Quantitative examination of stimulus-response relations in cortical networks in vitro

Abstract

Variable responses of neuronal networks to repeated sensory or electrical stimuli reflect the interaction of the stimulus' response with ongoing activity in the brain and its modulation by adaptive mechanisms, such as cognitive context, network state, or cellular excitability and synaptic transmission capability. Here, we focus on reliability, length, delays, and variability of evoked responses with respect to their spatial distribution, interaction with spontaneous activity in the networks, and the contribution of GABAergic inhibition. We identified network-intrinsic principles that underlie the formation and modulation of spontaneous activity and stimulus-response relations with the use of state-dependent stimulation in generic neuronal networks in vitro. The duration of spontaneously recurring network-wide bursts of spikes was best predicted by the length of the preceding interval. Length, delay, and structure of responses to identical stimuli systematically depended on stimulus timing and distance to the stimulation site, which were described by a set of simple functions of spontaneous activity. Response length at proximal recording sites increased with the duration of prestimulus inactivity and was best described by a saturation function y(t) ϭ A(1 Ϫ e Ϫ␣t ). Concomitantly, the delays of polysynaptic late responses at distant sites followed an exponential decay y(t) ϭ Be Ϫ␤t ϩ C. In addition, the speed of propagation was determined by the overall state of the network at the moment of stimulation. Disinhibition increased the number of spikes/network burst and interburst interval length at unchanged gross firing rate, whereas the response modulation by the duration of prestimulus inactivity was preserved. Our data suggest a process of network depression during bursts and subsequent recovery that limit evoked responses following distinct rules. We discuss shortterm synaptic depression due to depletion of neurotransmitter vesicles as an underlying mechanism. The seemingly unreliable patterns of spontaneous activity and stimulus-response relations thus follow a predictable structure determined by the interdependencies of network structures and activity states.

Synaptic dynamics contribute to long-term single neuron response fluctuations

“…Hence, they might interfere with our capacity to interpret the impacts of stimulation on response dynamics (Wallach and Marom, 2012; Weihberger et al, 2013). Network spikes may be suppressed by pharmacological blockage of NMDA channels in cultured cortical networks (Robinson et al, 1993; Jimbo et al, 2000; Bonzano et al, 2006).…”

Controlling neural network responsiveness: tradeoffs and constraints

“…This preparation proved useful, over the past 10–15 years, as a toy model in the study of functional network processes, ranging from development and adaptation to learning and stimulus representation (Maeda et al, 1995; Kamioka et al, 1996; Jimbo et al, 1998, 1999; Tateno and Jimbo, 1999; Shahaf and Marom, 2001; Corner et al, 2002; Eytan et al, 2003; Shahaf et al, 2008). A recent series of studies demonstrated the efficacy of several closed loop applications in controlling aspects of activity in these in vitro large-scale cortical networks (e.g., Wagenaar and Potter, 2004; Wagenaar et al, 2005; Arsiero et al, 2007; Rolston et al, 2010; Wallach et al, 2011; Weihberger et al, 2013). Here we implement one of these approaches, the so-called “response-clamp” procedure: a PI (Proportional-Integral) negative feedback algorithm (Wallach et al, 2011; Wallach, 2013), in order to control response features in vitro .…”

Investigating the impact of electrical stimulation temporal distribution on cortical network responses

“…1 or ∞). Based on previous results reported in the literature for regular stimulation [49, 50], we chose 0.2 Hz as a separation frequency in order to distinguish between two different conditions: time-dependence (MSR > 0.2 Hz) or independence (MSR < 0.2 Hz) of responses to subsequent stimuli. It is possible to note how the network preference for irregular stimulation sequences described above holds true only for fast sequences (i.e.…”

“…In 2013, a paper published by the group of Egert [50] suggested that strength and duration of neural network responses to electrical stimulations followed an exponential saturating profile as a function of the length of the preceding inactivity period. These results were compatible with short-term synaptic depression caused by bursts, due to depletion of readily releasable pool of neurotransmitter vesicles, also in conjunction with GABAergic inhibition (see Discussion of [50]).…”