Abstract
Experimental studies of neuronal cultures have revealed a wide variety of spiking network activity ranging from sparse, asynchronous firing to distinct, network-wide synchronous bursting. However, the functional mechanisms driving these observed firing patterns are not well understood. In this work, we develop an in silico network of cortical neurons based on known features of similar in vitro networks. The activity from these simulations is found to closely mimic experimental data. Furthermore, the strength or degree of network bursting is found to depend on a few parameters: the density of the culture, the type of synaptic connections, and the ratio of excitatory to inhibitory connections. Network bursting gradually becomes more prominent as either the density, the fraction of long range connections, or the fraction of excitatory neurons is increased. Interestingly, biologically prevalent values of parameters result in networks that are at the transition between strong bursting and sparse firing. Using principal components analysis, we show that a large fraction of the variance in firing rates is captured by the first component for bursting networks. These results have implications for understanding how information is encoded at the population level as well as for why certain network parameters are ubiquitous in cortical tissue.
Highlights
Networks of cultured neurons have proven to be a valuable tool in the study of the mechanisms of learning, plasticity, information processing, and bursting (Shahaf and Marom, 2001; Eytan et al, 2003)
A wide variety of spatiotemporal firing patterns has been observed in cultured neuronal recordings, including: sporadic or asynchronous firing, synchronized network bursting, and neuronal avalanches (Maeda et al, 1995; van Pelt et al, 2004; Chen et al, 2006; Eytan and Marom, 2006; Eckmann et al, 2008; Petermann et al, 2009)
These results help to uncover the nature of network bursting observed in in vitro cultures
Summary
Networks of cultured neurons have proven to be a valuable tool in the study of the mechanisms of learning, plasticity, information processing, and bursting (Shahaf and Marom, 2001; Eytan et al, 2003). With the advent of multielectrode arrays (MEAs), it is possible to monitor network activity by recording extracellularly from large numbers of neurons in vitro (Potter and DeMarse, 2001; Segev et al, 2001) or to influence network behavior through direct stimulation (Wagenaar et al, 2004). A wide variety of spatiotemporal firing patterns has been observed in cultured neuronal recordings, including: sporadic or asynchronous firing, synchronized network bursting, and neuronal avalanches (Maeda et al, 1995; van Pelt et al, 2004; Chen et al, 2006; Eytan and Marom, 2006; Eckmann et al, 2008; Petermann et al, 2009). Shew et al used information theoretic measures to show that cultured neuronal networks maximized information at a particular ratio of excitation to inhibition (Shew et al, 2011)
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