![]() Yet no neural circuit has these network topologies –. Typical modelling fall-backs of fully, regularly, or randomly connected networks are understandable choices when faced with this problem. This approach brings into sharp focus a further problem: how should we wire up the models? After all, the more accurate the underlying circuitry, the more confident we will be in linking dynamics of neural models to experimentally-recordable neural activity and, ultimately, to potential functions of the modelled structure. Faced with the sheer breadth of neuron and receptor types, many researchers are abandoning attempts to intuit the ‘essential elements’ of a neural circuit, instead building large-scale models of neural circuits, modelling neuron-for-neuron –. The mammalian brain is a vastly complex structure at every level of description. Together, these properties set a unique state for the input-output computations of the striatum. Our networks show features and dynamical implications of striatal wiring that would be difficult to intuit: the dominant input to the striatal projection neuron arises from other neurons just at the edge of its dendrites, and the main inhibitory interneurons are coupled locally by electrical connections and more distally by chemical synapses. With these in hand, we constructed artificial three-dimensional networks of the rat striatum and find that the connection distributions agree well with current estimates from anatomical studies. ![]() We demonstrate an approach that gets around these problems by using the available data to construct prototype neuron morphologies, and uses these to estimate how the probability of a connection between two neurons changes as we change the distance between them. Key barriers here are the difficulty of reconstructing such networks and the paucity of critical data on neuron morphology. Consequently, these models' dynamics may not accurately reflect those of the region. The brain has an immensely complex wiring diagram, but few computational models of brain regions attempt accurate renditions of the wiring between neurons. Our models thus intimately link striatal micro-anatomy to its dynamics, providing a biologically grounded platform for further study. We show that both properties influence striatal dynamics: the most potent inhibition of a MSN arises from a region of striatum at the edge of its dendritic field and the combination of local gap junction and distal synaptic networks between FSIs sets a robust input-output regime for the MSN population. The models predict two striking network properties: the dominant GABAergic input to a MSN arises from neurons with somas at the edge of its dendritic field and FSIs are inter-connected on two different spatial scales: locally by gap junctions and distally by synapses. They also show that the striatum is sparsely connected: FSI-MSN and MSN-MSN contacts respectively form 7% and 1.7% of all possible connections. The models predict that to achieve this, FSIs should be at most 1% of the striatal population. The constructed networks predict distributions of gap junctions between FSI dendrites, synaptic contacts between MSNs, and synaptic inputs from FSIs to MSNs that are consistent with current estimates. The MSN dendrite models predicted that half of all dendritic spines are within 100µm of the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. ![]() We grew dendrite and axon models for these neurons and extracted probabilities for the presence of these neurites as a function of distance from the soma. We developed an approach for constructing anatomically realistic networks and reconstructed the GABAergic microcircuit formed by the medium spiny neurons (MSNs) and fast-spiking interneurons (FSIs) of the adult rat striatum. A system's wiring constrains its dynamics, yet modelling of neural structures often overlooks the specific networks formed by their neurons. ![]()
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