. Using Factor Analysis, we examined temporal dynamics of latent population responses and discovered that trajectories of population activity make more direct transitions between baseline and stimulus-encoding neural states during locomotion. This could be partly explained by dampening of oscillatory dynamics present during stationary behavioural states. Functionally, changes in temporal response dynamics collectively enabled faster, more stable and more efficient encoding of new visual information during locomotion. These findings reveal a principle of how sensory systems adapt to perceptual demands, where flexible neural population dynamics govern the speed and stability of sensory encoding.

Serotonergic amplification of odor-evoked neural responses maps onto flexible behavioral outcomes.

Behavioral responses to many odorants are not fixed but are flexible, varying based on organismal needs. How such variations arise and the role of various neuromodulators in achieving flexible neural-to-behavioral mapping is not fully understood. In this study, we examined how serotonin modulates the neural and behavioral responses to odorants in locusts (Schistocerca americana). Our results indicated that serotonin can increase or decrease appetitive behavior in an odor-specific manner. On the other hand, in the antennal lobe, serotonergic modulation enhanced odor-evoked response strength but left the temporal features or the combinatorial response profiles unperturbed. This result suggests that serotonin allows for sensitive and robust recognition of odorants. Nevertheless, the uniform neural response amplification appeared to be at odds with the observed stimulus-specific behavioral modulation. We show that a simple linear model with neural ensembles segregated based on behavioral relevance is sufficient to explain the serotonin-mediated flexible mapping between neural and behavioral responses.

Serotonergic amplification of odor-evoked neural responses maps onto flexible behavioral outcomes

Behavioral responses to many odorants are not fixed but are flexible, varying based on organismal needs. How such variations arise and the role of various neuromodulators in achieving flexible neural-to-behavioral mapping is not fully understood. In this study, we examined how serotonin modulates the neural and behavioral responses to odorants in locusts (Schistocerca americana). Our results indicated that serotonin can increase or decrease appetitive behavior in an odor-specific manner. On the other hand, in the antennal lobe, serotonergic modulation enhanced odor-evoked response strength but left the temporal features or the combinatorial response profiles unperturbed. This result suggests that serotonin allows for sensitive and robust recognition of odorants. Nevertheless, the uniform neural response amplification appeared to be at odds with the observed stimulus-specific behavioral modulation. We show that a simple linear model with neural ensembles segregated based on behavioral relevance is sufficient to explain the serotonin-mediated flexible mapping between neural and behavioral responses.

New Insights into the Cytoarchitecture of the Rat Spinal Trigeminal Nucleus

The spinal trigeminal nucleus (SpV) is a crucial relay station in the central nervous system for processing sensory information from the face, head, and oral cavity. While historically considered a continuous structure, pioneering work by Olszewski revealed its subdivision into three distinct subnuclei: oral, interpolar, and caudal. Understanding the cytoarchitectonics of these subnuclei is fundamental for elucidating their functional roles in sensory perception and pain modulation. In this study, we conducted a detailed examination of the structural organization within each subnucleus of the SpV using histological techniques. Our findings reveal distinct cytoarchitectonic features characteristic of each subnucleus by providing insights into the spatial distribution and density of neuronal populations across the rostrocaudal extent of the SpV. These results contribute to a deeper understanding of the neural circuitry underlying sensory processing in the trigeminal system and may have implications for the diagnosis and treatment of disorders involving altered trigeminal function.

Neuromodulatory Compensation of Cortical Neural Activity on Electrodeposited Pt/Ir Modified Microelectrode Arrays for Temperature Transients.

Temperature has a profound influence on various neuromodulation processes and has emerged as a focal point. However, the effects of acute environmental temperature fluctuations on cultured cortical networks have been inadequately elucidated. To bridge this gap, we have developed a brain-on-a-chip platform integrating cortical networks and electrodeposited Pt/Ir modified microelectrode arrays (MEAs) with 3D-printed bear-shaped triple chambers, facilitating control of temperature transients. This innovative system administers thermal stimuli while concurrently monitoring neuronal activity, including spikes and local field potentials, from 60 microelectrodes (diameter: 30 μm; impedance: 9.34 ± 1.37 kΩ; and phase delay: -45.26 ± 2.85°). Temperature transitions of approximately ±10 °C/s were applied to cortical networks on MEAs via in situ perfusion within the triple chambers. Subsequently, we examined the spatiotemporal dynamics of the brain-on-a-chip under temperature regulation at both the group level (neuronal population) and their interactions (network dynamics) and the individual level (cellular activity). Specifically, we found that after the temperature reduction neurons enhanced the overall information transmission efficiency of the network through synchronous firing to compensate for the decreased efficiency of single-cell level information transmission, in contrast to temperature elevation. By leveraging the integration of high-performance MEAs with perfusion chambers, this investigation provides a comprehensive understanding of the impact of temperature on the spatiotemporal dynamics of neural networks, thereby facilitating future exploration of the intricate interplay between temperature and brain function.

Spiking Laguerre Volterra networks—predicting neuronal activity from local field potentials

Objective. Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations. Approach. Here, we fill this gap by proposing novel spiking Laguerre–Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics. Main results. The proposed networks were applied on extracellular microelectrode recordings of Parkinson’s Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features. Significance. These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.

A common variant in the iron regulatory gene (Hfe) alters the metabolic and transcriptional landscape in brain regions vulnerable to neurodegeneration.

The role of iron dyshomeostasis in neurodegenerative disease has implicated the involvement of genes that regulate brain iron. The homeostatic iron regulatory gene (HFE) has been at the forefront of these studies given the role of the H63D variant (H67D in mice) in increasing brain iron load. Despite iron's role in oxidative stress production, H67D mice have shown robust protection against neurotoxins and improved recovery from intracerebral hemorrhage. Previous data support the notion that H67D mice adapt to the increased brain iron concentrations and hence develop a neuroprotective environment. This adaptation is particularly evident in the lumbar spinal cord (LSC) and ventral midbrain (VM), both relevant to neurodegeneration. We studied C57BL6/129 mice with homozygous H67D compared to WT HFE. Immunohistochemistry was used to analyze dopaminergic (in the VM) and motor (in the LSC) neuron population maturation in the first 3 months. Immunoblotting was used to measure protein carbonyl content and the expression of oxidative phosphorylation complexes. Seahorse assay was used to analyze metabolism of mitochondria isolated from the LSC and VM. Finally, a Nanostring transcriptomic analysis of genes relevant to neurodegeneration within these regions was performed. Compared to WT mice, we found no difference in the viability of motor neurons in the LSC, but the dopaminergic neurons in H67D mice experienced significant decline before 3 months of age. Both regions in H67D mice had alterations in oxidative phosphorylation complex expression indicative of stress adaptation. Mitochondria from both regions of H67D mice demonstrated metabolic differences compared to WT. Transcriptional differences in these regions of H67D mice were related to cell structure and adhesion as well as cell signaling. Overall, we found that the LSC and VM undergo significant and distinct metabolic and transcriptional changes in adaptation to iron-related stress induced by the H67D HFE gene variant.

Adolescent Alcohol Exposure Promotes Mechanical Allodynia and Alters Synaptic Function at Inputs from the Basolateral Amgydala to the Prelimbic Cortex.

Binge drinking is common among adolescents despite mounting evidence linking it to various adverse health outcomes that include heightened pain perception. The prelimbic (PrL) cortex is vulnerable to insult from adolescent alcohol exposure and receives input from the basolateral amygdala (BLA) while sending projections to the ventrolateral periaqueductal gray (vlPAG) - two brain regions implicated in nociception. In this study, adolescent intermittent ethanol (AIE) exposure was carried out in male and female rats using a vapor inhalation procedure. Assessments of mechanical and thermal sensitivity revealed that AIE exposure induced protracted mechanical allodynia. To investigate synaptic function at BLA inputs onto defined populations of PrL neurons, retrobeads and viral labelling were combined with optogenetics and slice electrophysiology. Recordings from retrobead labelled cells in the PrL revealed AIE reduced BLA driven feedforward inhibition of neurons projecting from the PrL to the vlPAG, resulting in augmented excitation/inhibition (E/I) balance and increased intrinsic excitability. Consistent with this finding, recordings from virally tagged PrL parvalbumin interneurons (PVINs) demonstrated that AIE exposure reduced both E/I balance at BLA inputs onto PVINs and PVIN intrinsic excitability. These findings provide compelling evidence that AIE alters synaptic function and intrinsic excitability within a prefrontal nociceptive circuit.

The role of binocular disparity and attention in the neural representation of multiple moving stimuli in the visual cortex.

A fundamental process of vision involves segmenting visual scenes into distinct objects and surfaces. Stereoscopic depth and visual motion are important cues for segmentation. However, the mechanisms by which the visual system utilizes depth and motion cues to segment multiple objects are not fully understood. We investigated how neurons in the middle-temporal (MT) cortex of macaque monkeys represented overlapping surfaces located at different depths and moved simultaneously in different directions. We recorded neuronal activities in the MT of three male monkeys while they performed discrimination tasks under different attention conditions. We found that neuronal responses to overlapping surfaces showed a robust bias toward the horizontal binocular disparity of one of the two surfaces. Across all animals, the disparity bias of a neuron in response to two surfaces positively correlated with the neuron's disparity preference for a single surface. For two animals, neurons that preferred near disparities of single surfaces (near neurons) showed a near bias to overlapping stimuli, whereas neurons that preferred far disparities (far neurons) showed a far bias. For the third animal, both near and far neurons displayed a near bias, although the near neurons showed a stronger near bias than the far neurons. Interestingly, for all three animals, both near and far neurons exhibited an initial near bias relative to the average of the responses to the individual surfaces. Although attention can modulate neuronal response to better represent the attended surface, the disparity bias was not due to attention. We also found that the effect of attention modulation on MT responses was consistent with object-based rather than feature-based attention. We proposed a model in which the pool size of the neuron population that weighs the responses to individual stimulus components can be variable. This model is a novel extension of the standard normalization model and provides a unified explanation for the disparity bias observed across animals. Our results revealed the neural encoding rule for multiple stimuli located at different depths and presented new evidence of response modulation by object-based attention in MT. The disparity bias allows subgroups of neurons to preferentially represent individual surfaces of multiple stimuli at different depths, thereby facilitating segmentation.

Neurons for infant social behaviors in the mouse zona incerta.

Understanding the neural basis of infant social behaviors is crucial for elucidating the mechanisms of early social and emotional development. In this work, we report a specific population of somatostatin-expressing neurons in the zona incerta (ZISST) of preweaning mice that responds dynamically to social interactions, particularly those with their mother. Bidirectional neural activity manipulations in pups revealed that widespread connectivity of preweaning ZISST neurons to sensory, emotional, and cognitive brain centers mediates two key adaptive functions associated with maternal presence: the reduction of behavior distress and the facilitation of learning. These findings reveal a population of neurons in the infant mouse brain that coordinate the positive effects of the relationship with the mother on an infant's behavior and physiology.

Calmodulin triggers activity-dependent rRNA biogenesis via interaction with DDX21.

Protein synthesis in response to neuronal activity, known as activity-dependent translation, is critical for synaptic plasticity and memory formation. However, the signaling cascades that couple neuronal activity to the translational events remains elusive. In this study, we identified the role of calmodulin (CaM), a conserved Ca2+-binding protein, in rRNA biogenesis in neurons. We found the CaM-regulated rRNA synthesis is Ca2+-dependent and necessary for nascent protein synthesis and axon growth in hippocampal neurons. Mechanistically, CaM interacts with nucleolar DDX21 in a Ca2+-dependent manner to regulate nascent rRNA transcription within nucleoli. We further found CaM alters the conformation of DDX21 to liberate the DDX21-sequestered RPA194, the catalytic subunit of RNA polymerase I, to facilitate transcription of rDNA. Using high-throughput screening, we identified the small molecules Batefenterol and Indacaterol that attenuate the CaM-DDX21 interaction and suppress nascent rRNA synthesis and axon growth in hippocampal neurons. These results unveiled the previously unrecognized role of CaM as a messenger to link the activity-induced Ca2+ influx to the nucleolar events essential for protein synthesis. We thus identified the ability of CaM to transmit information to the nucleoli of neurons in response to stimulation.Significance statement Protein synthesis in response to neuronal activity, known as activity-dependent translation, is critical for synaptic plasticity and long-term memory formation. In this study, we identify the novel role of calmodulin (CaM), a highly conserved Ca2+-binding protein, which is well-known by regulating myriad vital biological processes, in activity-dependent translation by regulating rRNA synthesis in neurons. We find that CaM can shuttle into the nucleolus upon depolarization and modulate the activity-induced de novo rRNA biogenesis, which is associated with ribosome assembly and protein synthesis in neurons. Mechanistically, CaM interacts with DDX21, an RNA helicase directly associated with Pol I subunit, to regulate the transcription of rDNA. Our study demonstrates CaM as a messenger linking neuronal activity to ribosome-dependent protein biosynthesis.

Chromatin plasticity predetermines neuronal eligibility for memory trace formation.

Memories are encoded by sparse populations of neurons but how such sparsity arises remains largely unknown. We found that a neuron's eligibility to be recruited into the memory trace depends on its epigenetic state prior to encoding. Principal neurons in the mouse lateral amygdala display intrinsic chromatin plasticity, which when experimentally elevated favors neuronal allocation into the encoding ensemble. Such chromatin plasticity occurred at genomic regions underlying synaptic plasticity and was accompanied by increased neuronal excitability in single neurons in real time. Lastly, optogenetic silencing of the epigenetically altered neurons prevented memory expression, revealing a cell-autonomous relationship between chromatin plasticity and memory trace formation. These results identify the epigenetic state of a neuron as a key factor enabling information encoding.

State- and circuit-dependent opponent-processing of fear.

The presence of valence coding neurons in the basolateral amygdala (BLA) that form distinct projections to other brain regions implies functional opposition between aversion and reward during learning. However, evidence for opponent interactions in fear learning is sparse and may only be apparent under certain conditions. Here we test this possibility by studying the roles of the BLA→central amygdala (CeA) and BLA→nucleus accumbens (Acb) pathways in fear learning in male rats. First, we assessed the organisation of these pathways in the rat brain. BLA→CeA and BLA→Acb pathways were largely segregated in the BLA but shared overlapping molecular profiles. Then we assessed activity of the BLA→CeA and BLA→Acb pathways during two different forms of fear learning - fear learning in a neutral context and fear learning in a reward context. BLA → CeA neurons were robustly recruited by footshock regardless of where fear learning occurred whereas recruitment of BLA→Acb neurons was state-dependent because footshock only recruited this pathway in a reward context. Finally, we assessed the causal roles of activity in these pathways in fear learning. Photoinhibition of the BLA→CeA pathway during the footshock US impaired fear learning, regardless of where fear learning occurred. In contrast, photoinhibition of the BLA→Acb pathway augmented fear learning, but only in the reward context. Taken together, our findings show circuit- and state-dependent opponent processing of fear. Footshock activity in the BLA → Acb pathway limits how much fear is learned.Significance Statement Here we identify a fear opponent process in the brain. We show that an aversive event can recruit distinct populations of neurons in the rat basolateral amygdala. One population projects to the central amygdala to promote fear learning. A second population projects to the nucleus accumbens to oppose fear learning. These nucleus accumbens projecting neurons limit how much fear is learned and are candidates for therapeutic targeting to minimize the amount of fear learned after a traumatic experience.

Single-Trial Representations of Decision-Related Variables by Decomposed Frontal Corticostriatal Ensemble Activity.

The frontal cortex-striatum circuit plays a pivotal role in adaptive goal-directed behaviors. However, it remains unclear how decision-related signals are mediated through cross-regional transmission between the medial frontal cortex and the striatum by neuronal ensembles in making decision based on outcomes of past action. Here, we analyzed neuronal ensemble activity obtained through simultaneous multiunit recordings in the secondary motor cortex (M2) and dorsal striatum (DS) in rats performing an outcome-based left-or-right choice task. By adopting tensor component analysis (TCA), a single-trial-based unsupervised dimensionality reduction approach, for concatenated ensembles of M2 and DS neurons, we identified distinct three spatiotemporal neural dynamics (TCA components) at the single-trial level specific to task-relevant variables. Choice-position-selective neural dynamics reflected the positions chosen and was correlated with the trial-to-trial fluctuation of behavioral variables. Intriguingly, choice-pattern-selective neural dynamics distinguished whether the incoming choice was a repetition or a switch from the previous choice before a response choice. Other neural dynamics was selective to outcome and increased within-trial activity following response. Our results demonstrate how the concatenated ensembles of M2 and DS process distinct features of decision-related signals at various points in time. Thereby, the M2 and DS collaboratively monitor action outcomes and determine the subsequent choice, whether to repeat or switch, for action selection.

Beat frequency induced transitions in synchronization dynamics

In neurosciences, the brain processes information via the firing patterns of connected neurons operating across a spectrum of frequencies. To better understand the effects of these frequencies in the neuron dynamics, we have simulated a neuronal network of Izhikevich neurons to examine the interaction between frequency allocation and intermittent phase synchronization dynamics. As the synchronized population of neurons passes through a bifurcation, an additional frequency mode emerges, enabling a match in the mean frequency while retaining distinct most probable frequencies among neurons. Subsequently, the network intermittently transits between two patterns, one partially synchronized and the other unsynchronized. Through our analysis, we demonstrate that the frequency changes on the network lead to characteristic transition times between synchronization states. Moreover, these transitions adhere to beat frequency statistics when the neurons’ frequencies differ by multiples of a frequency gap. Finally, our results can improve the performance in predicting transitions on problems where the beat frequency strongly influences the dynamics.