Classifying heterogeneity of spontaneous up-states: a method for revealing variations in firing probability, engaged neurons and Fano factor.
The dynamics of spontaneous and sensory-evoked up-states have been recently compared, in multi-site recordings in vivo and found to have similarities and differences. Also in vitro, this is evident because we here describe a novel computational method to classify into statistically different states the spontaneous reverberating activity recorded from long-term (12-18 days-in vitro) cultured cortical neurons (from 60-site multi-electrode arrays, MEA). State classification was performed by spike number time histograms (SNTH, or other burst features) of excitatory and inhibitory neuron clusters and revealed that in novel identified states the number of engaged neurons or up-state duration can change. To improve the characterization of each state we also computed the firing spike histograms (FSH) which revealed a new facet of the firing probability of clusters. In exemplary functional experiments we show that: (i) up to 6-7 states can be safely categorized during several hours of recordings without observing spike rate changes, (ii) they disappear after a short pharmacological stimulation being replaced with novel states active and living up to 6-8 h, (iii) antagonists in the nM range can split the activity of a homogeneous network into the chronological coexistence of 2 states, one completely different and one not significantly different from control state. In conclusion, we believe that this novel procedure better characterizes the number of functional states of a network and opens up the possibility of predicting the elementary "vocabulary" used by small networks of neurons.
Department of Biotechnologies and Biosciences, University of Milano-Bicocca, Milan, Italy.
Francesca
F
Gullo
Andrea
A
Maffezzoli
Elena
E
Dossi
Marzia
M
Lecchi
Enzo
E
Wanke
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D000831:Animals, Newborn; D004594:Electrophysiology / Q000379:methods*; D051379:Mice; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology; D009474:Neurons / Q000145:classification / Q000502:physiology*; D061251:Primary Cell Culture; D012815:Signal Processing, Computer-Assisted; D009435:Synaptic Transmission / Q000502:physiology*