Lmst

#NeuronPopulations

I recently played around with #RateModels using #NESTsimulator. Compared to #SNN, RM focus on average firing rates of #NeuronPopulations, simplifying analysis of large networks. They effectively capture collective dynamics like #oscillations and #synchronization, though they miss precise spike timing details. Thus, both approaches have their merits. Here is a brief overview:

🌍 https://www.fabriziomusacchio.com/blog/2025-08-28-rate_models/

#CompNeuro #Neuroscience #Python #PythonTutorial #SpikingNeuralNetwork

Simulated population activity of the excitatory population using mesoscopic and microscopic simulations. The top panel shows the mesoscopic activity from the rate model:
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(blue) computed from spikerecorder data as a binned histogram (discrete, noisier) and
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(orange) from multimeter data as a continuous measure (smoother).
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is inherently noisier and strongly dependent on bin size, compared to
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, which averages activity continuously over the recording interval and therefore appears smoother. This is due to the fact that spikerecorder-based histograms capture discrete spike counts, while the multimeter integrates population firing as a continuous variable. The bottom panel shows in contrast to the rate model’s results the microscopic activity
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derived from simulated spiking GIF neurons. Mesoscopic and microscopic traces are not identical, since one averages firing rates and the other emerges from explicit spikes, but both capture the population’s strong activation after 1500 ms. Rate models thus offer efficient and smooth approximations, while spiking models preserve variability and spike-level detail.

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