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Publication

Mapping kainate activation of inner neurons in the rat retina.

Lisa Nivison-Smith | Daniel Sun | Erica L Fletcher | Robert E Marc | Michael Kalloniatis

The Journal of comparative neurology | 2013

Kainate receptors mediate fast, excitatory synaptic transmission for a range of inner neurons in the mammalian retina. However, allocation of functional kainate receptors to known cell types and their sensitivity remains unresolved. Using the cation channel probe 1-amino-4-guanidobutane agmatine (AGB), we investigated kainate sensitivity of neurochemically identified cell populations within the structurally intact rat retina. Most inner retinal neuron populations responded to kainate in a concentration-dependent manner. OFF cone bipolar cells demonstrated the highest sensitivity of all inner neurons to kainate. Immunocytochemical localization of AGB and macromolecular markers confirmed that type 2 bipolar cells were part of this kainate-sensitive population. The majority of amacrine (ACs) and ganglion cells (GCs) showed kainate responses with different sensitivities between major neurochemical classes (γ-aminobutyric acid [GABA]/glycine ACs > glycine ACs > GABA ACs; glutamate [Glu]/weakly GABA GCs > Glu GCs). Conventional and displaced cholinergic ACs were highly responsive to kainate, whereas dopaminergic ACs do not appear to express functional kainate receptors. These findings further contribute to our understanding of neuronal networks in complex multicellular tissues.

Pubmed ID: 23348566 RIS Download

Research resources used in this publication

None found

Additional research tools detected in this publication

NEURON (RRID:SCR_005393)

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Associated grants

Agency: NEI NIH HHS, United States
Id: P30 EY014800
Agency: NEI NIH HHS, United States
Id: R01 EY002576
Agency: NEI NIH HHS, United States
Id: EY014800
Agency: NEI NIH HHS, United States
Id: EY02576

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NEURON (tool)

RRID:SCR_005393

NEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. NEURON has benefited from judicious revision and selective enhancement, guided by feedback from the growing number of neuroscientists who have used it to incorporate empirically-based modeling into their research strategies. NEURON's computational engine employs special algorithms that achieve high efficiency by exploiting the structure of the equations that describe neuronal properties. It has functions that are tailored for conveniently controlling simulations, and presenting the results of real neurophysiological problems graphically in ways that are quickly and intuitively grasped. Instead of forcing users to reformulate their conceptual models to fit the requirements of a general purpose simulator, NEURON is designed to let them deal directly with familiar neuroscience concepts. Consequently, users can think in terms of the biophysical properties of membrane and cytoplasm, the branched architecture of neurons, and the effects of synaptic communication between cells. * helps users focus on important biological issues rather than purely computational concerns * has a convenient user interface * has a user-extendable library of biophysical mechanisms * has many enhancements for efficient network modeling * offers customizable initialization and simulation flow control * is widely used in neuroscience research by experimentalists and theoreticians * is well-documented and actively supported * is free, open source, and runs on (almost) everything

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