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Abstract Aims: Oxidative stress and neuronal death are the primary reasons for the progression of amyloid-beta (Aβ) deposition and cognitive deficits in Alzheimer’s disease (AD). Ecdysterone (Ecdy), a common derivative of ecdysteroids, possesses free radical scavenging and cognitive-improving effects. High-intensity interval training (HIIT) may be a therapeutic strategy for improving cognitive decline and oxidative stress. The present study was aimed to evaluate the effect of HIIT alone and its combination with Ecdysterone on the changes in learning and memory functions, hippocampal antioxidant enzymes activities, and neuronal population after AD induced by Aβ in male rats.Materials and methods: Following ten days of Aβ-injection, HIIT exercise and Ecdysterone treatment (10 mg/kg/day; P.O.) were initiated and continued for eight consecutive weeks in rats. At the end of the treatment period, rat’s learning and memory functions were assessed using water-maze and passive-avoidance tests. Moreover, the activity of superoxide dismutase (SOD), catalase (CAT), Glutathione Peroxidase (GPx), Glutathione Reductase (GRx) and neuronal population were evaluated in rat’s brains.Results: The results indicated that Aβ injection disrupted spatial/passive avoidance learning and memory in both water-maze and passive-avoidance paradigms, accompanied by a decrease in the superoxide dismutase and catalase (as endogenous antioxidants) in rat hippocampus. Additionally, Aβ injection resulted in neuronal loss in the cerebral cortex and hippocampus. Although consumption of Ecdysterone separately improved spatial/passive avoidance learning and memory impairments, recovered hippocampal activity of SOD, CAT, GRx, GRx and prevented the hippocampal neuronal loss, its combination with HIIT resulted in a more powerful and effective amelioration in all the above-mentioned Aβ-neuropathological changes.Conclusion: The current work's data confirms that a combination of HIIT exercise and Ecdysterone treatment could be a promising potential therapeutic agent against AD-associated cognitive decline, owing to their free radical scavenging and neuroprotective properties.

Recording from a large neuronal population of neurons is a crucial challenge to unravel how information is processed by the brain. In this review, we highlight the recent advances made in the field of “spike sorting”, which is arguably a very essential processing step to extract neuronal activity from extracellular recordings. We more specifically target the challenges faced by newly manufactured high-density multi-electrode array devices (HD-MEA), e.g. Neuropixels probes. Among them, we cover in depth the prominent problem of drifts (movements of the neurons with respect to the recording devices) and the current solutions to circumscribe it. In addition, we also review recent contributions making use of deep learning approaches for spike sorting, highlighting their advantages and disadvantages. Next, we highlight efforts and advances in unifying, validating, and benchmarking spike sorting tools. Finally, we discuss the spike sorting field in terms of its open and unsolved challenges, specifically regarding scalability and reproducibility. We conclude by providing our personal view on the future of spike sorting, calling for a community-based development and validation of spike sorting algorithms and fully automated, cloud-based spike sorting solutions for the neuroscience community.

Primary sensory cortex has long been believed to play a straightforward role in the initial processing of sensory information. Yet, the superficial layers of cortex overall are sparsely active, even during sensory stimulation; moreover, cortical activity is influenced by other modalities, task context, reward, and behavioral state. Our study demonstrates that reinforcement learning dramatically alters representations among longitudinally imaged neurons in superficial layers of mouse primary somatosensory cortex. Learning an object detection task recruits previously unresponsive neurons, enlarging the neuronal population sensitive to touch and behavioral choice. In contrast, cortical responses decrease upon repeated exposure to unrewarded stimuli. Moreover, training improved population encoding of the passage of time, and unexpected deviations in trial timing elicited even stronger responses than touch did. In conclusion, the superficial layers of sensory cortex exhibit a high degree of learning-dependent plasticity and are strongly modulated by non-sensory but behaviorally-relevant features, such as timing and surprise.

Recently, the sleep-wake states have been analysed using novel complexity measures, complementing the classical analysis of EEGs by frequency bands. This new approach consistently shows a decrease in EEG's complexity during slow-wave sleep, yet it is unclear how cortical oscillations shape these complexity variations. In this work, we analyse how the frequency content of brain signals affects the complexity estimates in freely moving rats. We find that the low-frequency spectrum - including the Delta, Theta, and Sigma frequency bands - drives the complexity changes during the sleep-wake states. This happens because low-frequency oscillations emerge from neuronal population patterns, as we show by recovering the complexity variations during the sleep-wake cycle from micro, meso, and macroscopic recordings. Moreover, we find that the lower frequencies reveal synchronisation patterns across the neocortex, such as a sensory-motor decoupling that happens during REM sleep. Overall, our works shows that EEG's low frequencies are critical in shaping the sleep-wake states' complexity across cortical scales.

Genetically encoded voltage indicators (GEVIs) hold great promise for monitoring neuronal population activity, but GEVI imaging in dense neuronal populations remains difficult due to a lack of contrast and/or speed. To address this challenge, we developed a novel confocal microscope that allows simultaneous multiplane imaging with high-contrast at near-kHz rates. This approach enables high signal-to-noise ratio voltage imaging in densely labeled populations and minimizes optical crosstalk during concurrent optogenetic photostimulation.

AbstractLearning engages a high-dimensional neuronal population space spanning multiple brain regions. We identified a low-dimensional signature associated with operant conditioning, a ubiquitous form of learning in which animals learn from the consequences of behavior. Using single-neuron resolution voltage imaging, we identified two low-dimensional motor modules in the neuronal population underlying Aplysia feeding. Our findings point to a temporal shift in module recruitment as the primary signature of operant learning.

Abstract Objective: In this work we propose a method for producing engineered human derived three-dimensional neuronal assemblies coupled to Micro-Electrode Array (MEA) substrates for studying the electrophysiological activity of such networks. Approach: We used biocompatible chitosan microbeads as scaffold to build 3D networks and to ensure nutrients-medium exchange from the core of the structure to the external environment. We used excitatory neurons derived from human-induced Pluripotent Stem Cells (hiPSCs) co-cultured with astrocytes. By adapting the well-established NgN2 differentiation protocol, we obtained 3D engineered networks with good control over cell density, volume and cell composition. We coupled the 3D neuronal networks to 60-channel Micro Electrode Arrays (MEAs) to evaluate and monitor the functional activity of the neuronal population. In parallel, we generated two-dimensional neuronal networks to compare the results of the two models. Main results: 3D cultures were healthy and functional up to 42 Days In Vitro (DIVs). From the structural point of view, the hiPSC derived neurons were able to adhere to chitosan microbeads and to form a stable 3D assembly thanks to the connections among cells. From a functional point of view, neuronal networks showed spontaneous activity after a couple of weeks. We monitored the functional electrophysiological behavior up to 6 weeks and we compared the network dynamic with 2D models. Significance: We presented for the first time a method to generate 3D engineered cultures with human-derived neurons coupled to MEAs, overcoming some of the limitations related to 2D and 3D neuronal networks and thus increasing the therapeutic target potential of these models for biomedical applications.

Diverse physiological phenotypes in a neuronal population can broaden the range of computational capabilities within a brain region. The avian cochlear nucleus angularis (NA) contains a heterogeneous population of neurons whose variation in intrinsic properties results in electrophysiological phenotypes with a range of sensitivities to temporally modulated input. The low-threshold potassium conductance (GKLT) is a key feature of neurons involved in fine temporal structure coding for sound localization but a role for these channels in intensity or spectrotemporal coding has not been established. To determine whether GKLT affects the phenotypical variation and temporal properties of NA neurons, we applied dendrotoxin (DTX), a potent antagonist of Kv1-type potassium channels, to chick brain stem slices in vitro during whole-cell patch clamp recordings. We found a cell-type specific subset of NA neurons were sensitive to DTX: single-spiking NA neurons were most profoundly affected, as well as a subset of tonic firing neurons. Both tonic I (phasic onset bursting) and tonic II (delayed firing) neurons showed DTX sensitivity in their firing rate and phenotypical firing pattern. Tonic III neurons were unaffected. Spike time reliability and fluctuation sensitivity measured in DTX-sensitive NA neurons was also reduced with DTX. Finally, DTX reduced spike threshold adaptation in these neurons, suggesting that GKLT contributes to the temporal properties that allow coding of rapid changes in the inputs to NA neurons. These results suggest that variation in Kv1 channel expression may be a key factor in functional diversity in the avian cochlear nucleus.

Economic value encapsulates the subjective combination of reward magnitude and probability. We investigated the mechanism for subjective value computation in single neurons using an economic axiomatic approach. We found that single neurons in the macaque orbitofrontal cortex, known to be sensitive to reward magnitude and probability, encode the economic value functions (utility and probability weighting) in a heterogeneous manner, such that the activity of individual neurons did not match the animal's choices. However, the utility and probability weighting code from a population of these varied neurons reliably matched the animals' choices and risk attitudes. Thus, the neuronal population code for economic value amounted to a distributional representation of the formal economic functions. With a diverse single-unit economic value code converging into a reliable population-level utility code, this scheme suggests a brain mechanism for the flexible accommodation of multiple choice patterns and risk attitudes.