
@article {1011,
	title = {Sloppiness in spontaneously active neuronal networks.},
	journal = {J Neurosci},
	volume = {35},
	year = {2015},
	month = {2015 Jun 3},
	pages = {8480-92},
	abstract = {<p>Various plasticity mechanisms, including experience-dependent, spontaneous, as well as homeostatic ones, continuously remodel neural circuits. Yet, despite fluctuations in the properties of single neurons and synapses, the behavior and function of neuronal assemblies are generally found to be very stable over time. This raises the important question of how plasticity is coordinated across the network. To address this, we investigated the stability of network activity in cultured rat hippocampal neurons recorded with high-density multielectrode arrays over several days. We used parametric models to characterize multineuron activity patterns and analyzed their sensitivity to changes. We found that the models exhibited sloppiness, a property where the model behavior is insensitive to changes in many parameter combinations, but very sensitive to a few. The activity of neurons with sloppy parameters showed faster and larger fluctuations than the activity of a small subset of neurons associated with sensitive parameters. Furthermore, parameter sensitivity was highly correlated with firing rates. Finally, we tested our observations from cell cultures on an in vivo recording from monkey visual cortex and we confirm that spontaneous cortical activity also shows hallmarks of sloppy behavior and firing rate dependence. Our findings suggest that a small subnetwork of highly active and stable neurons supports group stability, and that this endows neuronal networks with the flexibility to continuously remodel without compromising stability and function.</p>
},
	keywords = {Action Potentials, Animals, Brain, Cells, Cultured, Electric Stimulation, Embryo, Mammalian, Entropy, Models, Neurological, Nerve Net, Neural Pathways, Neurons, Rats, Synapses, Visual Cortex},
	issn = {1529-2401},
	doi = {10.1523/JNEUROSCI.4421-14.2015},
	author = {Panas, Dagmara and Amin, Hayder and Maccione, Alessandro and Muthmann, Oliver and van Rossum, Mark and Berdondini, Luca and Hennig, Matthias H}
}
@article {1200,
	title = {Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays.},
	journal = {Front Neuroinform},
	volume = {9},
	year = {2015},
	month = {2015},
	pages = {28},
	abstract = {<p>An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data and the dense sampling of spikes with closely spaced electrodes. Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data, was used to test and validate these methods. We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio (SNR) through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units. Overall, we show how the improved spatial resolution provided by high density, large scale MEAs can be reliably exploited to characterize activity from large neural populations and brain circuits.</p>
},
	issn = {1662-5196},
	doi = {10.3389/fninf.2015.00028},
	author = {Muthmann, Jens-Oliver and Amin, Hayder and Sernagor, Evelyne and Maccione, Alessandro and Panas, Dagmara and Berdondini, Luca and Upinder S Bhalla and Hennig, Matthias H}
}
