Development and Plasticity of Spontaneous Activity and Up States in Cortical Organotypic Slices

Abstract

Cortical computations are an emergent property of neural dynamics. To understand how neural dynamics emerges within local cortical networks, we characterized the development and underlying mechanisms of spontaneous dynamics in cortical organotypic slices. We observed not only a quantitative increase in the levels of spontaneous dynamics, but a qualitative transition from brief bursts of activity to well defined Up states during the first 4 weeks in vitro. Analysis of cellular and synaptic properties indicates that these changes are driven by increasing excitatory drive accompanied by changes in the balance of excitation and inhibition. Examination of the structure of spontaneous dynamics revealed no evidence of precisely repeating patterns. Slices exposed to chronic patterned stimulation exhibited decreased levels of spontaneous activity, suggesting homeostatic control of the levels of network activity. Together, these results suggest that Up states reflect a fundamental mode of network dynamics that emerges through the orchestrated regulation of multiple cellular and synaptic properties in parallel.

Homeostasis of neuronal avalanches during postnatal cortex development in vitro

“…Using recordings from single networks over the course of 6 weeks, the present study extends these observations and demonstrates that neuronal avalanches are a robust form of neuronal synchronization that occurs throughout postnatal development. Large changes in activity levels have been reported previously for organotypic slice cultures during postnatal maturation (Maeda, Robinson et al, 1995;Kamioka, Maeda et al, 1996;Corner, van Pelt et al, 2002;Johnson and Buonomano, 2007) and are also found in our study. Importantly, we show that despite these changes in activity levels, the organization of synchronization in the form of neuronal avalanches remains constant for many weeks even in single cultures.…”

“…These cultures transition through different activity levels during postnatal maturation (Maeda, Robinson et al, 1995;Kamioka, Maeda et al, 1996;Corner, van Pelt et al, 2002;Johnson and Buonomano, 2007) and are well suited to identify a common network state that encompasses the variable features of the overall activity. We show that a power law with an exponent of α = −1.5, i.e.…”

Spike-Time Precision and Network Synchrony Are Controlled by the Homeostatic Regulation of the D-Type Potassium Current

“…Spontaneous network activity constitutes a hallmark of developing neuronal networks and organotypic hippocampal slices display in vitro increasing amounts of synaptic activity (Gähwiler et al, 1997;Johnson and Buonomano, 2007). Since I D helps control excitability, we tested whether it might be modified during homeostatic plasticity induced by activity deprivation.…”

Embedding Multiple Trajectories in Simulated Recurrent Neural Networks in a Self-Organizing Manner

“…Rich dynamical behaviors, in the form of spatiotemporal patterns of neuronal spikes are observed in vitro (Beggs and Plenz, 2003;Shu et al, 2003;Johnson and Buonomano, 2007) and in vivo (Wessberg et al, 2000;Churchland et al, 2007;Pastalkova et al, 2008), and have been shown to code information about sensory inputs (Laurent, 2002;Broome et al, 2006), motor behaviors (Wessberg et al, 2000;Hahnloser et al, 2002), as well as memory and planning (Euston et al, 2007;Pastalkova et al, 2008). Although it is clear that the neural dynamics that emerges as a result of the recurrent architecture of cortical networks is fundamental to brain function, relatively little is known about how recurrent networks are set up in a manner that support computations, yet avoid pathological states, including runaway excitation and epileptic activity.…”

“…Experimental studies using organotypic cortical slices have shown that during the first week of in vitro development, a brief stimulus does not lead to any propagation, but at later stages stimulation elicits spatiotemporal patterns of activity lasting up to a few hundred milliseconds (Buonomano, 2003;Johnson and Buonomano, 2007). Here, we sought to examine the learning rules that could lead to this type of evoked propagation.…”