title: Passive dendritic integration heavily affects spiking dynamics of recurrent networks.
abstract: According to dendritic cable theory, proximal synapses give rise to inputs with short delay, high amplitude, and short duration. In contrast, inputs from distal synapses have long delays, low amplitude, and long duration. Nevertheless, large scale neural networks are seldom built with realistically layered synaptic architectures and corresponding electrotonic parameters. Here, we use a simple model to investigate the spike response dynamics of networks with different electrotonic structures. The networks consist of a layer of neurons receiving a sparse feedforward projection from a set of inputs, as well as sparse recurrent connections from within the layer. Firing patterns are set in the inputs, and recorded from the neuron (output) layer. The feedforward and recurrent synapses are independently set as proximal or distal, representing dendritic connections near or far from the soma, respectively. Analyses of firing dynamics indicate that recurrent distal synapses tend to concentrate network activity in fewer neurons, while proximal recurrent synapses result in a more homogeneous activity distribution. In addition, when the feedforward input is regular (spiking or bursting) and asynchronous, the output is regular if recurrent synapses are more distal than feedforward ones, and irregular in the opposite configuration. Finally, the amplitude of network fluctuations in response to asynchronous input is lower if feedforward and recurrent synapses are electrotonically distant from one another (in either configuration). In conclusion, electrotonic effects reflecting different dendritic positions of synaptic inputs significantly influence network dynamics.
authors.affiliation: Krasnow Institute and Psychology Department, George Mason University, 4400 University Dr., Fairfax, VA 22030, USA. ascoli@gmu.edu
authors.forename: Giorgio A
authors.initials: GA
authors.lastname: Ascoli
mesh_terms: D000200:Action Potentials / Q000502:physiology; D003712:Dendrites / Q000502:physiology; D016571:Neural Networks, Computer