My long term goal is to discover how neural circuits solve problems of signal processing. The key problems are that optical images are noisy (because natural scenes have low contrast) and that retinal circuits are also noisy (because they employ Poisson processes with relatively small numbers: few transmitter quanta, few channels). General strategies to improve and protect signal/noise ratio (SNR) are known, such as signal averaging, bandwidth compression, and gain control. But how these are implemented in specific neural circuits is not established. I propose to study circuits to the beta (x) ganglion cell in cat and the midget (P) ganglion cell in monkey. These cell types are critical to fine spatial vision, contributing respectively 50% and 90% of axons in the optic nerve. The """"""""schematic wiring diagrams"""""""" for these circuits are virtually complete, including numbers of converging rods, cones, and bipolar cells, number Of synapses at each stage, sites of electrical coupling, sites of lateral connectivity, identity of neural transmitters and postsynaptic receptors. The responses of most individual neuron types are known, including for some the signal and noise amplitudes. Finally, computational models (compartmental) of several components of the overall circuits have been established (cone-horizontal cell, bipolar-ganglion cell) and """"""""tuned"""""""" to reproduce the known responses such as receptive field extent and amplitude. These are large-scale models (up to 50,000 compartments) governed by an established simulator (NeuronC). I propose to use the existing models, extending them where necessary, and simulate different levels of photon, synaptic, and channel noise. I will evaluate the respective contributions of these noise sources for each stage in the circuits and the effects of specific circuit features in improving/maintaining SNR. Specifically, I will: l) compare the SNR at the beta cell from rod signals transmitted via rod bipolar in high photon noise (starlight) to that from rod signals transmitted by coupling to the cone bipolar circuit in moderate photon noise (twilight). 2) compare the SNR at bipolar cell input stage when controlled by feedforward or/and feedback inhibition. 3) compare SNRs of bipolar cell output stage when controlled by inhibitory amacrine synapses where the bipolar and amacrine inputs to ganglion cell are uncorrelated or correlated (as at """"""""dyad"""""""" synapse). 4) evaluate noise in ganglion cell contributed by each circuit component, including the spike generator. 5) perform """"""""sensitivity analysis"""""""" to assess which parameters in the circuit have greatest affect on SNR at the retinal output and the costs of improving SNR in terms of speed, reliability, and retinal thickness. The project will help understand how neural circuits contribute to spatial acuity in human vision.
| Xu, Ying; Dhingra, Narender K; Smith, Robert G et al. (2005) Sluggish and brisk ganglion cells detect contrast with similar sensitivity. J Neurophysiol 93:2388-95 |
| Vardi, N; Sterling, P (1994) Subcellular localization of GABAA receptor on bipolar cells in macaque and human retina. Vision Res 34:1235-46 |
| Vardi, N; Kaufman, D L; Sterling, P (1994) Horizontal cells in cat and monkey retina express different isoforms of glutamic acid decarboxylase. Vis Neurosci 11:135-42 |
| Vardi, N; Matesic, D F; Manning, D R et al. (1993) Identification of a G-protein in depolarizing rod bipolar cells. Vis Neurosci 10:473-8 |
| Smith, R G (1992) NeuronC: a computational language for investigating functional architecture of neural circuits. J Neurosci Methods 43:83-108 |
