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Modern well-performing approaches to neural decoding are based on machine learning models such as decision tree ensembles and deep neural networks. The wide range of algorithms that can be utilized to learn from neural spike trains, which are essentially time-series data, results in the need for diverse and challenging benchmarks for neural decoding, similar to the ones in the fields of computer vision and natural language processing. In this work, we propose a spike train classification benchmark, based on open-access neural activity datasets and consisting of several learning tasks such as stimulus type classification, animal's behavioral state prediction, and neuron type identification. We demonstrate that an approach based on hand-crafted time-series feature engineering establishes a strong baseline performing on par with state-of-the-art deep learning-based models for neural decoding. We release the code allowing to reproduce the reported results. Author summary: Machine learning-based neural decoding has been shown to outperform traditional approaches like Wiener and Kalman filters on certain key tasks. To further the advancement of neural decoding models, such as improvements in deep neural network architectures and better feature engineering for classical ML models, there need to exist common evaluation benchmarks similar to the ones in the fields of computer vision or natural language processing. In this work, we propose a benchmark consisting of several individual neuron spike train classification tasks based on open-access data from a range of animals and brain regions. We demonstrate that it is possible to achieve meaningful results in such a challenging benchmark using the massive time-series feature extraction approach, which is found to perform similarly to state-of-the-art deep learning approaches. [ABSTRACT FROM AUTHOR]

Experimental studies highlight the important role of the extracellular matrix (ECM) in the regulation of neuronal excitability and synaptic connectivity in the nervous system. In its turn, the neural ECM is formed in an activity-dependent manner. Its maturation closes the so-called critical period of neural development, stabilizing the efficient configurations of neural networks in the brain. ECM is locally remodeled by proteases secreted and activated in an activity-dependent manner into the extracellular space and this process is important for physiological synaptic plasticity. We ask if ECM remodeling may be exaggerated under pathological conditions and enable activity-dependent switches between different regimes of ECM expression. We consider an analytical model based on known mechanisms of interaction between neuronal activity and expression of ECM, ECM receptors and ECM degrading proteases. We demonstrate that either inhibitory or excitatory influence of ECM on neuronal activity may lead to the bistability of ECM expression, so two stable stationary states are observed. Noteworthy, only in the case when ECM has predominant inhibitory influence on neurons, the bistability is dependent on the activity of proteases. Excitatory ECM-neuron feedback influences may also result in spontaneous oscillations of ECM expression, which may coexist with a stable stationary state. Thus, ECM-neuronal interactions support switches between distinct dynamic regimes of ECM expression, possibly representing transitions into disease states associated with remodeling of brain ECM. [ABSTRACT FROM AUTHOR]

Information and action coding by cortical circuits relies on a balanced dialogue between excitation and inhibition. Circuit hyperexcitability is considered a potential pathophysiological mechanism in various brain disorders, but the underlying deficits, especially at early disease stages, remain largely unknown. We report that asymptomatic female mice carrying the chromosome 9 open reading frame 72 (C9orf72) repeat expansion, which represents a high-prevalence genetic abnormality for human amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) spectrum disorder, exhibit abnormal motor cortex output. The number of primary motor cortex (M1) layer 5 pyramidal neurons is reduced in asymptomatic mice, with the surviving neurons receiving a decreased inhibitory drive that results in a higher M1 output, specifically during high-speed animal locomotion. Importantly, using deep-learning algorithms revealed that speed-dependent M1 output predicts the likelihood of C9orf72 genetic expansion. Our data link early circuit abnormalities with a gene mutation in asymptomatic ALS/FTLD carriers. [Display omitted] • Asymptomatic females with C9orf72 expansion show cell-specific loss in motor cortex • Layer 5 pyramidal cells (L5-PYRs) in motor cortex are disinhibited • L5-PYRs show abnormal activity during high-speed animal locomotion • Training deep neural networks to decode motor cortex output predicts mutation Amalyan et al. report a deficit in synaptic inhibition and increased activity in the layer 5 pyramidal neurons of the primary motor cortex (M1) in asymptomatic female carriers with C9orf72 genetic expansion. Training deep neural networks to decode the abnormal M1 output allowed prediction of the pathological phenotype. [ABSTRACT FROM AUTHOR]

Signal propagation in neuronal dendrites represents the basis for interneuron communication and information processing in the brain. Here we take into account charge inhomogeneities arising in the vicinity of ion channels in cytoplasm and obtain a modified cable equation. We show that charge inhomogeneities acting on a millisecond time scale can lead to the appearance of propagating waves with wavelengths of hundreds of micrometers. They correspond to a certain frequency band predicting the appearance of resonant properties in brain neuron signaling. We also show that membrane potential in spiny dendrites obeys the modified cable equation suggesting a crucial role of the spines in dendritic subthreshold resonance. [ABSTRACT FROM AUTHOR]

Nicotinic acetylcholine receptors (nAChRs) modulate the cholinergic drive to a hierarchy of inhibitory neurons in the superficial layers of the PFC, critical to cognitive processes. It has been shown that genetic deletions of the various types of nAChRs impact the properties of ultra-slow transitions between high and low PFC activity states in mice during quiet wakefulness. The impact characteristics depend on specific interneuron populations expressing the manipulated receptor subtype. In addition, recent data indicate that a genetic mutation of the α 5 nAChR subunit, located on vasoactive intestinal polypeptide (VIP) inhibitory neurons, the rs16969968 single nucleotide polymorphism ( α 5 SNP), plays a key role in the hypofrontality observed in schizophrenia patients carrying the SNP. Data also indicate that chronic nicotine application to α 5 SNP mice relieves the hypofrontality. We developed a computational model to show that the activity patterns recorded in the genetically modified mice can be explained by changes in the dynamics of the local PFC circuit. Notably, our model shows that these altered PFC circuit dynamics are due to changes in the stability structure of the activity states. We identify how this stability structure is differentially modulated by cholinergic inputs to the parvalbumin (PV), somatostatin (SOM) or the VIP inhibitory populations. Our model uncovers that a change in amplitude, but not duration of the high activity states can account for the lowered pyramidal (PYR) population firing rates recorded in α 5 SNP mice. We demonstrate how nicotine-induced desensitization and upregulation of the β 2 nAChRs located on SOM interneurons, as opposed to the activation of α 5 nAChRs located on VIP interneurons, is sufficient to explain the nicotine-induced activity normalization in α 5 SNP mice. The model further implies that subsequent nicotine withdrawal may exacerbate the hypofrontality over and beyond one caused by the SNP mutation., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors.)

In fast-spiking (FS), parvalbumin-expressing interneurons of the CA1 hippocampus, activation of the GluA2-lacking Ca 2+ -permeable AMPA receptors (CP-AMPARs) in basal dendrites is coupled to Ca 2+ -induced Ca 2+ -release (CICR), and can result in a supralinear summation of postsynaptic Ca 2+ -transients (post-CaTs). While this mechanism is important in controlling the direction of long-term plasticity, it is still unknown whether it can operate at all excitatory synapses converging onto FS cells or at a set of synapses receiving a particular input. Using a combination of patch-clamp recordings and two-photon Ca 2+ imaging in acute mouse hippocampal slices with computational simulations, here we compared the generation of supralinear post-CaTs between apical and basal dendrites of FS cells. We found that, similar to basal dendrites, apical post-CaTs summated supralinearly and relied mainly on the activation of the CP-AMPARs, with a variable contribution of other Ca 2+ sources, such as NMDA receptors, L-type voltage-gated Ca 2+ -channels and Ca 2+ release. In addition, supralinear post-CaTs generated in apical dendrites had a slower decay time and a larger cumulative charge than those in basal, and were associated with a stronger level of somatic depolarization. The model predicted that modulation of ryanodine receptors and of the Ca 2+ extrusion mechanisms, such as the Na + /Ca 2+ -exchanger and SERCA pump, had a major impact on the magnitude of supralinear post-CaTs. These data reveal that supralinear Ca 2+ summation is a common mechanism of Ca 2+ signaling at CP-AMPAR-containing synapses. Shaped in a location-specific manner through modulation of ryanodine receptors and Ca 2+ extrusion mechanisms, CP-AMPAR/CICR signaling is suitable for synapse-specific bidirectional modification of incoming inputs in the absence of active dendritic conductances.