Noise and Shunting

Ulrich, 2003; (noise)
Dynamic clamp in L5 pyr somatosensory cortex. Shunting multiplies the voltage, but not the spiking. He adds a GABA blocker and shows that it has an additive effect.

Chance, Abbott, Reyes 2002; (noise)
Dynamic clamp of rat somatosensory cortex slices L5 pyr. Random incoming excitatory and inhibitory synaptic inputs, increasing the "rates" of the inputs causes more shunting and more variance in the injected current. They inject driving current on top of the noisy current, more noise means the gain is decreased. Shunting inhibition without noise leads to shift in the firing-rate curve. They make a model, but its not a simulation -- they have an analytic description of firing rate from noise. The gain effect in the model goes away for high firing rates. Background excitation and inhibition must be balanced.

Mitchell & Silver, 2003; (noise)
Dynamic clamp of cerebellar granule cells. If synaptic excitation is frequency dependent then you get the gain effect, step currents give additive effect. Their IF model gets step shunting inhibition and synaptic excitation, and shows a gain and offset effect. The gain effect requires that the variance of the excitation increases with the level of excitation.

Capaday 2002; (inh feedback or neuromodulators)
Two compartment HH-like conductance model of motorneuron (dendrite and soma). He concludes that the mixture of excitatory and inhibitory conductances do not matter for gain, its not synaptic currents per se, but ligand action on receptors capable of activating intrinsic conductances. Inhibitory feedback can also be used to produce gain (circuit mechanism).

Gabbiani & Knopfel 1994;
Granule cell model -- multi-compartment, but "compact" (which effectively makes it a single compartment, I guess they just have a very low intra-cellular resistance). Shunting inhibition is additive and not multiplicative. This is just Holt & Koch before they described it.

Doiron et al. 2000; (noise)
Complex conductance model of pyramidal cell in electric fish and LIF model. Subthreshold shunting is divisive, in spiking regime additive. They get gain but only if there is stochasticity in the inhibition, and only for low firing rates. The gain is only there if the variance of the inhibition increases with the mean conductance.

Abbott & Chance 2005;
"(It is important to note that, despite comments in the literature to the contrary, divisive inhibition
of neuronal responses cannot arise from so called shunting inhibition. As has been shown both theoretically (Gabbiani et al., 1994; Holt and Koch, 1997) and experimentally (Chance et al., 2002),"
Review based on Chance 2002. They both can suggest that the circuit is doing a driving and balancing act to produce the gain, but require changes in the noise variance.

Salinas & Abbot 1996; (circuit)
Recurrent network for gain control.

Hahnloser et al, 2000; (circuit)
Analog circuit, feedback recurrent excitation and inhibition sets up a gain modulation. Can change the inhibition and alter the gain.

Ayaz & Chance 2009; (noise, circuit)
Normalization pool and modulatory pool, normalization pool driven by the stimuli within RF, modulatory pool driven by stimuli in surround. Inhibtion increases proportionally to the sum of both pools. When the pools increase noisy synaptic input you get a gain effect.

Prescott & De Koninck 2003; (noise, dendritic saturation)
Similar to Chance 2002, need noise but also dendritic saturation apparently helps. Multi-compartmental model of L5 pyramidal cell. Many of the other papers talked about how the gain effect goes away for higher inputs, the saturation prevents that.

Burkitt, Meffin, Graydin 2003; (noise, circuit)
LIF model. Two regimes: linear - excitation and extra leak from background noise cancel out, non-linear - leakiness dominates resulting in diminished gain.