Shaping Neuronal Network Activity by Presynaptic Mechanisms

RESEARCH ARTICLE Shaping Neuronal Network Activity by Presynaptic Mechanisms Ayal Lavi1,2☯, Omri Perez2,3☯, Uri Ashery1,2* 1 Department of Neurobiology, Life Sciences Institute, Tel Aviv University, Tel Aviv, Israel, 2 Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, 3 Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ☯ These authors contributed equally to this work. * Abstract OPEN ACCESS Citation: Lavi A, Perez O, Ashery U (2015) Shaping Neuronal Network Activity by Presynaptic Mechanisms. PLoS Comput Biol 11(9): e1004438. doi:10.1371/journal.pcbi.1004438 Editor: Boris S. Gutkin, École Normale Supérieure, College de France, CNRS, FRANCE Received: January 13, 2015 Accepted: June 23, 2015 Published: September 15, 2015 Copyright: © 2015 Lavi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: This work was supported, in part, by Israel Science Foundation Grants 1211/07 and 730/11 (http://www.isf.org.il/english/) and the German–Israeli Foundation Grant 1125- 145.1/2010 (http://www.gif. org.il/Pages/default.aspx) to UA. AL received a Teva Pharmaceutical Industries Ltd. fellowship under the Israeli National Network of Excellence in Neuroscience (NNE) established by Teva. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Neuronal microcircuits generate oscillatory activity, which has been linked to basic functions such as sleep, learning and sensorimotor gating. Although synaptic release processes are well known for their ability to shape the interaction between neurons in microcircuits, most computational models do not simulate the synaptic transmission process directly and hence cannot explain how changes in synaptic parameters alter neuronal network activity. In this paper, we present a novel neuronal network model that incorporates presynaptic release mechanisms, such as vesicle pool dynamics and calcium-dependent release probability, to model the spontaneous activity of neuronal networks. The model, which is based on modified leaky integrate-and-fire neurons, generates spontaneous network activity patterns, which are similar to experimental data and robust under changes in the model's primary gain parameters such as excitatory postsynaptic potential and connectivity ratio. Furthermore, it reliably recreates experimental findings and provides mechanistic explanations for data obtained from microelectrode array recordings, such as network burst termination and the effects of pharmacological and genetic manipulations. The model demonstrates how elevated asynchronous release, but not spontaneous release, synchronizes neuronal network activity and reveals that asynchronous release enhances utilization of the recycling vesicle pool to induce the network effect. The model further predicts a positive correlation between vesicle priming at the single-neuron level and burst frequency at the network level; this prediction is supported by experimental findings. Thus, the model is utilized to reveal how synaptic release processes at the neuronal level govern activity patterns and synchronization at the network level. Author Summary The activity of neuronal networks underlies basic neural functions such as sleep, learning and sensorimotor gating. Computational models of neuronal networks have been developed to capture the complexity of the network activity and predict how neuronal networks generate spontaneous activity. However, most computational models do not simulate the PLOS Computational Biology | DOI:10.1371/journal.pcbi.1004438 September 15, 2015 1 / 27 Shaping Network Activity by Presynaptic Mechanisms Competing Interests: The authors have declared that no competing interests exist. intricate synaptic release process that governs the interaction between neurons and has been shown to significantly impact neuronal network activity and animal behavior, learning and memory. Our paper demonstrates the importance of simulating the elaborate synaptic release process to understand how neuronal networks generate spontaneous activity and respond to manipulations of the release process. The model provides mechanistic explanations and predictions for experimental pharmacological and genetic manipulations. Thus, the model presents a novel computational platform to understand how mechanistic changes in the synaptic release process modulate network oscillatory activity that might impact basic neural functions. Introduction Oscillatory activity patterns in the brain have been linked to sleep, sensorimotor gating, shortterm memory storage and selective attention [1,2]. Neuronal microcircuits in the brain spontaneously generate oscillatory activity patterns via synaptic interaction between groups of neurons [1,2]. Indeed, changes in synaptic transmission cause alterations in neuronal firing and neuronal network activity [3–5], and synaptic dysfunction can lead to pathological epileptic conditions [6–8]. Even though small alterations in synaptic transmission and in the firing properties of single neurons can alter the spontaneous and evoked activity of entire neuronal circuits [3,9], most computational models of neuronal networks do not explicitly account for the elaborate presynaptic neurotransmission process. Presynaptic transmission is a regulated multistep process that encompasses the loading of neurotransmitters into synaptic vesicles, the translocation to and docking of those vesicles at the plasma membrane (PM), and vesicle preparation for fusion through a calcium-dependent maturation process generally referred to as "vesicle priming" [10–14]. This pool of primed vesicles is the readily releasable pool (RRP), where vesicles undergo immediate fusion with the PM upon acute elevation in intracellular calcium concentration ([Ca2+]i). Another presynaptic pool of vesicles, the recycling pool (ReP), accommodates unprimed vesicles which can undergo maturation and fusion during repetitive synaptic stimulation; all of the remaining vesicles in the presynaptic terminal belong to the reserve pool (RP). Equilibrium of the presynaptic vesicles transition between these pools depends on neuronal activity, synaptic proteins and calcium [15–19]. In the synapses, there are three types of synaptic release modes that rely on the high dynamic range of [Ca2+]i and share the same vesicle pools [20,21] (but see [22,23]). They are defined by their temporal association with the action potential (AP): a) synchronous release, driven by a short-lived acute increase in [Ca2+]i, is time-locked to the AP [24–26]; b) asynchronous release begins several milliseconds after an AP and drives slower v (...truncated)