The impact of neuron morphology on cortical network architecture

The neurons in the cerebral cortex are not randomly interconnected. This specificity in wiring can result from synapse formation mechanisms that connect neurons, depending on their electrical activity and genetically defined identity. Here, we report that the morphological properties of the neurons provide an additional prominent source by which wiring specificity emerges in cortical networks. This morphologically determined wiring specificity reflects similarities between the neurons’ axo-dendritic projections patterns, the packing density, and the cellular diversity of the neuropil. The higher these three factors are, the more recurrent is the topology of the network. Conversely, the lower these factors are, the more feedforward is the network’s topology. These principles predict the empirically observed occurrences of clusters of synapses, cell type-specific connectivity patterns, and nonrandom network motifs. Thus, we demonstrate that wiring specificity emerges in the cerebral cortex at subcellular, cellular, and network scales from the specific morphological properties of its neuronal constituents

former title: A theory for the emergence of neocortical network architecture

Developmental programs that guide neurons and their neurites into specific subvolumes of the mammalian neocortex give rise to lifelong constraints for the formation of synaptic connections. To what degree do these constraints affect cortical wiring diagrams? Here we introduce an inverse modeling approach to show how cortical networks would appear if they were solely due to the spatial distributions of neurons and neurites. We find that neurite packing density and morphological diversity will inevitably translate into non-random pairwise and higher-order connectivity statistics. More importantly, we show that these non-random wiring properties are not arbitrary, but instead reflect the specific structural organization of the underlying neuropil. Our predictions are consistent with the empirically observed wiring specificity from subcellular to network scales. Thus, independent from learning and genetically encoded wiring rules, many of the properties that define the neocortex’ characteristic network architecture may emerge as a result of neuron and neurite development

Dense Statistical Connectome of Rat Barrel Cortex

Synaptic connectivity is one important constrain for cortical signal ow and function. Consequently, a complete synaptic connectivity map (i.e., connectome) of a cortical area across spatial scales would advance our understanding of cortex organization and function. We present a dense statistical connectome of the entire rat vibrissal cortex based on measured 3D distributions of axons/dendrites/somata of excitatory and inhibitory neurons. By calculating the structural overlap between pre- and postsynaptic cells our model provides quantitative estimates on connectivity measurements like connection probability and number of synapses on cell type, cellular, and subcellular levels. We found that our model reproduces connectivity measurements between thalamic and excitatory/inhibitory neurons reported in paired recordings and light- and electronmicroscopic studies. Similarly, intracortical synaptic connectivity of our model matches most connectivity measurements. However, the location and distance between pre- and postsynaptic cells and - in case of slicing experiments - the degree of truncation strongly in uences the connectivity. When reproducing electronmicroscopic and in vitro slicing experiments in our model, we found that measurements obtained under the respective experimental conditions are in line with our model's results, but represent only a small fraction of the underlying distribution. The experimental conditions such as the small volume analyzed in electron-microscopic studies or the truncation of morphologies thus biases the conclusions that are drawn, e.g. an underestimation of the connection probability. Our approach can therefore be used to improve experimental design and seen as a starting point to simulate sensory-evoked signal ow and investigate structural and functional organization of the cortex

Dense Statistical Connectome of Rat Barrel Cortex

Synaptic connectivity is one important constrain for cortical signal flow and function. Consequently, a complete synaptic connectivity map (i.e., connectome) of a cortical area across spatial scales would advance our understanding of cortex organization and function. We present a dense statistical connectome of the entire rat vibrissal cortex based on measured 3D distributions of axons/dendrites/somata of excitatory and inhibitory neurons. By calculating the structural overlap between pre- and postsynaptic cells our model provides quantitative estimates on connectivity measurements like connection probability and number of synapses on cell type, cellular, and subcellular levels. We found that our model reproduces connectivity measurements between thalamic and excitatory/inhibitory neurons reported in paired recordings and light- and electron-microscopic studies. Similarly, intracortical synaptic connectivity of our model matches most connectivity measurements. However, the location and distance between pre- and postsynaptic cells and - in case of slicing experiments - the degree of truncation strongly influences the connectivity. When reproducing electronmicroscopic and in vitro slicing experiments in our model, we found that measurements obtained under the respective experimental conditions are in line with our model's results, but represent only a small fraction of the underlying distribution. The experimental conditions such as the small volume analyzed in electron-microscopic studies or the truncation of morphologies thus biases the conclusions that are drawn, e.g. an underestimation of the connection probability. Our approach can therefore be used to improve experimental design and seen as a starting point to simulate sensory-evoked signal flow and investigate structural and functional organization of the cortex

Inhibitory Transfer of Thalamocortical Input in a Column of Rat Vibrissal Cortex

Thalamocortical (TC) afferents give rise to the elementary functional units of sensory cortex, cortical columns. Principles underlying spread of inhibition within a column remain, however, unknown. Here, we unravel how TC input may be relayed spatially by inhibitory interneurons (INs) by classifying axonal projection patterns of INs (N=204) located throughout layers (L) 2-6 in rat vibrissal cortex (vS1). We found five projection types independent of laminar location and postsynaptic target specificity. We integrated these into a dense average 3D model of rat vS1, thus allowing describing TC input to INs by structural overlap. This procedure provided first-order quantitative estimates of how TC input is relayed by INs within a column. We found three major TC->IN pathways that are largely decoupled from those of excitatory cell types: (i) focal inhibition to L4, (ii) blanket inhibition, and (iii) specific inhibition targeting cortical zones of highest IN soma density

Sources of wiring specificity in a connectome model of rat barrel cortex

We present an anatomically constrained model of the dense wiring diagram of thalamoand intra-cortical circuits in rat barrel cortex. The model is based on the assumptions that synaptic contacts are formed (1) randomly { i.e. proportional to the locally available of pre- and postsynaptic target structures { and (2) independent of each other. These two assumptions can be regarded as a 'null-hypothesis' for non-specificity in cortical connectivity (commonly referred to as Peters' Rule), allowing to test whether wiring diagrams as predicted from these assumptions are in line with different experimental measurements. We show that the model reproduces measurements of 1st order statistics (e.g. pair-wise connection probability) for all cell types and layers, and that it predicts highly non-random 2nd and higher order connectivity patterns. We find that non-random (i.e. specific) connectivity patterns in cortex do not necessarily originate from local specificity rules, but reflect cell type- and location-specific organizational principles at population, cellular and morphological levels

Generation of dense statistical connectomes from sparse morphological data

Sensory-evoked signal flow, at cellular and network levels, is primarily determined by the synaptic wiring of the underlying neuronal circuitry. Measurements of synaptic innervation, connection probabilities and sub-cellular organization of synaptic inputs are thus among the most active fields of research in contemporary neuroscience. Methods to measure these quantities range from electrophysiological recordings over reconstructions of dendrite-axon overlap at light-microscopic levels to dense circuit reconstructions of small volumes at electron-microscopic resolution. However, quantitative and complete measurements at subcellular resolution and mesoscopic scales to obtain all local and long-range synaptic in/outputs for any neuron within an entire brain region are beyond present methodological limits. Here, we present a novel concept, implemented within an interactive software environment called NeuroNet, which allows (i) integration of sparsely sampled (sub)cellular morphological data into an accurate anatomical reference frame of the brain region(s) of interest, (ii) up-scaling to generate an average dense model of the neuronal circuitry within the respective brain region(s) and (iii) statistical measurements of synaptic innervation between all neurons within the model. We illustrate our approach by generating a dense average model of the entire rat vibrissal cortex, providing the required anatomical data, and illustrate how to measure synaptic innervation statistically. Comparing our results with data from paired recordings in vitro and in vivo, as well as with reconstructions of synaptic contact sites at light- and electron-microscopic levels, we find that our in silico measurements are in line with previous results