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Biol Cybern (2014) 108:261273 DOI 10.1007/s00422-014-0601-y
ORIGINAL PAPER
Dynamical estimation of neuron and network properties III: network analysis using neuron spike times
Chris Knowlton C. Daniel Meliza
Daniel Margoliash Henry D. I. Abarbanel
Received: 26 August 2013 / Accepted: 24 March 2014 / Published online: 24 April 2014 Springer-Verlag Berlin Heidelberg 2014
Abstract Estimating the behavior of a network of neurons requires accurate models of the individual neurons along with accurate characterizations of the connections among them. Whereas for a single cell, measurements of the intracellular voltage are technically feasible and sufcient to characterize a useful model of its behavior, making sufcient numbers of simultaneous intracellular measurements to characterize even small networks is infeasible. This paper builds on prior work on single neurons to explore whether knowledge of the time of spiking of neurons in a network, once the nodes (neurons) have been characterized biophysically, can provide enough information to usefully constrain the functional architecture of the network: the existence of synaptic links among neurons and their strength. Using standardized voltage and synaptic gating variable waveforms associated with a spike, we demonstrate that the functional architecture of a small network of model neurons can be established.
Keywords Networks Data assimilation Neuronal
dynamics
1 Introduction
Networks of neurons enable information processing and control. They are composed of a variety of neuron types, connected by chemical and/or electrical synapses. Over short periods of time, the behavior of a given network is determined by its architecture, namely the types of cells, the connectivity between cells, the types of synaptic connections, and the strengths of the synaptic connections. To fully characterize and predict the behavior of an identied network, one would need to know this architecture as well as any external currents or driving forces (afferent input) applied to the network. Networks may change structure as a result of plasticity or external neuromodulatory input, so it would be useful to be able to rapidly characterize the current state of a network in a short amount of time.
The general procedure outlined here is to build the networks from the bottom up. Initially, neurons are characterized individually using intracellular recording methods, which allow for low-noise (1mV RMS) measurements of trans-
membrane voltage at high sampling rates (50kHz) (Hamill et al....