Information transmission of mean and variance coding in integrate-and-fire neurons

Herfurth, Tim; Tchumatchenko, Tatjana

doi:10.1103/physreve.99.032420

Cited by 1 publication

(1 citation statement)

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“…In our study, neurons operate in the noise-driven regime that is typical for cortical neurons in vivo. Therefore, as long as the parameters stay within this biologically plausible regime, our prior work (Herfurth & Tchumatchenko, 2019; and the work of others (Brunel, Chance, Fourcaud, & Abbott, 2001;Gerstner & Kistler, 2002) suggests that one can expect similar results because the shape of the frequency response function is largely preserved across a broad range of membrane time constants, firing rates, and noise levels. To facilitate the exploration of other spiking models or parameter regimes in combination with optogenetic stimulation, we release our program code, so that our readers can consider the effects of any other custom neuron model on the recurrent network activity and, if necessary, adapt the parameters to any particular value of interest (on instructions see the file CUSTOM_MODELS.txt from the code linked in the Methods section).…”

Section: Discussionsupporting

confidence: 59%

Nonlinear response characteristics of neural networks and single neurons undergoing optogenetic excitation

Luboeinski

Tchumatchenko

2020

Network Neuroscience

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Optogenetic stimulation has become the method of choice for investigating neural computation in populations of neurons. Optogenetic experiments often aim to elicit a network response by stimulating specific groups of neurons. However, this is complicated by the fact that optogenetic stimulation is nonlinear, more light does not always equal to more spikes, and neurons that are not directly but indirectly stimulated could have a major impact on how networks respond to optogenetic stimulation. To clarify how optogenetic excitation of some neurons alters the network dynamics, we studied the temporal and spatial response of individual neurons and recurrent neural networks. In individual neurons, we find that neurons show a monotonic, saturating rate response to increasing light intensity and a non-monotonic rate response to increasing pulse frequency. At the network level, we find that Gaussian light beams elicit spatial firing rate responses that are substantially broader than the stimulus profile. In summary, our analysis and our network simulation code allow to predict the outcome of an optogenetic experiment and to assess whether the observed effects can be attributed to direct or indirect stimulation of neurons.

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Section: Discussionsupporting

confidence: 59%