P008 Data-Driven Functional Analysis of a Mammalian Neuron Type Connectome
Giorgio A. Ascoli*1
1Center for Neural Informatics, George Mason University, Fairfax (VA), USA
*Email: ascoli@gmu.edu
Introduction
The increasing availability of dense connectomes enables unprecedented opportunities for the quantitative investigation of neural circuitry. Although these advances are essential to reveal the architectural principles of biological neural networks, they fall short of providing a complete accounting of functional dynamics. To understand the computational role of specific neuron types within this structural blueprint, connectivity must be complemented by essential physiological parameters quantifying intrinsic excitability as well as synaptic transmission.
Methods The communication through a pair of neuron types can be characterized to a first approximation by (1) their connection probability; (2) the pre-synaptic cell count; the (3) post-synaptic conductance peak value, sign (excitatory vs. inhibitory), and (4) decay time constant (signal duration); and (5) the input-output function of the post-synaptic neuron type. If these data could be measured or estimated experimentally for each neuron type pair, it should then be possible to compute signal propagation throughout the network from any arbitrary stimulation. We have collected all the above parameters from experimental measurements for every known neuron type in the rodent hippocampal-entorhinal formation (hippocampome.org). Results This framework allows one to calculate the instantaneous firing rate of each neuron type based on its input-output function and total input current; the total input current corresponds to the sum of charge transfer from all of its presynaptic partners; and the charge transfer from each partner can be derived by multiplying the peak conductance, time constant, and presynaptic firing rate at the immediately preceding time. Extending this calculation to all neuron types based on their connectivity yields the evolution of activity dynamics across the entire network as a function of time. Discussion The described approach allows a functional connectomic analysis of a whole mammalian cortical circuit at the neuron type level. This first approximation should then be refined based on short- and long-term synaptic plasticity, signal delays, and non-linearities in charge transfer integration. Possible applications include graph-theoretic analysis of activity dynamics and multiscale modeling linking whole neural system level to single-neuron compartmental simulations.
Acknowledgements NIH grant R01 NS39600htt References https://hippocampome.org
