Abstract
Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these “connectivity methods” on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings.
Highlights
Large random networks of cortical neurons developing in vitro and chronically coupled to Micro-Electrode Arrays (MEAs) (Figure 1A) represent a well established experimental model for studying the neuronal dynamics at the network level [1,2,3,4,5], and for understanding the basic principles of information coding [6], separation property [7], learning [8], and memory [9]
We present the performances of Transfer Entropy (TE), Mutual Information (MI), JE and CC evaluated by means of Receiver Operating Characteristic (ROC) and Positive Precision Curve (PPC) curves
In this work we compared the performances of well established and novel techniques to estimate the functional connectivity in cultured cortical neurons
Summary
Large random networks of cortical neurons developing in vitro and chronically coupled to Micro-Electrode Arrays (MEAs) (Figure 1A) represent a well established experimental model for studying the neuronal dynamics at the network level [1,2,3,4,5], and for understanding the basic principles of information coding [6], separation property [7], learning [8], and memory [9]. The introduction of MEAs allows simultaneous recordings from tens of microelectrodes, giving the opportunity to access several ‘‘nodes’’ of the network, to study how neurons are connected each other, and which topological architectures underlie a specific dynamic behavior [10,11] Within this topic, recent technological efforts (increase of the number of electrodes and of the spatial resolution [12]), allow to obtain a more precise mapping of the neuronal network up to a possible identification of its anatomical connections (i.e., the set of physical or structural-synaptic connections linking neuronal units at a given time [13]). There are some drawbacks related to the limited access to single units and large populations at the same time, and to a poor temporal resolution [14]
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