Neural signaling employs many stochastic processes (transmitter release, receptor binding, channel opening) that might add noise and thus lose information. Loss has been assessed by measuring neural efficiency, the ratio of sensitivities for a human and an """"""""ideal observer"""""""", which is a computational model that includes the human preneural losses (blur, photon noise, etc.) but no neural loss. Neural efficiency has seemed to reach at most about 0.5, implying considerable loss (50%); however, neural efficiency might be much higher (about 1) and have been underestimated because test stimuli were ill-matched to the neural pathway that mediates the discrimination. To test these alternative hypotheses, AIM 1 will measure neural efficiency for """"""""brisk"""""""" ganglion cells that encode by precise spike timing and supply the geniculo-striate system. We record a cell's response to an optimal stimulus (i.e., matched to the spatiotemporal impulse response); then we compare the cell's sensitivity to that of an ideal observer subject only to preneural loss. Ganglion cell efficiency (the ratio of these measures) may well be >0.5. We will also record from pairs of adjacent brisk ganglion cells and use their combined responses to compute overall retinal efficiency, expecting values >0.50. Finally, we will test psychophysical sensitivity to similar stimuli, which might show that the brain and retina are equally efficient. Roughly half of all ganglion cells use a different coding strategy. They fire """"""""sluggishly"""""""" (fewer spikes, less precise timing) to signal complex features of the visual scene, such as local edges. We hypothesize that a """"""""sluggish code"""""""", although less effective than the """"""""brisk code"""""""" at transmitting high temporal frequencies, is more efficient with respect to metabolic energy and wire volume. To evaluate this hypothesis AIM 2 will compare the coding properties, energy budgets, and wire volumes for brisk-sustained and """"""""local-edge"""""""" cells, which have similar receptive field size and are the most numerous of the brisk and sluggish types. Finally, AIM 3 will investigate local circuits that may lend efficiency to both types of coding. Certain bipolar terminals release glutamate quanta in """"""""bursts"""""""" whose timing resembles the spike bursts in brisk ganglion cells. We hypothesize that the bursts are caused by inhibitory amacrine feedback onto the terminals of brisk but not sluggish bipolar cells. We will test this by identifying the bipolar types that contact brisk and sluggish cells (morphology and function) and reconstructing their local amacrine circuits. The proposed studies address fundamental mechanisms of retinal function critical to early stages of human vision.
| Borghuis, Bart G; Sterling, Peter; Smith, Robert G (2009) Loss of sensitivity in an analog neural circuit. J Neurosci 29:3045-58 |
| Borghuis, Bart G; Ratliff, Charles P; Smith, Robert G et al. (2008) Design of a neuronal array. J Neurosci 28:3178-89 |
| Xu, Ying; Vasudeva, Viren; Vardi, Noga et al. (2008) Different types of ganglion cell share a synaptic pattern. J Comp Neurol 507:1871-8 |
| Beaudoin, Deborah L; Borghuis, Bart G; Demb, Jonathan B (2007) Cellular basis for contrast gain control over the receptive field center of mammalian retinal ganglion cells. J Neurosci 27:2636-45 |
| Zaghloul, Kareem A; Manookin, Michael B; Borghuis, Bart G et al. (2007) Functional circuitry for peripheral suppression in Mammalian Y-type retinal ganglion cells. J Neurophysiol 97:4327-40 |
| Sterling, Peter; Freed, Michael (2007) How robust is a neural circuit? Vis Neurosci 24:563-71 |
| 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 |
| Zaghloul, Kareem A; Boahen, Kwabena; Demb, Jonathan B (2005) Contrast adaptation in subthreshold and spiking responses of mammalian Y-type retinal ganglion cells. J Neurosci 25:860-8 |
| Sterling, Peter; Matthews, Gary (2005) Structure and function of ribbon synapses. Trends Neurosci 28:20-9 |
| Choi, Sue-Yeon; Borghuis, Bart G; Borghuis, Bart et al. (2005) Encoding light intensity by the cone photoreceptor synapse. Neuron 48:555-62 |
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