A simple model of retina-LGN transmission

Abstract

To gain a deeper understanding of the transmission of visual signals from retina through the lateral geniculate nucleus (LGN), we have used a simple leaky integrate and-fire model to simulate a relay cell in the LGN. The simplicity of the model was motivated by two questions: (1) Can an LGN model that is driven by a retinal spike train recorded as synaptic ('S') potentials, but does not include a diverse array of ion channels, nor feedback inputs from the cortex, brainstem, and thalamic reticular nucleus, accurately simulate the LGN discharge on a spike-for-spike basis? (2) Are any special synaptic mechanisms, beyond simple summation of currents, necessary to model experimental recordings? We recorded cat relay cell responses to spatially homogeneous small or large spots, with luminance that was rapidly modulated in a pseudo-random fashion. Model parameters for each cell were optimized with a Simplex algorithm using a short segment of the recording. The model was then tested on a much longer, distinct data set consisting of responses to numerous repetitions of the noisy stimulus. For LGN cells that spiked in response to a sufficiently large fraction of retinal inputs, we found that this simplified model accurately predicted the firing times of LGN discharges. This suggests that modulations of the efficacy of the retino-geniculate synapse by pre-synaptic facilitation or depression are not necessary in order to account for the LGN responses generated by our stimuli, and that post-synaptic summation is sufficient.

Sharpening of directional selectivity from neural output of rabbit retina

“…Scientific evidences suggest that direction selectivity is carried from the retina to higher brain areas. Here we adopt a simple integrate-andfire neuron model, inspired by recent work of Casti et al (2008), to investigate how directional selectivity changes in cells postsynaptic to directional selective retinal ganglion cells (DSRGC). Our model analysis shows that directional selectivity in the postsynaptic cells increases over a wide parameter range.…”

Recoding of Sensory Information across the Retinothalamic Synapse

“…Past work points to the mechanism of "paired-spike enhancement" (Usrey et al, 1998). This term describes the experimental observation that short interspike intervals in the presynaptic spike train increase the efficacy of synaptic transmission (Usrey et al, 1998), presumably as the result of temporal summation of retinogeniculate EPSPs (Carandini et al, 2007;Casti et al, 2008). The efficacy is defined as the fraction of presynaptic spikes that are relayed by the postsynaptic neuron.…”

“…To explore how a correlation code might be transformed to a single spike code, we built a simple model based on a mechanism that is important for relaying spikes across the synapse (Usrey et al, 1998;Carandini et al, 2007;Casti et al, 2008). The mechanism is paired-spike enhancement (as the interval between two inputs shortens, the probability of evoking a postsynaptic spike increases).…”

“…Furthermore, the joint encoding model allowed us to identify specific features that were encoded by pairwise correlations in the retinal spike train and then recoded into independent spikes in the thalamus. Finally, we built a mechanistic model [using time-dependent changes in the efficacy of retinal inputs (Usrey et al, 1998;Carandini et al, 2007;Casti et al, 2008)] that explained our experimental observations.…”

A minimal mechanistic model for temporal signal processing in the lateral geniculate nucleus

“…The results of the study is that the time constant of a rate model might in many situations be well approximated by the time constant of the synaptic connection. However, this finding should be tested further using a similar numerical approach one available spike-based models made particulary for the retinogeniculate transmission (Carandini et al 2007;Casti et al 2008). Rate-model time constants extracted from such a study would reduce the number of unknown parameters in our model and thus make the predictions better constrained.…”