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261 - 270 of 52742 results
  • Journal Article
    Population Coupling of V1 and V4 Neurons and Its Relation to Local Cortical State Fluctuations and Attention in Macaque Monkey | eNeuro
    Neurons couple to various degrees to the activity level of the local neighboring population whereby strongly coupled “choristers” and weakly coupled “soloists” have been identified as two extremes of a continuous spectrum. At the same time neuronal populations undergo coordinated ON and OFF cortical state activity fluctuations, which are locally modulated by attention. The population coupling of soloists and choristers suggests that soloists should show limited alignment with cortical state fluctuations, while choristers should exhibit profound alignment. To test this, we recorded neurons across cortical layers in macaque areas V1 and V4 ( n  = 2 males), while animals performed a feature-based spatial attention task. As expected, we found a wide range of population coupling strength of neurons. In line with our prediction, coupling of choristers to cortical state changes (ON-OFF transitions) was generally stronger than that of soloists. The strength of population coupling of neurons was similar during spon...
    May 1, 2026 Mahyar Doost
  • Journal Article
    Erbin Confers Neuroprotection against Cerebral Ischemia–Reperfusion Injury in Mice via MAPK Pathway Inhibition | eNeuro
    Ischemic stroke, a leading cause of neurological morbidity, is characterized by extensive neuronal injury and a robust inflammatory response. Erbin, a scaffold protein involved in multiple cellular signaling pathways, regulates neuroinflammation and may confer neuroprotection against ischemia–reperfusion (I/R) injury. A mouse model of middle cerebral artery occlusion was utilized to evaluate the neuroprotective role of Erbin. Male mice were allocated into groups receiving either a lentiviral (LV) control vector or LV-mediated Erbin overexpression, followed by I/R injury induction. Neurological function, infarct volume, and expression levels of inflammatory cytokines and mitogen-activated protein kinase (MAPK) signaling proteins were analyzed. Overexpression of Erbin via LV transduction significantly reduced cerebral infarct volume and mitigated neurological impairments post-I/R injury. Furthermore, Erbin overexpression suppressed the phosphorylation of p38 and extracellular signal-regulated kinase in HT22 ...
    May 1, 2026 Danyang Meng
  • Journal Article
    Cortex-Wide Neuron Activation after Traumatic Brain Injury in Mice | eNeuro
    Following a traumatic brain injury (TBI), the neocortex undergoes time-dependent cellular responses including immediate tissue deformation, enhanced excitability, and elevated expression of immediate early genes. However, the spatial extent of early neuronal activity after a focal injury remains unclear. Here we use targeted recombination in active populations reporter mice of both sexes to identify neurons activated in the acute phase following a controlled cortical impact injury applied to the somatosensory neocortex. We find widespread cell activation across large portions of the cortex that extends beyond the astrocytic and microglial responses marking the injury site. Activated cells are predominantly neurons, and few cells colabel with GFAP or IBA1. Our findings reveal that even focal injury engages cortical circuits across large portions of the injured brain, highlighting the importance of considering cortex-wide neuronal dynamics in the early postinjury period and their potential impact on network ...
    May 1, 2026 Alexa Tierno
  • Journal Article
    Spike Generation in Electroreceptor Afferents Introduces Additional Spectral Response Components by Weakly Nonlinear Interactions | eNeuro
    Spiking thresholds in neurons or rectification at synapses are essential for neuronal computations rendering neuronal processing inherently nonlinear. Nevertheless, linear response theory has been instrumental for understanding, for example, the impact of noise or neuronal synchrony on signal transmission, or the emergence of oscillatory activity, but is valid only at low stimulus amplitudes or large levels of intrinsic noise. At higher signal-to-noise ratios, however, nonlinear response components become relevant. Theoretical results for leaky integrate-and-fire neurons in the weakly nonlinear regime suggest strong responses at the sum of two input frequencies if one of these frequencies or their sum matches the neuron’s baseline firing rate. We here analyze nonlinear responses in two types of primary electroreceptor afferents, the P-units of the active and the ampullary cells of the passive electrosensory system of the wave-type electric fish Apteronotus leptorhynchus of either sex. In our combined exper...
    May 1, 2026 Alexandra Barayeu
  • Journal Article
    Erratum: “Is Social Media Use a Blessing or Cure for Motor Function and Skill Acquisition? An Opinion Paper” | eNeuro
    In the article “Is Social Media Use a Blessing or Cure for Motor Function and Skill Acquisition? An Opinion …
    May 1, 2026
  • Journal Article
    Mistaking Covariance for Combination in Sensorimotor Adaptation: Regression Slopes Do Not Test Additivity | eNeuro
    Sensorimotor adaptation depends on implicit recalibration and explicit strategy. These processes are commonly assumed to sum ( A  =  I  +  E ), and this additivity assumption justifies subtractive measurement and informs computational models of motor learning. Recent work has challenged additivity by examining regression slopes between implicit and explicit measures. When slopes deviate from β  = −1, the interpretation has been that the processes are “sub-additive” and fail to sum as expected. Here, we show this reasoning is mistaken. Regression slopes reflect covariance structure: how learning processes relate across individuals. Additivity is a claim about motor output combination: whether learning processes sum within individuals. These are different questions, and regression slopes do not address the latter. We derive the expected slope under subtractive logic and show it equals β  = −1 only when total adaptation is uncorrelated with the measured component. Monte Carlo simulations confirm this benchmar...
    May 1, 2026 Joshua Liddy
  • Journal Article
    Role of Concentration in Opposing Effects of Anandamide on Nociceptive Synapses versus Non-nociceptive Synapses | eNeuro
    There is considerable interest in cannabinoid-based therapies to treat pain, but activation of the endogenous cannabinoid (endocannabinoid) system can elicit pro- and anti-nociceptive effects. This study tests the hypothesis that the concentration of the endocannabinoid arachidonoylethanolamine (AEA) contributes to whether pro- or anti-nociceptive effects are observed. Experiments were carried out using isolated ganglia from the medicinal leech Hirudo verbana where it is possible to selectively record from nociceptive and non-nociceptive synapses in the central nervous system (CNS). Previous studies using Hirudo have shown that endocannabinoids depress nociceptive (N) sensory cell synapses and potentiate of non-nociceptive pressure (P) sensory cell synapses. In this study, exogenously applied AEA produced depression of N synapses and potentiation of P synapses across the same range of concentrations. However, the results differed when using URB597, a drug that raises AEA by inhibiting fatty acid amine hydr...
    May 1, 2026 Brian D. Burrell
  • Journal Article
    Cell Density Impacts Population Activity in Human iPSC-Derived Neural Networks | eNeuro
    Multi-electrode recording of neuronal activity in cultures offer opportunities for understanding how the structure of a network gives rise to function. Neuronal cultures derived from human induced pluripotent stem cells (iPSCs) from male and female individuals are often plated at highly variable cell densities across studies, but its impact on neuronal activity remains poorly understood. We found that properties such as the mean firing rate of the individual cells, the pairwise correlations between cells, and the entropy of the population all changed significantly with changes in culture density. We used a maximum entropy model to capture the structure of the population activity using only the firing rates and correlations, and we found that the model performed best at the highest densities, suggesting that changes in activity reflected differences in structure of interactions between neurons across scales of complexity. Our work thus shows that culture density is an important experimental parameter that i...
    May 1, 2026 Yavuz Selim Uzun
  • Journal Article
    Neural Mechanisms of Self-Generated Action Sequences | eNeuro
    Complex problems often allow multiple paths to a solution. Choosing and taking the best path is an important part of the executive cognition that underpins intelligent problem-solving behavior. However, once a path is chosen, the motor system must be activated for executing it. This interface between problem-solving and self-generated action has rarely been studied. We recorded EEG movement-related potentials while 25 participants (7 males, 18 females) performed the “Tower of London” problem-solving task. In a control condition, participants merely followed instructed steps without planning for any goal and thus without any sense that their movements solved a problem. Readiness potentials (RPs) preceding actions showed a more sustained preparatory negativity for self-generated than stimulus-driven movements. Critically, this effect was most pronounced at the first move of a sequence and diminished at later stages, indicating that preparatory activity is closely linked to the planning demands of sequence in...
    May 1, 2026 Silvia Seghezzi
  • Journal Article
    Optimizing and Benchmarking Machine Learning and Traditional Synaptic Event Detection Pipelines in Neurophysiology Experiments | eNeuro
    Synaptic physiology experiments are fundamental to neuroscience research. Consequently, accurate detection of synaptic currents is crucial for conducting high-quality experiments. Traditionally, detecting inhibitory and excitatory postsynaptic currents (sIPSCs/sEPSCs) relied on hand-counting individual events. Although sEPSCs and sIPSCs are clear to the trained eye, hand analysis is time and labor intensive. Recent advances in applied machine learning promise faster, superior event detectors that may improve data quality and reduce or even completely negate the need for hand curation. While many strategies for sIPSC and sEPSC detection exist, rarely have they been quantitatively compared for accuracy within an experiment. Our study aims to establish practical ground-truth event detection in a large experimental dataset through meticulous hand counting and to assess variance in detection results across different laboratories, analysis techniques, and cell types. Using thoroughly hand-counted data as our gro...
    May 1, 2026 Joshua P. Sevigny
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