Year: 2020
Type: article
Abstract: The lateral geniculate nucleus (LGN) is responsible for transmitting visual information from the optic nerve to the primary visual cortex.Located within the LGN is the triadic synapse, an unusual yet fundamental structure wherein a retinal ganglion cell simultaneously synapses onto a relay cell and an interneuron, with the same interneuron also providing inhibition to the relay cell.Despite the large body of physiological data available for each of these cell types individually, the triadic synapse's behaviour and function in information processing remains poorly understood.In this work, we create a biophysical model of the triadic synapse using Python with NEURON.Our model is based on specifications from literature and consists of retinal ganglion inputs, an interneuron and a relay cell synapsing in appropriate triad formation.Computational simulations through the model find that triadic inhibition alone causes faster neuronal repolarisation following excitation than axonal inhibition alone, granting temporal precision to visual signals.Importantly, we find that our triad model expresses temporal selectivity by boosting coincident retinal spikes to selectively pass significant visual events over network noise.This occurs as synchronous retinal inputs elicit a strong relay cell response, whilst asynchronous inputs produce overlapping excitation and inhibition, thus driving relay cells less effectively.This validates the feasibility of temporal selectivity as a core functional property of the synapse and compounds current computational research in investigating triadic circuitry behaviour.
Authors: Laura Lazzari, Patrick T. McCarthy, Jonathan Martin, Simon R. Schultz
Institution: Imperial College London
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4242336682