Dataset Information

Nonlinear Spatiotemporal Integration by Electrical and Chemical Synapses in the Retina.

ABSTRACT: Electrical and chemical synapses coexist in circuits throughout the CNS. Yet, it is not well understood how electrical and chemical synaptic transmission interact to determine the functional output of networks endowed with both types of synapse. We found that release of glutamate from bipolar cells onto retinal ganglion cells (RGCs) was strongly shaped by gap-junction-mediated electrical coupling within the bipolar cell network of the mouse retina. Specifically, electrical synapses spread signals laterally between bipolar cells, and this lateral spread contributed to a nonlinear enhancement of bipolar cell output to visual stimuli presented closely in space and time. Our findings thus (1) highlight how electrical and chemical transmission can work in concert to influence network output and (2) reveal a previously unappreciated circuit mechanism that increases RGC sensitivity to spatiotemporally correlated input, such as that produced by motion.

SUBMITTER: Kuo SP

PROVIDER: S-EPMC4840068 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Nonlinear Spatiotemporal Integration by Electrical and Chemical Synapses in the Retina.

Kuo Sidney P SP Schwartz Gregory W GW Rieke Fred F

Neuron 20160407 2

Electrical and chemical synapses coexist in circuits throughout the CNS. Yet, it is not well understood how electrical and chemical synaptic transmission interact to determine the functional output of networks endowed with both types of synapse. We found that release of glutamate from bipolar cells onto retinal ganglion cells (RGCs) was strongly shaped by gap-junction-mediated electrical coupling within the bipolar cell network of the mouse retina. Specifically, electrical synapses spread signal ...[more]

PMID: 27068789

Similar Datasets

Project description:Multiscale modeling and simulations in neuroscience is gaining scientific attention due to its growing importance and unexplored capabilities. For instance, it can help to acquire better understanding of biological phenomena that have important features at multiple scales of time and space. This includes synaptic plasticity, memory formation and modulation, homeostasis. There are several ways to organize multiscale simulations depending on the scientific problem and the system to be modeled. One of the possibilities is to simulate different components of a multiscale system simultaneously and exchange data when required. The latter may become a challenging task for several reasons. First, the components of a multiscale system usually span different spatial and temporal scales, such that rigorous analysis of possible coupling solutions is required. Then, the components can be defined by different mathematical formalisms. For certain classes of problems a number of coupling mechanisms have been proposed and successfully used. However, a strict mathematical theory is missing in many cases. Recent work in the field has not so far investigated artifacts that may arise during coupled integration of different approximation methods. Moreover, in neuroscience, the coupling of widely used numerical fixed step size solvers may lead to unexpected inefficiency. In this paper we address the question of possible numerical artifacts that can arise during the integration of a coupled system. We develop an efficient strategy to couple the components comprising a multiscale test problem in neuroscience. We introduce an efficient coupling method based on the second-order backward differentiation formula (BDF2) numerical approximation. The method uses an adaptive step size integration with an error estimation proposed by Skelboe (2000). The method shows a significant advantage over conventional fixed step size solvers used in neuroscience for similar problems. We explore different coupling strategies that define the organization of computations between system components. We study the importance of an appropriate approximation of exchanged variables during the simulation. The analysis shows a substantial impact of these aspects on the solution accuracy in the application to our multiscale neuroscientific test problem. We believe that the ideas presented in the paper may essentially contribute to the development of a robust and efficient framework for multiscale brain modeling and simulations in neuroscience.

Project description:BackgroundA large number of invertebrate models, including the snail Helix, emerged as particularly suitable tools for investigating the formation of synapses and the specificity of neuronal connectivity. Helix neurons can be individually identified and isolated in cell culture, showing well-conserved size, position, biophysical properties, synaptic connections, and physiological functions. Although we previously showed the potential usefulness of Helix polysynaptic circuits, a full characterization of synaptic connectivity and its dynamics during network development has not been performed.ResultsIn this paper, we systematically investigated the in vitro formation of polysynaptic circuits, among Helix B2 and the serotonergic C1 neurons, from a morphological and functional point of view. Since these cells are generally silent in culture, networks were chemically stimulated with either high extracellular potassium concentrations or, alternatively, serotonin. Potassium induced a transient depolarization of all neurons. On the other hand, we found prolonged firing activity, selectively maintained following the first serotonin application. Statistical analysis revealed no significant changes in neuronal dynamics during network development. Moreover, we demonstrated that the cell-selective effect of serotonin was also responsible for short-lasting alterations in C1 excitability, without long-term rebounds.Estimation of the functional connections by means of cross-correlation analysis revealed that networks under elevated KCl concentrations exhibited strongly correlated signals with short latencies (about 5 ms), typical of electrically coupled cells. Conversely, neurons treated with serotonin were weakly connected with longer latencies (exceeding 20 ms) between the interacting neurons. Finally, we clearly demonstrated that these two types of correlations (in terms of strength/latency) were effectively related to the presence of electrical or chemical connections, by comparing Micro-Electrode Array (MEA) signal traces with intracellularly recorded cell pairs.ConclusionsNetworks treated with either potassium or serotonin were predominantly interconnected through electrical or chemical connections, respectively. Furthermore, B2 response and short-term increase in C1 excitability induced by serotonin is sufficient to trigger spontaneous activity with chemical connections, an important requisite for long-term maintenance of firing activity.

Dataset Information

Nonlinear Spatiotemporal Integration by Electrical and Chemical Synapses in the Retina.

Publications

Nonlinear Spatiotemporal Integration by Electrical and Chemical Synapses in the Retina.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets