Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type

We describe convergent evidence from transcriptomics, morphology and physiology for a specialized GABAergic neuron subtype in human cortex. Using unbiased single nucleus RNA sequencing, we identify ten GABAergic interneuron subtypes with combinatorial gene signatures in human cortical layer 1 and characterize a novel group of human interneurons with anatomical features never described in rodents having large, “rosehip”-like axonal boutons and compact arborization. These rosehip cells show an immunohistochemical profile (GAD1/CCK-positive, CNR1/SST/CALB2/PVALB-negative) matching a single transcriptomically-defined cell type whose molecular signature is not seen in mouse cortex. Rosehip cells make homotypic gap junctions, predominantly target apical dendritic shafts of layer 3 pyramidal neurons and inhibit backpropagating pyramidal action potentials in microdomains of the dendritic tuft. These cells are therefore positioned for potent local control of distal dendritic computation in cortical pyramidal neurons.

Cell-type–specific inhibition of the dendritic plateau potential in striatal spiny projection neurons

Striatal spiny projection neurons (SPNs) receive convergent excitatory synaptic inputs from the cortex and thalamus. Activation of spatially clustered and temporally synchronized excitatory inputs at the distal dendrites could trigger plateau potentials in SPNs. Such supralinear synaptic integration is crucial for dendritic computation. However, how plateau potentials interact with subsequent excitatory and inhibitory synaptic inputs remains unknown. By combining computational simulation, two-photon imaging, optogenetics, and dual-color uncaging of glutamate and GABA, we demonstrate that plateau potentials can broaden the spatiotemporal window for integrating excitatory inputs and promote spiking. The temporal window of spiking can be delicately controlled by GABAergic inhibition in a cell-type–specific manner. This subtle inhibitory control of plateau potential depends on the location and kinetics of the GABAergic inputs and is achieved by the balance between relief and reestablishment of NMDA receptor Mg2+ block. These findings represent a mechanism for controlling spatiotemporal synaptic integration in SPNs.

Multi-Transition Systems: A theory for neural spatial navigation

Spatial navigation is considered fundamental for animals and is attributed primarily to place and grid cells in the rodent brain. Commonly believed to either perform path integration or localization, the true objective of grid cells, their hexagonal grid fields, and especially their discrete scales remain puzzling. Here it is proposed that grid cells efficiently encode transitions in sequences. A biologically plausible model for dendritic computation in grid cells is presented. A network of competitive cells shows positive gridness scores early in simulations and realigns the orientation of all cells over time. Then, a scale-space model of grid cells is introduced. It improves behaviorally questionable run-times of a single scale significantly by look-ahead in multiple scales, and it is shown that the optimal scale-increment between consecutive scales is √2. Finally, a formal theory for sequences and transitions is stated. It is demonstrated that hexagonal transition encoders are optimal to encode transitions in Euclidean space and emerge due to the sampling theorem. The paper concludes with a discussion about the suggested purpose, makes testable predictions, and highlights relevant connections to computational neuroscience as well as computer science and robotics.

Dendritic processing of spontaneous neuronal sequences for one-shot learning

Spontaneous firing sequences colloquially called “preplay” are fundamental features of hippocampal network physiology. Preplay sequences have been hypothesized to participate in hippocampal learning and memory, but such functional roles and their potential cellular mechanisms remain unexplored. Here, we report a computational model based on the functional propagation of preplay sequences in the CA3 neuronal network. The model instantiates two synaptic pathways in CA3 neurons, one for proximal dendrite-somatic interactions to generate intrinsic preplay sequences and the other for distal dendritic processing of extrinsic sensory signals. The core dendritic computation is the maximization of matching between patterned activities in the two compartments through nonlinear spike generation. The model performs robust one-shot learning with long-term stability and independence that are modulated by the plasticity of dendrite-targeted inhibition. This model demonstrates that learning models combined with dendritic computations can enable preplay sequences to act as templates for rapid and stable memory formation.