Black dots on the blue wall indicate the location of the somata for the various neuron types. The box floor represents the predicted circuitry, in which black and gray squares correspond to potential excitatory and inhibitory connections, respectively, and white squares indicate absence of synapses.
The two walls of the box represent the pre-synaptic axonal (red) and post-synaptic dendritic (blue) binary distributions of every neuron type (listed horizontally) across all neuropils (listed vertically). C: A periodic table of neuromorphological patterns in the dentate gyrus. Abbreviations: SMo (outer stratum moleculare), SMi (inner stratum moleculare), SG (stratum granulare), H (hilus), SLM (stratum lacunosum-moleculare), SR (stratum radiatum), SL (stratum lucidum), SP (stratum pyramidale), and SO (stratum oriens). The recurrent and commissural terminals of CA3 pyramidal cells demarcate the oriens and radiatum layers in CA3. The mossy fiber (MF) axonal terminals of DG granule cells define the hilus in DG and the lucidum layer in CA3. The perforant path (PP) axonal terminals of entorhinal layer 2 spiny stellate cells delineate the outer molecular layer in DG and lacunosum-moleculare in CA3. B: The layers of the dentate gyrus (DG) and hippocampal area CA3 are defined by the somatic locations and axonal terminals of principal neurons. ) and of an OML cell (dendrites in black, axons in green NMO_00178 from Ref. A: Morphological reconstructions (from NeuroMorpho.Org ) of a rat dentate gyrus semilunar granule cell (dendrites in blue, axons in red NMO_10035 from Ref. In this example, neuron types are divided along the vertical axis based on parvalbumin (PV) expression (one of many molecular biomarkers), along the depth axis based on firing threshold potential (V thresh one of many electrophysiological variables), and along the horizontal axis as to whether they can receive perforant path (PP) input (one of many morphological factors).Īxonal-dendritic patterns and anatomical regions: two sides of the same coin. D: The GABAergic interneurons of hippocampal area CA1 can be grouped on the basis of selected properties across three dimensions in a highly simplified binary classification scheme. The advantages and disadvantages of every technique are color-coded along themes that recur across dimensions: invasiveness, cost, scalability, training time, coverage scope, and resolution. Each approach is suitable to investigate a subset of general properties and specific features based on different techniques.
The three main experimental approaches to characterize neuron types. At the same time, this core classification scheme is suitable for aggregating biochemical, physiological, and synaptic information.Īxons circuits classification dendrites neurons. Furthermore, among the features commonly employed to describe neurons, morphology is considerably robust to experimental conditions. Just as the positions of the elements in the periodic table indicate their potential to combine into molecules, axonal and dendritic distributions provide the blueprint for network connectivity. We propose that the patterns of presence or absence of axons and dendrites within known anatomical parcels may serve as the key principle to define neuron types. The situation is not dissimilar to the state of Chemistry prior to Mendeleev's tabulation of the elements.
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No one knows yet how to organize, in a simple yet predictive form, the knowledge concerning the anatomical, biophysical, and molecular properties of neurons that are accumulating in thousands of publications every year.
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