Summary How does coordinated activity between distinct brain regions implement a set of learning rules to sculpt information processing in a given neural circuit? Using interneuron cell-type specific optical activation and pharmacogenetic silencing in vitro, we show that temporally precise pairing of direct entorhinal perforant path (PP) and trisynaptic Schaffer collateral (SC) inputs to CA1 pyramidal cells selectively suppresses SC-associated perisomatic inhibition from cholecystokinin (CCK) expressing interneurons. The CCK interneurons provide a surprisingly strong feed-forward inhibitory drive to effectively control the coincident excitation of CA1 pyramidal neurons by convergent inputs. Thus, in-phase cortico-hippocampal activity provides a powerful heterosynaptic learning rule for long-term gating of information flow through the hippocampal excitatory macrocircuit by the silencing of the CCK inhibitory microcircuit.

SUMMARY Although hippocampal CA1 pyramidal neurons (PNs) were thought to comprise a uniform population, recent evidence supports two distinct sublayers along the radial axis, with deep neurons more likely to form place cells than superficial neurons. CA1 PNs also differ along the transverse axis with regard to direct inputs from entorhinal cortex (EC), with medial EC (MEC) providing spatial information to PNs toward CA2 (proximal CA1) and lateral EC (LEC) providing non-spatial information to PNs toward subiculum (distal CA1). We demonstrate that the two inputs differentially activate the radial sublayers and that this difference reverses along the transverse axis, with MEC preferentially targeting deep PNs in proximal CA1 and LEC preferentially exciting superficial PNs in distal CA1. This differential excitation reflects differences in dendritic spine numbers. Our results reveal a heterogeneity in EC-CA1 connectivity that may help explain differential roles of CA1 PNs in spatial and non-spatial learning and memory.

Neuronal network topologies and connectivity patterns were explored in control and glutamate-injured hippocampal neuronal networks, cultured on planar multielectrode arrays. Spontaneous activity was characterized by brief episodes of synchronous firing at many sites in the array (network bursts). During such assembly activity, maximum numbers of neurons are known to interact in the network. After brief glutamate exposure followed by recovery, neuronal networks became hypersynchronous and fired network bursts at higher frequency. Connectivity maps were constructed to understand how neurons communicate during a network burst. These maps were obtained by analysing the spike trains using cross-covariance analysis and graph theory methods. Analysis of degree distribution, which is a measure of direct connections between electrodes in a neuronal network, showed exponential and Gaussian distributions in control and glutamate-injured networks, respectively. Although both the networks showed random features, smallworld properties in these networks were different. These results suggest that functional two-dimensional neuronal networks in vitro are not scale-free. After brief exposure to glutamate, normal hippocampal neuronal networks became hyperexcitable and fired a larger number of network bursts with altered network topology. The small-world network property was lost and this was accompanied by a change from an exponential to a Gaussian network.

Synaptic inputs from different brain areas are often targeted to distinct regions of neuronal dendritic arbors. Inputs to proximal dendrites usually produce large somatic EPSPs that efficiently trigger action potential (AP) output, whereas inputs to distal dendrites are greatly attenuated and may largely modulate AP output. In contrast to most other cortical and hippocampal neurons, hippocampal CA2 pyramidal neurons show unusually strong excitation by their distal dendritic inputs from entorhinal cortex (EC). In this study, we demonstrate that the ability of these EC inputs to drive CA2 AP output requires the firing of local dendritic Na+ spikes. Furthermore, we find that CA2 dendritic geometry contributes to the efficient coupling of dendritic Na+ spikes to AP output. These results provide a striking example of how dendritic spikes enable direct cortical inputs to overcome unfavorable distal synaptic locale to trigger axonal AP output and thereby enable efficient cortico-hippocampal information flow.DOI: http://dx.doi.org/10.7554/eLife.04551.001

The impact of a given neuronal pathway depends on the number of synapses it makes with its postsynaptic target, the strength of each individual synapse, and the integrative properties of the postsynaptic dendrites. Here we explore the cellular and synaptic mechanisms responsible for the differential excitatory drive from the entorhinal cortical pathway onto mouse CA2 compared with CA1 pyramidal neurons (PNs). Although both types of neurons receive direct input from entorhinal cortex onto their distal dendrites, these inputs produce a 5- to 6-fold larger EPSP at the soma of CA2 compared with CA1 PNs, which is sufficient to drive action potential output from CA2 but not CA1. Experimental and computational approaches reveal that dendritic propagation is more efficient in CA2 than CA1 as a result of differences in dendritic morphology and dendritic expression of the hyperpolarization-activated cation current (). Furthermore, there are three times as many cortical inputs onto CA2 compared with CA1 PN distal dendrites. Using a computational model, we demonstrate that the differences in dendritic properties of CA2 compared with CA1 PNs are necessary to enable the CA2 PNs to generate their characteristically large EPSPs in response to their cortical inputs; in contrast, CA1 dendritic properties limit the size of the EPSPs they generate, even to a similar number of cortical inputs. Thus, the matching of dendritic integrative properties with the density of innervation is crucial for the differential processing of information from the direct cortical inputs by CA2 compared with CA1 PNs. Recent discoveries have shown that the long-neglected hippocampal CA2 region has distinct synaptic properties and plays a prominent role in social memory and schizophrenia. This study addresses the puzzling finding that the direct entorhinal cortical inputs to hippocampus, which target the very distal pyramidal neuron dendrites, provide an unusually strong excitatory drive at the soma of CA2 pyramidal neurons, with EPSPs that are 5-6 times larger than those in CA1 pyramidal neurons. We here elucidate synaptic and dendritic mechanisms that account quantitatively for the marked difference in EPSP size. Our findings further demonstrate the general importance of fine-tuning the integrative properties of neuronal dendrites to their density of synaptic innervation.

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Many Coronavirus disease 2019 (COVID-19) and post-COVID-19 patients experience muscle fatigues. Early detection of muscle fatigue and muscular paralysis helps in the diagnosis, prediction, and prevention of COVID-19 and post-COVID-19 patients. Nowadays, the biomedical and clinical domains widely used the electromyography (EMG) signal due to its ability to differentiate various neuromuscular diseases. In general, nerves or muscles and the spinal cord influence numerous neuromuscular disorders. The clinical examination plays a major role in early finding and diagnosis of these diseases; this research study focused on the prediction of muscular paralysis using EMG signals. Machine learning–based diagnosis of the diseases has been widely used due to its efficiency and the hybrid feature extraction (FE) methods with deep learning classifier are used for the muscular paralysis disease prediction. The discrete wavelet transform (DWT) method is applied to decompose the EMG signal and reduce feature degradation. The proposed hybrid FE method consists of Yule-Walker, Burg’s method, Renyi entropy, mean absolute value, min-max voltage FE, and other 17 conventional features for prediction of muscular paralysis disease. The hybrid FE method has the advantage of extract the relevant features from the signals and the Relief-F feature selection (FS) method is applied to select the optimal relevant feature for the deep learning classifier. The University of California, Irvine (UCI), EMG-Lower Limb Dataset is used to determine the performance of the proposed classifier. The evaluation shows that the proposed hybrid FE method achieved 88% of precision, while the existing neural network (NN) achieved 65% of precision and the support vector machine (SVM) achieved 35% of precision on whole EMG signal.

high-quality magnetotelluric data at 100 stations, provide both regional information about the thickness of the Deccan Traps and the occurrence of localized density heterogeneities and anomalous conductive zones in the vicinity of the hypocentral zone. Acquisition of airborne LiDAR data to obtain a high-resolution topographic model of the region has been completed over an area of 1,064 km 2 centred on the Koyna seismic zone. Seismometers have been deployed in the granitic basement inside two boreholes and are planned in another set of six boreholes to obtain accurate hypocentral locations and constrain the disposition of fault zones.