In this study, we measured the firing rate response of pyramidal cells in young mice cortex and we found quite some differences in individual responses. We thus needed a versatile procedure to capture the individual responses into an analytical description.
[[to be continued ...]]
yann's research webpage
In this blog, I share some material about my research. Research papers mostly focus on results and their interpretations. Here, I rather emphasize the methodological aspects of the studies and I provide the tools (in particular, the code) for their re-use. Do not hesitate to contact me or to comment on this blog for more details !
Friday, 24 February 2017
Wednesday, 22 February 2017
Accounting for state-dependency in an early sensory system (Reig et al., 2015)
In this study, in collaboration with an experimental team in Barcelona, we designed a simple theoretical framework to account for how the dependency on network state gives rise to a non-trivial relationship between the stimulus intensity and the response amplitude (here measured as evoked post-synaptic deflections) along the early auditory system (up to the primary auditory cortex).
Basically, what the modeling part brings is 1) to account for the state dependency of the recruitment of spiking neurons at a given stimulus level (an analytical estimate for the effect numerically evidenced in a previous paper from Alain), 2) give an analytical estimate for the state-dependency of postsynaptic deflections (the input conductance effect) and 3) apply this recruitment process to a network and a chain of networks.
An Ipython notebook with the code generating the figures can be found on the following link: http://nbviewer.jupyter.org/github/yzerlaut/notebook_papers/blob/master/reig_et_al_2015.ipynb.
Basically, what the modeling part brings is 1) to account for the state dependency of the recruitment of spiking neurons at a given stimulus level (an analytical estimate for the effect numerically evidenced in a previous paper from Alain), 2) give an analytical estimate for the state-dependency of postsynaptic deflections (the input conductance effect) and 3) apply this recruitment process to a network and a chain of networks.
An Ipython notebook with the code generating the figures can be found on the following link: http://nbviewer.jupyter.org/github/yzerlaut/notebook_papers/blob/master/reig_et_al_2015.ipynb.
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