Neuronal Populations Research Articles

Histomorphology and Histomorphometrics of the retina in Juvenile and Adult African Giant Rats (Cricetomys gambianus)

Aging is accompanied with various forms of functional ultrastructural and morphological changes in the eye, including the retina, leading to vision deterioration. However, age related retinal degenerative changes requires further elucidation especially as all previous studies on age-related change of the retina were done in mutant mouse strain models of hereditary retinal degenerations. The potential of using African giant rats (AGRs) as models for ageing in the eye, especially retina, was explored in this study. A total of 14 AGRs divided into two age groups (juvenile and adult) were used to study the histomorphology and histomorphometrics of the retina as well as retinal astrocyte morphology and heterogeneity. Histological findings included retinal atrophy and hypoplasia, with cellular swellings of neuronal cell populations and astrocytes soma and ramifications in the retina of adult compared to juvenile AGR. We suggest that AGR be used as animal model for translational research into normal aging process of the retina, as well as to elucidate the process of age-related neuronal cells loss.

Touch and slippage detection in robotic hands with spiking neural networks

Significant research efforts have focused on advancing upper limb prostheses, addressing issues such as dexterity, embodiment, weight, and resistance. These key features require enhancements to mitigate prosthesis abandonment. Sensory feedback is a vital feature for improving the dexterity and embodiment of prostheses for patients. Current techniques utilize electronic signals for feedback to control systems and to partially restore tactile sensations to amputees. However, it has already been demonstrated that, in order to elicit responses from biological neurons signals must be converted into neuromorphic tactile information. Most of those demonstrations relied on algorithmic implementations of mechanoreceptor and neuron models running on classic processor cores. However, integrating real-time encoding and control strategies into such architectures is challenging due to computational complexity and high power usage. A prospective solution is to increasingly incorporate neuromorphic technologies for prosthesis sensing and control. In this work, we demonstrate the feasibility of using spiking neural networks for performing two key functions in controlling upper limb prosthesis: touch and slippage detection. We found that a simple two-layered architecture with a few tens of neurons is sufficient to identify slip and touch with nearly 100% accuracy. The first layer consists of a heterogeneous population of neurons with their parameters adjusted to model slowly and fast-adapting type 1 mechanoreceptors. An output layer classifies touch or slip events. Furthermore, simulations showed that implementing the network in ultra-low-power neuromorphic hardware, such as Loihi, has the potential to reduce power consumption by up to 162 times, with respect to a von Neumann processor.

Electrophysiological activity pattern of mouse hippocampal CA1 and dentate gyrus under isoflurane anesthesia.

General anesthesia can impact a patient's memory and cognition by influencing hippocampal function. The CA1 and dentate gyrus (DG), serving as the primary efferent and gateway of the hippocampal trisynaptic circuit facilitating cognitive learning and memory functions, exhibit significant differences in cellular composition, molecular makeup, and responses to various stimuli. However, the effects of isoflurane-induced general anesthesia on CA1 and DG neuronal activity in mice are not well understood. In this study, utilizing electrophysiological recordings, we examined neuronal population dynamics and single-unit activity (SUA) of CA1 and DG in freely behaving mice during natural sleep and general anesthesia. Our findings reveal that isoflurane anesthesia shifts local field potential (LFP) to delta frequency and reduces the firing rate of SUA in both CA1 and DG, compared to wakefulness. Additionally, the firing rates of DG neurons are significantly lower than CA1 neurons during isoflurane anesthesia, and the recovery of theta power is slower in DG than in CA1 during the transition from anesthesia to wakefulness, indicating a stronger and more prolonged impact of isoflurane anesthesia on DG. This work presents a suitable approach for studying brain activities during general anesthesia and provides evidence for distinct effects of isoflurane anesthesia on hippocampal subregions.

Social Status as a Latent Variable in the Amygdala of Observers of Social Interactions.

Monkeys infer the social status of conspecifics from videos of simulated dyadic interactionsDuring fixations neural populations signal the social status of the attended individualsNeurons in the amygdala jointly encode the status of interacting individuals. Third party-viewers of pairwise dominant-subordinate interactions infer social status from the observed behaviors. Neurons in the amygdala are tuned to the inferred dominant/subordinate status of both individuals.

Noise Enhances Excitability of a Neuronal Population with Heterogeneous Excitatory Neurons

AbstractCortical excitability is sensitive to noise perturbations despite homeostatic regulation. However, modeling the effect of noise in a heterogeneous neuronal population is a mathematical challenge. Using a mean‐field model of a randomly connected excitatory population, we investigated how noise affects excitability at the resting state. Stable equilibrium analysis revealed that increasing noise intensity enhances excitability, indexed by synaptic conductance. Further, this enhancement disappeared when excitability is sufficiently high due to strong mean input current, but not influenced by the connection probability of the population. Finally, the mechanism of noise‐induced enhancement was explained by eigenvalues moving toward the imaginary axis. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

A novel behavioral paradigm using mice to study predictive postural control.

The ability to anticipate disturbances and adjust one's posture accordingly-known as "predictive postural control"-is crucial for preventing falls and for advancing robotics. Human postural studies often face limitations with measurement tools and sample sizes, hindering insight into underlying neural mechanisms. To address these limitations, we developed a postural perturbation task for freely moving mice, modeled after those used in human studies. Using a dynamic platform, we delivered reproducible perturbations with or without preceding auditory cues and quantified how the auditory cue affects postural responses to perturbations. Our work provides validation of a new postural control model, which opens the door to the types of neural population recordings and circuit manipulation that are currently possible only in mice.

Detection of Memory Engrams in Mammalian Neuronal Circuits.

It has long been assumed that activity patterns persist in neuronal circuits after they are first experienced, as part of the process of information processing and storage by the brain. However, these "reverberations" of current activity have not been directly observed on a single-neuron level in a mammalian system. Here we demonstrate that specific induced activity patterns are retained in mature cultured hippocampal neuronal networks. Neurons within the network are induced to fire at a single frequency or in a more complex pattern containing two distinct frequencies. After the stimulation was stopped, the subsequent neuronal activity of hundreds of neurons in the network was monitored. In the case of single-frequency stimulation, it was observed that many of the neurons continue to fire at the same frequency that they were stimulated to fire at. Using a recurrent neural network trained to detect specific, more complex patterns, we found that the multiple-frequency stimulation patterns were also retained within the neuronal network. Moreover, it appears that the component frequencies of the more complex patterns are stored in different populations of neurons and neuron subtypes.

Enhanced In Situ Spatial Proteomics by Effective Combination of MALDI Imaging and LC-MS/MS

Highly specialized cells are fundamental for the proper functioning of complex organs. Variations in cell-type-specific gene expression and protein composition have been linked to a variety of diseases. Investigation of the distinctive molecular makeup of these cells within tissues is therefore critical in biomedical research. Although several technologies have emerged as valuable tools to address this cellular heterogeneity, most workflows lack sufficient in situ resolution and are associated with high costs and extremely long analysis times. Here, we present a combination of experimental and computational approaches that allows a more comprehensive investigation of molecular heterogeneity within tissues than by either shotgun LC-MS/MS or MALDI imaging alone. We applied our pipeline to the mouse brain, which contains a wide variety of cell types that not only perform unique functions but also exhibit varying sensitivities to insults. We explored the distinct neuronal populations within the hippocampus, a brain region crucial for learning and memory that is involved in various neurological disorders. As an example, we identified the groups of proteins distinguishing the neuronal populations of the dentate gyrus (DG) and the cornu ammonis (CA) in the same brain section. Most of the annotated proteins matched the regional enrichment of their transcripts, thereby validating the method. As the method is highly reproducible, the identification of individual masses through the combination of MALDI-IMS and LC-MS/MS methods can be used for the much faster and more precise interpretation of MALDI-IMS measurements only. This greatly speeds up spatial proteomic analyses and allows the detection of local protein variations within the same population of cells. The method’s general applicability has the potential to be used to investigate different biological conditions and tissues and a much higher throughput than other techniques making it a promising approach for clinical routine applications.

Chronic activation of a negative engram induces behavioral and cellular abnormalities.

Negative memories engage a brain and body-wide stress response in humans that can alter cognition and behavior. Prolonged stress responses induce maladaptive cellular, circuit, and systems-level changes that can lead to pathological brain states and corresponding disorders in which mood and memory are affected. However, it is unclear if repeated activation of cells processing negative memories induces similar phenotypes in mice. In this study, we used an activity-dependent tagging method to access neuronal ensembles and assess their molecular characteristics. Sequencing memory engrams in mice revealed that positive (male-to-female exposure) and negative (foot shock) cells upregulated genes linked to anti- and pro-inflammatory responses, respectively. To investigate the impact of persistent activation of negative engrams, we chemogenetically activated them in the ventral hippocampus over 3 months and conducted anxiety and memory-related tests. Negative engram activation increased anxiety behaviors in both 6- and 14-month-old mice, reduced spatial working memory in older mice, impaired fear extinction in younger mice, and heightened fear generalization in both age groups. Immunohistochemistry revealed changes in microglial and astrocytic structure and number in the hippocampus. In summary, repeated activation of negative memories induces lasting cellular and behavioral abnormalities in mice, offering insights into the negative effects of chronic negative thinking-like behaviors on human health.

VTA μ-opioidergic neurons facilitate low sociability in protracted opioid withdrawal.

Opioids initiate dynamic maladaptation in brain reward and affect circuits that occur throughout chronic exposure and withdrawal that persist beyond cessation. Protracted withdrawal is characterized by negative affective behaviors such as heightened anxiety, irritability, dysphoria, and anhedonia, which pose a significant risk factor for relapse. While the ventral tegmental area (VTA) and mu-opioid receptors (MORs) are critical for opioid reinforcement, the specific contributions of VTAMOR neurons in mediating protracted withdrawal-induced negative affect is not fully understood. In our study, we elucidate the role of VTAMOR neurons in mediating negative affect and altered brain-wide neuronal activities following opioid exposure and withdrawal in male and female mice. Utilizing a chronic oral morphine administration model, we observe increased social deficit, anxiety-related, and despair-like behaviors during protracted withdrawal. VTAMOR neurons show heightened neuronal FOS activation at the onset of withdrawal and connect to an array of brain regions that mediate reward and affective processes. Viral re-expression of MORs selectively within the VTA of MOR knockout mice demonstrates that the disrupted social interaction observed during protracted withdrawal is facilitated by this neural population, without affecting other protracted withdrawal behaviors. Lastly, VTAMORs contribute to heightened neuronal FOS activation in the anterior cingulate cortex (ACC) in response to an acute morphine challenge, suggesting their unique role in modulating ACC-specific neuronal activity. These findings identify VTAMOR neurons as critical modulators of low sociability during protracted withdrawal and highlight their potential as a mechanistic target to alleviate negative affective behaviors associated with opioid withdrawal.

Neural population dynamics optimization algorithm: A novel brain-inspired meta-heuristic method

Meta-heuristic algorithms are popular for their efficiency in solving complex optimization problems. Although there are many known algorithms, identifying ways to improve their performance remains an important research area. This paper proposes a brain neuroscience-inspired meta-heuristic algorithm called the Neural Population Dynamics Optimization Algorithm (NPDOA). There are three strategies in NPDOA. (1) The attractor trending strategy drives neural populations towards optimal decisions, thereby ensuring exploitation capability. (2) The coupling disturbance strategy deviates neural populations from attractors by coupling with other neural populations, thus improving exploration ability. (3) The information projection strategy controls the communication between neural populations, enabling a transition from exploration to exploitation. The results of benchmark and practical problems verified the effectiveness of NPDOA.

Recent visual experience reshapes V4 neuronal activity and improves perceptual performance.

Recent visual experience heavily influences our visual perception, but how this is mediated by the reshaping of neuronal activity to alter and improve perceptual discrimination remains unknown. We recorded from populations of neurons in visual cortical area V4 while monkeys performed a natural image change detection task under different experience conditions. We found that maximizing the recent experience with a particular image led to an improvement in the ability to detect a change in that image. This improvement was associated with decreased neural responses to the image, consistent with neuronal changes previously seen in studies of adaptation and expectation. We found that the magnitude of behavioral improvement was correlated with the magnitude of response suppression. Furthermore, this suppression of activity led to an increase in signal separation, providing evidence that a reduction in activity can improve stimulus encoding. Within populations of neurons, greater recent experience was associated with decreased trial-to-trial shared variability, indicating that a reduction in variability is a key means by which experience influences perception. Taken together, the results of our study contribute to an understanding of how recent visual experience can shape our perception and behavior through modulating activity patterns in mid-level visual cortex.

Jointly efficient encoding and decoding in neural populations.

The efficient coding approach proposes that neural systems represent as much sensory information as biological constraints allow. It aims at formalizing encoding as a constrained optimal process. A different approach, that aims at formalizing decoding, proposes that neural systems instantiate a generative model of the sensory world. Here, we put forth a normative framework that characterizes neural systems as jointly optimizing encoding and decoding. It takes the form of a variational autoencoder: sensory stimuli are encoded in the noisy activity of neurons to be interpreted by a flexible decoder; encoding must allow for an accurate stimulus reconstruction from neural activity. Jointly, neural activity is required to represent the statistics of latent features which are mapped by the decoder into distributions over sensory stimuli; decoding correspondingly optimizes the accuracy of the generative model. This framework yields in a family of encoding-decoding models, which result in equally accurate generative models, indexed by a measure of the stimulus-induced deviation of neural activity from the marginal distribution over neural activity. Each member of this family predicts a specific relation between properties of the sensory neurons-such as the arrangement of the tuning curve means (preferred stimuli) and widths (degrees of selectivity) in the population-as a function of the statistics of the sensory world. Our approach thus generalizes the efficient coding approach. Notably, here, the form of the constraint on the optimization derives from the requirement of an accurate generative model, while it is arbitrary in efficient coding models. Moreover, solutions do not require the knowledge of the stimulus distribution, but are learned on the basis of data samples; the constraint further acts as regularizer, allowing the model to generalize beyond the training data. Finally, we characterize the family of models we obtain through alternate measures of performance, such as the error in stimulus reconstruction. We find that a range of models admits comparable performance; in particular, a population of sensory neurons with broad tuning curves as observed experimentally yields both low reconstruction stimulus error and an accurate generative model that generalizes robustly to unseen data.

Investigating the Impact of Intracerebroventricular Streptozotocin on Female Rats with and without Ovaries: Implications for Alzheimer's Disease.

To contribute to research on female models of Alzheimer's disease (AD), our aim was to study the effect of intracerebroventricular (ICV) injection of streptozotocin (STZ) in female rats, and to evaluate a potential neuroprotective action of ovarian steroids against STZ. Female rats were either ovariectomized (OVX) or kept with ovaries (Sham) two weeks before ICV injections. Animals were injected with either vehicle (artificial cerebrospinal fluid, aCSF) or STZ (3mg/kg) and separated into four experimental groups: Sham + aCSF, Sham + STZ, OVX + aCSF and OVX + STZ. Nineteen days post-injection, we assessed different behavioral aspects: burying, anxiety and exploration, object recognition memory, spatial memory, and depressive-like behavior. Immunohistochemistry and Immunoblot analyses were performed in the hippocampus to examine changes in AD-related proteins and neuronal and microglial populations. STZ affected burying and exploratory behavior depending on ovarian status, and impaired recognition but not spatial memory. STZ and ovariectomy increased depressive-like behavior. Interestingly, STZ did not alter the expression of β-amyloid peptide or Tau phosphorylated forms. STZ affected the neuronal population from the Dentate Gyrus, where immature neurons were more vulnerable to STZ in OVX rats. Regarding microglia, STZ increased reactive cells, and the OVX + STZ group showed an increase in the total cell number. In sum, STZ partially affected female rats, compared to what was previously reported for males. Although AD is more frequent in women, reports about the effect of ICV-STZ in female rats are scarce. Our work highlights the need to deepen into the effects of STZ in the female brain and study possible sex differences.

How to design optimal brain stimulation to modulate phase-amplitude coupling?

Objective. Phase-amplitude coupling (PAC), the coupling of the amplitude of a faster brain rhythm to the phase of a slower brain rhythm, plays a significant role in brain activity and has been implicated in various neurological disorders. For example, in Parkinson’s disease, PAC between the beta (13–30 Hz) and gamma (30–100 Hz) rhythms in the motor cortex is exaggerated, while in Alzheimer’s disease, PAC between the theta (4–8 Hz) and gamma rhythms is diminished. Modulating PAC (i.e. reducing or enhancing PAC) using brain stimulation could therefore open new therapeutic avenues. However, while it has been previously reported that phase-locked stimulation can increase PAC, it is unclear what the optimal stimulation strategy to modulate PAC might be. Here, we provide a theoretical framework to narrow down the experimental optimisation of stimulation aimed at modulating PAC, which would otherwise rely on trial and error. Approach. We make analytical predictions using a Stuart–Landau model, and confirm these predictions in a more realistic model of coupled neural populations. Main results. Our framework specifies the critical Fourier coefficients of the stimulation waveform which should be tuned to optimally modulate PAC. Depending on the characteristics of the amplitude response curve of the fast population, these components may include the slow frequency, the fast frequency, combinations of these, as well as their harmonics. We also show that the optimal balance of energy between these Fourier components depends on the relative strength of the endogenous slow and fast rhythms, and that the alignment of fast components with the fast rhythm should change throughout the slow cycle. Furthermore, we identify the conditions requiring to phase-lock stimulation to the fast and/or slow rhythms. Significance. Together, our theoretical framework lays the foundation for guiding the development of innovative and more effective brain stimulation aimed at modulating PAC for therapeutic benefit.

Initial and corrective submovement encoding differences within primary motor cortex during precision reaching.

Precision reaching often requires corrective submovements to obtain the desired goal. Most studies of reaching have focused on single initial movements, and implied the cortical encoding model was the same for all submovements. However, corrective submovements may show different encoding patterns from the initial submovement with distinct patterns of activation across the population. Two rhesus macaques performed a precision center-out-task with small targets. Neural activity from single units in the primary motor cortex and associated behavioral data were recorded to evaluate movement characteristics. Neural population data and individual neuronal firing rates identified with a peak finding algorithm to identify peaks in hand speed were examined for encoding differences between initial and corrective submovements. Individual neurons were fitted with a regression model that included the reach vector, position, and speed to predict firing rate. For both initial and corrective submovements, the largest effect remained movement direction. We observed a large subset changed their preferred direction greater than 45° between initial and corrective submovements. Neuronal depth of modulation also showed considerable variation when adjusted for movement speed. By using principal component analysis, neural trajectories of initial and corrective submovements progressed through different neural subspaces. These findings all suggest that different neural encoding patterns exist for initial and corrective submovements within the cortex. We hypothesize that this variation in how neurons change to encode small, corrective submovements might allow for a larger portion of the neural space being used to encode a greater range of movements with varying amplitudes and levels of precision.NEW & NOTEWORTHY Neuronal recordings matched with kinematic behavior were collected in a precision center-out task that often required corrective movements. We reveal large differences in preferred direction and depth of modulation between initial and corrective submovements across the neural population. We then present a model of the neural population describing how these shifts in tuning create different subspaces for signaling initial and corrective movements likely to improve motor precision.

Selective Functional Connectivity between Ocular Dominance Columns in the Primary Visual Cortex.

The primary visual cortex (V1) in humans and many animals is comprised of fine-scale neuronal ensembles that respond preferentially to the stimulation of one eye over the other, also known as the ocular dominance columns (ODCs). Despite its importance in shaping our perception, to date, the nature of the functional interactions between ODCs has remained poorly understood. In this work, we aimed to improve our understanding of the interaction mechanisms between fine-scale neuronal structures distributed within V1. To that end, we applied high-resolution functional MRI to study mechanisms of functional connectivity between ODCs. Using this technique, we quantified the level of functional connectivity between ODCs as a function of the ocular preference of ODCs, showing that alike ODCs are functionally more connected compared to unalike ones. Through these experiments, we aspired to contribute to filling the gap in our knowledge of the functional connectivity of ODCs in humans as compared to animals.

Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed.

Systems neuroscience has experienced an explosion of new tools for reading and writing neural activity, enabling exciting new experiments such as all-optical or closed-loop control that effect powerful causal interventions. At the same time, improved computational models are capable of reproducing behavior and neural activity with increasing fidelity. Unfortunately, these advances have drastically increased the complexity of integrating different lines of research, resulting in the missed opportunities and untapped potential of suboptimal experiments. Experiment simulation can help bridge this gap, allowing model and experiment to better inform each other by providing a low-cost testbed for experiment design, model validation, and methods engineering. Specifically, this can be achieved by incorporating the simulation of the experimental interface into our models, but no existing tool integrates optogenetics, two-photon calcium imaging, electrode recording, and flexible closed-loop processing with neural population simulations. To address this need, we have developed Cleo: the Closed-Loop, Electrophysiology, and Optophysiology experiment simulation testbed. Cleo is a Python package enabling injection of recording and stimulation devices as well as closed-loop control with realistic latency into a Brian spiking neural network model. It is the only publicly available tool currently supporting two-photon and multi-opsin/wavelength optogenetics. To facilitate adoption and extension by the community, Cleo is open-source, modular, tested, and documented, and can export results to various data formats. Here we describe the design and features of Cleo, validate output of individual components and integrated experiments, and demonstrate its utility for advancing optogenetic techniques in prospective experiments using previously published systems neuroscience models.

A vagal-brainstem interoceptive circuit for cough-like defensive behaviors in mice.

Coughing is a respiratory behavior that plays a crucial role in protecting the respiratory system. Here we show that the nucleus of the solitary tract (NTS) in mice contains heterogenous neuronal populations that differentially control breathing. Within these subtypes, activation of tachykinin 1 (Tac1)-expressing neurons triggers specific respiratory behaviors that, as revealed by our detailed characterization, are cough-like behaviors. Chemogenetic silencing or genetic ablation of Tac1 neurons inhibits cough-like behaviors induced by tussive challenges. These Tac1 neurons receive synaptic inputs from the bronchopulmonary chemosensory and mechanosensory neurons in the vagal ganglion and coordinate medullary regions to control distinct aspects of cough-like defensive behaviors. We propose that these Tac1 neurons in the NTS are a key component of the airway-vagal-brain neural circuit that controls cough-like defensive behaviors in mice and that they coordinate the downstream modular circuits to elicit the sequential motor pattern of forceful expiratory responses.

Unveiling the impact of low-frequency electrical stimulation on network synchronization and learning behavior in cultured hippocampal neural networks

Understanding the dynamics of neural networks and their response to external stimuli is crucial for unraveling the mechanisms associated with learning processes. In this study, we hypothesized that electrical stimulation (ES) would lead to significant alterations in the activity patterns of hippocampal neuronal networks and investigated the effects of low-frequency ES on hippocampal neuronal populations using the microelectrode arrays (MEAs). Our findings revealed significant alterations in the activity of hippocampal neuronal networks following low-frequency ES trainings. Post-stimulation, the neural activity exhibited an organized burst firing pattern characterized by increased spike and burst firings, increased synchronization, and enhanced learning behaviors. Analysis of peri-stimulus time histograms (PSTHs) further revealed that low-frequency ES (1Hz) significantly enhanced neural plasticity, thereby facilitating the learning process of cultured neurons, whereas high-frequency ES (>10Hz) impeded this process. Moreover, we observed a substantial increase in correlations and connectivity within neuronal networks following ES trainings. These alterations in network properties indicated enhanced synaptic plasticity and emphasized the positive impact of low-frequency ES on hippocampal neural activities, contributing to the brain's capacity for learning and memory.