Neuronal Populations Research Articles

The geometry of correlated variability leads to highly suboptimal discriminative sensory coding.

The brain represents the world through the activity of neural populations; however, whether the computational goal of sensory coding is to support discrimination of sensory stimuli or to generate an internal model of the sensory world is unclear. Correlated variability across a neural population (noise correlations) is commonly observed experimentally, and many studies demonstrate that correlated variability improves discriminative sensory coding compared to a null model with no correlations. However, such results do not address whether correlated variability is for discriminative sensory coding. If the computational goal of sensory coding is discriminative, then correlated variability should be optimized to support that goal. We assessed optimality of noise correlations for discriminative sensory coding in diverse datasets by developing two novel null models, each with a biological interpretation. Across datasets, we found correlated variability in neural populations leads to highly suboptimal discriminative sensory coding according to both null models. Furthermore, biological constraints prevent many subsets of the neural populations from achieving optimality, and subselecting based on biological criteria leaves discriminative coding performance suboptimal. Finally, we show that optimal subpopulation are exponentially small as the population size grows. Together, these results demonstrate that the geometry of correlated variability leads to highly suboptimal discriminative sensory coding.

Natural forgetting reversibly modulates engram expression

Memories are stored as ensembles of engram neurons and their successful recall involves the reactivation of these cellular networks. However, significant gaps remain in connecting these cell ensembles with the process of forgetting. Here, we utilized a mouse model of object memory and investigated the conditions in which a memory could be preserved, retrieved, or forgotten. Direct modulation of engram activity via optogenetic stimulation or inhibition either facilitated or prevented the recall of an object memory. In addition, through behavioral and pharmacological interventions, we successfully prevented or accelerated forgetting of an object memory. Finally, we showed that these results can be explained by a computational model in which engrams that are subjectively less relevant for adaptive behavior are more likely to be forgotten. Together, these findings suggest that forgetting may be an adaptive form of engram plasticity which allows engrams to switch from an accessible state to an inaccessible state.

Natural forgetting reversibly modulates engram expression.

Memories are stored as ensembles of engram neurons and their successful recall involves the reactivation of these cellular networks. However, significant gaps remain in connecting these cell ensembles with the process of forgetting. Here, we utilized a mouse model of object memory and investigated the conditions in which a memory could be preserved, retrieved, or forgotten. Direct modulation of engram activity via optogenetic stimulation or inhibition either facilitated or prevented the recall of an object memory. In addition, through behavioral and pharmacological interventions, we successfully prevented or accelerated forgetting of an object memory. Finally, we showed that these results can be explained by a computational model in which engrams that are subjectively less relevant for adaptive behavior are more likely to be forgotten. Together, these findings suggest that forgetting may be an adaptive form of engram plasticity which allows engrams to switch from an accessible state to an inaccessible state.

Parallel maturation of rodent hippocampal memory and CA1 task representations.

Hippocampal-dependent memory is known to emerge late in ontogeny, and its full development is protracted. Yet the changes in hippocampal neuronal function that underlie this delayed and gradual maturation remain relatively unexplored. To address this gap, we recorded ensembles of CA1 neurons while charting the development of hippocampal-dependent spatial working memory (WM) in rat pups (∼2-4weeks of age). We found a sharp transition in WM development, with age of inflection varying considerably between individual animals. In parallel with the sudden emergence of WM, hippocampal spatial representations became abruptly task specific, remapping between encoding and retrieval phases of the task. Further, we show how the development of task-phase remapping could partly be explained by changes in place-field size during this developmental period as well as the onset of precise temporal coordination of CA1 excitatory input. Together, these results suggest that a hallmark of hippocampal memory development may be the emergence of contextually specific CA1 representations driven by the maturation of CA1 micro-circuits.

VP3.15 reduces acute cerebellum damage after germinal matrix-intraventricular hemorrhage of the preterm newborn

Germinal matrix-intraventricular hemorrhage (GM-IVH) is one of the most common complications of the preterm newborn. The pathology of the GM-IVH is not completely understood and even regions distant from the lesion area are severely affected. It has been suggested that cerebellar diaschisis may underlie the neurodevelopmental problems that many of these kids show, including cerebral palsy, attention deficit disorders or hyperactivity. Additionally, GM-IVH has no successful treatment. VP3.15 is a dual action phosphodiesterase 7 (PDE7) and glycogen synthase kinase-3β (GSK-3β) inhibitor that limits neuroinflammation and neuronal loss. Therefore, it might also provide a relevant tool to reduce complications associated with GM-IVH. We have used a murine model of GM-IVH to analyze the short and long-term effects of VP3.15 in brain pathology and behavioral complications. In our hands, the induction of unilateral GM-IVH to P7 CD1 mice results in a short-term (P14) compromise of the cerebellar neuronal population and Purkinje cells arborization, an increase of microglia burden in the nuclei and an overall increase of punctuate cerebellar hemorrhages. Whereas brain alterations are no longer observed in the long term (P110), these animals present overt hyperactivity when analyzed in the adulthood, supporting the long-term behavioral impairment. Also, hyperactivity significantly correlates with ipsi and contralateral cerebellar sizes, neuronal densities and myelin basic protein levels. Importantly, treatment with VP3.15 significantly reduces neuronal loss, Purkinje cells simplification, the presence of cerebellar hemorrhages, as well as hyperactivity. Altogether, our data support the neuroprotective effects of VP3.15 in GM-IVH of the PT.

Neurochemical atlas of the rabbit spinal cord.

Complex neurophysiological and morphologic experiments require suitable animal models for investigation. The rabbit is one of the most successful models for studying spinal cord functions owing to its substantial size. However, achieving precise surgical access to specific spinal regions requires a thorough understanding of the spinal cord's cytoarchitectonic structure and its spatial relationship with the vertebrae. The comprehensive anatomo-neurochemical atlases of the spinal cord are invaluable for attaining such insight. While such atlases exist for some rodents and primates, none exist for rabbits. We have developed a spinal cord atlas for rabbits to bridge this gap. Utilizing various neurochemical markers-including antibodies to NeuN, calbindin 28kDa, parvalbumin, choline acetyltransferase, nitric oxide synthase, and non-phosphorylated heavy-chain neurofilaments (SMI-32 antibody)-we present the visualization of diverse spinal neuronal populations, various spinal cord metrics, stereotaxic maps of transverse slices for each spinal segment, and a spatial map detailing the intricate relationship between the spinal cord and the vertebrae across its entire length.

Artificial Intelligence in Life Expectancy Prediction: A Paradigm Shift for Annuity Pricing and Risk Management

This article explores the transformative potential of artificial intelligence (AI) in predicting life expectancy and its far-reaching implications for the annuities market. Traditional actuarial models, often reliant on demographic data and historical trends, face limitations in accuracy and personalization. We present a novel approach leveraging machine learning algorithms, including neural networks, decision trees, and ensemble methods, alongside natural language processing and deep learning techniques. Our AI-driven models integrate diverse data sources, including medical histories, genetic information, lifestyle factors, and socio-economic indicators, to provide more accurate and individualized life expectancy predictions. Using a dataset of anonymized health records and historical mortality data, we demonstrate that our AI models significantly outperform traditional actuarial tables in predicting individual mortality risks. The enhanced predictive power of these models has substantial implications for the annuities market, enabling insurers to price products more precisely, manage longevity risk more effectively, and optimize reserve capital. Moreover, consumers benefit from fairer pricing and personalized product offerings. This article underscores the need for regulatory framework revisions to accommodate AI-driven methodologies in actuarial practices. We also discuss ethical considerations, data privacy concerns, and the challenge of model interpretability, highlighting areas for future research to ensure responsible deployment of AI in life expectancy predictions and annuity pricing

Spatially resolved transcriptomic signatures of hippocampal subregions and Arc-expressing ensembles in active place avoidance memory

The rodent hippocampus is a spatially organized neuronal network that supports the formation of spatial and episodic memories. We conducted bulk RNA sequencing and spatial transcriptomics experiments to measure gene expression changes in the dorsal hippocampus following the recall of active place avoidance (APA) memory. Through bulk RNA sequencing, we examined the gene expression changes following memory recall across the functionally distinct subregions of the dorsal hippocampus. We found that recall induced differentially expressed genes (DEGs) in the CA1 and CA3 hippocampal subregions were enriched with genes involved in synaptic transmission and synaptic plasticity, while DEGs in the dentate gyrus (DG) were enriched with genes involved in energy balance and ribosomal function. Through spatial transcriptomics, we examined gene expression changes following memory recall across an array of spots encompassing putative memory-associated neuronal ensembles marked by the expression of the IEGs Arc, Egr1, and c-Jun. Within samples from both trained and untrained mice, the subpopulations of spatial transcriptomic spots marked by these IEGs were transcriptomically and spatially distinct from one another. DEGs detected between Arc + and Arc− spots exclusively in the trained mouse were enriched in several memory-related gene ontology terms, including “regulation of synaptic plasticity” and “memory.” Our results suggest that APA memory recall is supported by regionalized transcriptomic profiles separating the CA1 and CA3 from the DG, transcriptionally and spatially distinct IEG expressing spatial transcriptomic spots, and biological processes related to synaptic plasticity as a defining the difference between Arc + and Arc− spatial transcriptomic spots.

Local translatome sustains synaptic function in impaired Wallerian degeneration.

After injury, severed axons separated from their somas activate programmed axon degeneration, a conserved pathway to initiate their degeneration within a day. Conversely, severed projections deficient in programmed axon degeneration remain morphologically preserved with functional synapses for weeks to months after axotomy. How this synaptic function is sustained remains currently unknown. Here, we show that dNmnat overexpression attenuates programmed axon degeneration in distinct neuronal populations. Severed projections remain morphologically preserved for weeks. When evoked, they elicit a postsynaptic behavior, a readout for preserved synaptic function. We used ribosomal pulldown to isolate the translatome from these projections 1 week after axotomy. Translatome candidates of enriched biological classes identified by transcriptional profiling are validated in a screen using a novel automated system to detect evoked antennal grooming as a proxy for preserved synaptic function. RNAi-mediated knockdown reveals that transcripts of themTORC1 pathway, a mediator of protein synthesis, and of candidate genes involved in protein ubiquitination and Ca2+ homeostasis are required for preserved synaptic function. Our translatome dataset also uncovers several uncharacterized Drosophila genes associated with human disease. It may offer insights into novel avenues for therapeutic treatments.

Microplastic and the Enteric Nervous System: Effect of PET Microparticles on Selected Neurotransmitters and Cytokines in the Porcine Ileum

Microplastic is an environmental hazard to which both animals and humans are exposed. Current reports show that it can cause inflammation, including in the gastrointestinal tract. To examine the impact on the ileum, 15 eight-week-old gilts (five individuals/group) were exposed to PET microplastics (7.6 µm–416.9 µm) at a dose of 0.1 g/day or 1 g/day for 28 days. The collected ileum fragments were investigated for the cytokine concentrations (IL-1β, IL-6, IL-8, IL-10, and TNF-α; ELISA test), neuron populations (cocaine and amphetamine-regulated transcript, galanin, neuronal nitric oxide synthase, substance P, vesicular acetylcholine transporter, and vasoactive intestinal peptide; immunofluorescence staining), and morphometric parameters (histological analysis). Under the influence of MP-PET, there was a reduction in the populations of CART- and GAL-positive neurons in the submucosal plexuses and of nNOS-, VAChT-, and VIP-positive neurons in all the plexuses. In contrast, there was an increase in GAL-positive neurons in the myenteric plexus and SP-positive neurons in all the plexuses. The concentrations of IL-1β, IL-6, IL-8, IL-10, and TNF-α did not undergo statistically significant changes under the influence of the low or high dose of MP-PET. The changes in the histological structure exclusively concerned the thinning of the mucosa and the muscularis externa. The results support the thesis that MP-PET is not neutral to the ileal cells.

Leptin-activated hypothalamic BNC2 neurons acutely suppress food intake.

Leptin is an adipose tissue hormone that maintains homeostatic control of adipose tissue mass by regulating the activity of specific neural populations controlling appetite and metabolism1. Leptin regulates food intake by inhibiting orexigenic agouti-related protein (AGRP) neurons and activating anorexigenic pro-opiomelanocortin (POMC) neurons2. However, whereas AGRP neurons regulate food intake on a rapid time scale, acute activation of POMC neurons has only a minimal effect3-5. This has raised the possibility that there is a heretofore unidentified leptin-regulated neural population that rapidly suppresses appetite. Here we report the discovery of a new population of leptin-target neurons expressing basonuclin 2 (Bnc2) in the arcuate nucleus that acutely suppress appetite by directly inhibiting AGRP neurons. Opposite to the effect of AGRP activation, BNC2 neuronal activation elicited a place preference indicative of positive valence in hungry but not fed mice. The activity of BNC2 neurons is modulated by leptin, sensory food cues and nutritional status. Finally, deleting leptin receptors in BNC2 neurons caused marked hyperphagia and obesity, similar to that observed in a leptin receptor knockout in AGRP neurons. These data indicate that BNC2-expressing neurons are a key component of the neural circuit that maintains energy balance, thus filling an important gap in our understanding of the regulation of food intake and leptin action.

Integrity of the circadian clock determines regularity of high-frequency and diurnal LFP rhythms within and between brain areas.

Circadian clocks control most physiological processes of many species. We specifically wanted to investigate the influence of environmental and endogenous rhythms and their interplay on electrophysiological dynamics of neuronal populations. Therefore, we measured local field potential (LFP) time series in wild-type and Cryptochrome 1 and 2 deficient (Cry1/2-/-) mice in the suprachiasmatic nucleus and the nucleus accumbens under regular light conditions and constant darkness. Using refined descriptive and statistical analyses, we systematically profiled LFP time series activity. We show that both environmental and endogenous rhythms strongly influence the rhythmicity of LFP signals and their frequency components, but also shape neuronal patterns on much smaller time scales, as neuronal activity in Cry1/2-/- mice is significantly less regular but at each time more synchronous within and between brain areas than in wild-type mice. These results show that functional circadian rhythms are integral for both circadian and non-circadian coordination of neuronal ensemble dynamics.

Application of Multi-Input Multi-Output Brain-Computer Interface in Cognitive Assessment for Alzheimer’s Disease

This research aims to augment human cognitive abilities, particularly memory function, through the development of innovative prosthetics. The hippocampus, a crucial brain region involved in memory encoding and recall, has been the focal point of numerous studies seeking to develop prosthetics that can restore or enhance memory function in individuals with impairments such as Alzheimer’s disease. This review examines two studies that delve into the development and application of hippocampal neural prosthetics, highlighting the potential of these prosthetics to revolutionize the treatment of memory impairments. One study utilized static neural stimulation patterns to enhance memory for specific information content, while another developed a nonlinear Multiple Input Multiple Output (MIMO) dynamic model to predict and stimulate neural activity patterns during memory encoding. Both studies demonstrated significant improvements in memory performance. Additionally, this paper discusses the clinical significance and application prospects of hippocampal neural prosthetics, including the potential for implantable prosthetics and the widespread applicability of MIMO stimulation. Furthermore, the paper explores advanced modeling and classification techniques for neural ensembles and visual memory decoding, including Volterra-type hierarchical modeling, optimal multi-unit stimulation patterns, and a sparse classification model for decoding visual memories. Finally, the paper presents the implementation and evaluation of neural prosthesis systems, including an application-specific integrated circuit (ASIC) design for a hippocampal prosthesis and a high-performance, scalable system architecture for real-time estimation of neural activity. These advancements hold promise for future research and applications in neural prosthetics for memory enhancement.

Application of Optogenetics in the Regulation of Neural Circuits in Parkinson’s Disease

This paper reviews the application of optogenetics in the study of Parkinson’s disease and discuss how optogenetics stimulates specific neural circuits to regulate the symptoms and behaviors of Parkinson’s disease. Optogenetics technology has a higher spatial resolution than traditional electrical stimulation, and optogenetics can accurately control specific neuron populations, providing a new perspective for studying PD and exploring potential treatments. The researchers found that selective optogenetic stimulation of the dorsal striatum was sufficient to induce levodopalike dyskinesia in 6-hydroxydopamine (6-OHDA)-induced PD rat models. In addition, the researchers found that transplanted dopamine neurons can correct some of the motor deficits through their neural activity, including dopamine release and synaptic transmission. Although the utility of optogenetic techniques in human patients is unclear, the precise control of neural circuits in animal models provides an important way to understand the mechanisms and side effects of DBS therapy. The study also suggests that optogenetic stimulation of glutamatergic neurons in the mesocerebellar nucleus may improve motor function in PD mouse models, providing a new potential target for PD treatment. Finally, the article discusses how optogenetic technology can be transformed from a research tool into a treatment for PD, and how to repair damaged neural circuits through optogenetic technology to provide a more durable and effective treatment.

Emerging many-to-one weighted mapping in hippocampus-amygdala network underlies memory formation

Memories are crucial for daily life, yet the network-level organizing principles governing neural representations of experiences remain unknown. Employing dual-site in vivo recording in freely behaving male mice, here we show that hippocampal dorsal CA1 (dCA1) and basolateral amygdala (BLA) utilize distinct coding strategies for novel experiences. A small assembly of BLA neurons emerged active during memory acquisition and persisted through consolidation, whereas most dCA1 neurons were engaged in both processes. Machine learning decoding revealed that dCA1 population spikes predicted BLA assembly firing rate, suggesting that most dCA1 neurons concurrently index an episodic event by rapidly establishing weighted communication with a specific BLA assembly – a process we term “many-to-one weighted mapping.” We also found that dCA1 reactivations preceded BLA assembly activity preferably during elongated and enlarged dCA1 ripples. Using a closed-loop strategy, we demonstrated that suppressing BLA activity after large dCA1 ripples impaired memory. These findings highlight a many-to-one weighted mapping mechanism underlying both the acquisition and consolidation of new memories.

Continuous integration of heading and goal directions guides steering.

Navigating animals must integrate a diverse array of sensory cues into a single locomotor decision. Insects perform intricate navigational feats using a brain region termed the central complex in which an animal's heading direction is transformed through several layers of circuitry to elicit goal-directed locomotion. These transformations occur mostly in the fan-shaped body (FB), a major locus of multi-sensory integration in the central complex. Key aspects of these sensorimotor computations have been extensively characterized by functional studies, leveraging the genetic tools available in the fruit fly. However, our understanding of how neuronal activity in the FB dictates locomotor behaviors during navigation remains enigmatic. Here, we manipulate the activity of two key neuronal populations that input into the FB-the PFN a and PFN d neurons-used to encode the direction of two complex navigational cues: wind plumes and optic flow, respectively. We find that flies presented with unidirectional optic flow steer along curved walking trajectories, but silencing PFN d neurons abolishes this curvature. We next use optogenetic activation to introduce a fictive heading signal in the PFNs to establish the causal relationship between their activity and steering behavior. Our studies reveal that the central complex guides locomotion by summing the PFN-borne directional signals and shifting movement trajectories left or right accordingly. Based on these results, we propose a model of central complex-mediated locomotion wherein the fly achieves fine-grained control of sensory-guided steering by continuously integrating its heading and goal directions over time.

Isolated theta waves originating from the midline thalamus trigger memory reactivation during NREM sleep in mice

During non-rapid eye movement (NREM) sleep, neural ensembles in the entorhinal-hippocampal circuit responsible for encoding recent memories undergo reactivation to facilitate the process of memory consolidation. This reactivation is widely acknowledged as pivotal for the formation of stable memory and its impairment is closely associated with memory dysfunction. To date, the neural mechanisms driving the reactivation of neural ensembles during NREM sleep remain poorly understood. Here, we show that the neural ensembles in the medial entorhinal cortex (MEC) that encode spatial experiences exhibit reactivation during NREM sleep. Notably, this reactivation consistently coincides with isolated theta waves. In addition, we found that the nucleus reuniens (RE) in the midline thalamus exhibits typical theta waves during NREM sleep, which are highly synchronized with those occurring in the MEC in male mice. Closed-loop optogenetic inhibition of the RE-MEC pathway specifically suppressed these isolated theta waves, resulting in impaired reactivation and compromised memory consolidation following a spatial memory task in male mice. The findings suggest that theta waves originating from the ventral midline thalamus play a role in initiating memory reactivation and consolidation during sleep.

General anesthesia activates a central anxiolytic center in the BNST

Low doses of general anesthetics like ketamine and dexmedetomidine have anxiolytic properties independent of their sedative effects, but the underlying mechanisms remain unclear. We discovered a population of GABAergic neurons in the oval division of the bed nucleus of the stria terminalis that are activated by multiple anesthetics and the anxiolytic drug diazepam (ovBNSTGA). The majority of ovBNSTGA neurons express neurotensin receptor 1 (Ntsr1) and form circuits with brain regions known to regulate anxiety and stress responses. Optogenetic activation of ovBNSTGA or ovBNSTNtsr1 neurons significantly attenuated anxiety-like behaviors in both naive animals and mice with inflammatory pain, while inhibition of these cells elevated anxiety. Activation of these neurons decreased heart rate and increased heart rate variability, suggesting that they reduce anxiety by modulating autonomic responses. Our study identifies ovBNSTGA/ovBNSTNtsr1 neurons as a common neural substrate mediating the anxiolytic effect of low-dose anesthetics and a potential therapeutic target for treating anxiety-related disorders.

Dispensable regulation of brain development and myelination by the immune-related protein Serpina3n.

Serine protease inhibitor clade A member 3n (Serpina3n) or its human orthologue SERPINA3 is a secretory immune-related molecule produced primarily in the liver and brain under homeostatic conditions and up-regulated in response to system inflammation. Yet, it remains elusive regarding its cellular identity and physiological significance in the development of the postnatal brain. Here, we reported that oligodendroglial lineage cells are the major cell population expressing Serpina3n protein in the postnatal murine CNS. Using loss-of-function genetic tools, we found that Serpina3n conditional knockout (cKO) from Olig2-expressing cells does not significantly affect cognitive and motor functions in mice. Serpina3n depletion does not appear to interfere with oligodendrocyte differentiation and developmental myelination nor affects the population of other glial cells and neurons invivo. Interestingly, Serpina3n is significantly up-regulated in response to oxidative stress and its deficiency alleviates oxidative injury and diminishes cell senescence of oligodendrocytes invitro. Together, our data suggest that the immune-related molecule Serpina3n plays a minor role in neural cell development under homeostasis, yet it primes oligodendrocytes for CNS insults and regulates oligodendrocyte health under injured conditions. Our findings raise the interest in pursuing its functional significance in the CNS under disease/injury conditions.

Deep learning-based spike sorting: a survey.

Objective.Deep learning is increasingly permeating neuroscience, leading to a rise in signal-processing applications for extracellular recordings. These signals capture the activity of small neuronal populations, necessitating 'spike sorting' to assign action potentials (spikes) to their underlying neurons. With the rise in publications delving into new methodologies and techniques for deep learning-based spike sorting, it is crucial to synthesise these findings critically. This survey provides an in-depth evaluation of the approaches, methodologies and outcomes presented in recent articles, shedding light on the current state-of-the-art.Approach.Twenty-four articles published until December 2023 on deep learning-based spike sorting have been examined. The proposed methods are divided into three sub-problems of spike sorting: spike detection, feature extraction and classification. Moreover, integrated systems, i.e., models that detect spikes and extract features or do classification within a single network, are included.Main Results.Although most algorithms have been developed for single-channel recordings, models utilising multi-channel data have already shown promising results, with efficient hardware implementations running quantised models on ASICs and FPGAs. Convolutional neural networks have been used extensively for spike detection and classification as the data can be processed spatiotemporally while maintaining low-parameter models and increasing generalisation and efficiency. Autoencoders have been mainly utilised for dimensionality reduction, enabling subsequent clustering with standard methods. Also, integrated systems have shown great potential in solving the spike sorting problem from end to end.Significance.This survey explores recent articles on deep learning-based spike sorting and highlights the capabilities of deep neural networks in overcoming associated challenges, but also highlights potential biases of certain models. Serving as a resource for both newcomers and seasoned researchers in the field, this work provides insights into the latest advancements and may inspire future model development.