Region-specific network plasticity in simulated and living cortical networks: comparison of the center of activity trajectory (CAT) with other statistics

Abstract

Electrically interfaced cortical networks cultured in vitro can be used as a model for studying the network mechanisms of learning and memory. Lasting changes in functional connectivity have been difficult to detect with extracellular multi-electrode arrays using standard firing rate statistics. We used both simulated and living networks to compare the ability of various statistics to quantify functional plasticity at the network level. Using a simulated integrate-and-fire neural network, we compared five established statistical methods to one of our own design, called center of activity trajectory (CAT). CAT, which depicts dynamics of the location-weighted average of spatiotemporal patterns of action potentials across the physical space of the neuronal circuitry, was the most sensitive statistic for detecting tetanus-induced plasticity in both simulated and living networks. By reducing the dimensionality of multi-unit data while still including spatial information, CAT allows efficient real-time computation of spatiotemporal activity patterns. Thus, CAT will be useful for studies in vivo or in vitro in which the locations of recording sites on multi-electrode probes are important.

Plasticity of recurring spatiotemporal activity patterns in cortical networks

“…The mean Euclidean distance of integrated occurrences in Pre2 to the centroid of integrated occurrences in Pre1 (C, for change) was compared to the mean Euclidean distance of integrated occurrences in Pre1 to their own centroid (D, for drift). The ratio of change to drift, C/D, was used to quantify the change from Pre1 to Pre2 before the tetanus (no change if this ratio is ∼1) (Chao et al 2007). Similarly, C/D between Pre2 and Post1 was used to quantify the change across the tetanus, and C/D between Post1 and Post2 was used to quantify the stability of tetanus-induced changes.…”

Physiological consequences of selective suppression of synaptic transmission in developing cerebral cortical networks in vitro: Differential effects on intrinsically generated bioelectric discharges in a living ‘model’ system for slow-wave sleep activity

“…Little would be gained with respect to such parameters by sampling more than a single point in a given preparation. Much of the interindividual variance in other variables measured, however, is an expression of regional differences within each explant, as is readily ascertainable by recording either sequentially (see Baker et al, 2006;Crain, 1976) or from several electrodes simultaneously (e.g., Plenz, 2003, 2004;Bettencourt et al, 2007;Chao et al, 2007;Droge et al, 1986;Giugliano et al, 2004;Jimbo and Robinson, 2000;Krause et al, 2006;Madhavan et al, 2007;Potter and DeMarse, 2001;Rolston et al, 2007;Wagenaar et al, 2006a,b). Thus, the mean duration, intensity and oscillatory character of a midi-or maxi-burst tends to be unpredictably site-specific, even in mega-co-cultures , thus giving each age and treatment group an irreducibly large variance with regard to those parameters (Echevarria and Albus, 2000;Pinato et al, 1999;Wagenaar et al, 2006a).…”

“…Multi-electrode recording techniques offer, of course, the additional advantage of quantifying each channel separately so that local interactions within a neural network can be studied (e.g., Chao et al, 2007;Li et al, 2007a,b;Van Pelt et al, 2004a,b, 2005. Such studies have revealed that bursts are initiated probabilistically from different locations in dispersed neuronal cell cultures, but that the incidence of such 'leading sites' can vary considerably (Droge et al, 1986;Menendez de la Prida and Sanchez-Andres, 2000;Robinson et al, 1993;Tsodyks et al, 2000;Volgushev et al, 2006;Yvon et al, 2005; also see Compte et al, 2003;Massimini et al, 2004).…”

Adaptive Goal-Directed Behavior in Embodied Cultured Networks: Living Neuronal Networks and a Simulated Model

“…1, ). The inclusion of spatial information was found to better depict network state and neural plasticity [9]. T transformed CA from neural electrode space to the population vector in animat movement space (Fig.1, ): the range of CAs were found prior to the closed-loop experiment, offset by the mean, and scaled separately in the X and Y directions to produce a uniform distribution and the ability to move in all directions.…”