Redigif (2024)

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Digital art gif. Thousands of shiny, red 3D hearts that look like hard candies fall down into a red background.
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Jun 13, 2019 · The perfect Redi Animated GIF for your conversation. Discover and Share the best GIFs on Tenor.
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Jul 4, 2023 · The perfect Реди Redi Redia Animated GIF for your conversation. Discover and Share the best GIFs on Tenor.
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Jun 19, 2023 · Type to search. Advanced channel search. flag English. Site language. flag Russian flag English flag Uzbek. Sign In.
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Views statistics. post #2032 of the channel @Redigif. ×. 10 minutes hours days. 19 Jun 2023 60 50 40 30 20 10 0. Download SVG. Download PNG. Download CSV ...
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Black: iGIF-free, red: iGIF-Na. Gray areas indicate one standard deviation across cells for the iGIF-Na model. (B) Passive membrane filter κm(t). Inset ...
Author Summary Over the last decades, a variety of simplified spiking models have been shown to achieve a surprisingly high performance in predicting the neuronal responses to in vitro somatic current injections. Because of the complex adaptive behavior featured by cortical neurons, this success is however restricted to limited stimulus ranges: model parameters optimized for a specific input regime are often inappropriate to describe the response to input currents with different statistical properties. In the present study, a new spiking neuron model is introduced that captures single-neuron computation over a wide range of input statistics and explains different aspects of the neuronal dynamics within a single framework. Our results indicate that complex forms of single neuron adaptation are mediated by the nonlinear dynamics of the firing threshold and that the input-output transformation performed by cortical pyramidal neurons can be intuitively understood in terms of an enhanced Generalized Linear Model in which both the input filter and the spike-history filter adapt to the input statistics.

Black: iGIF-free, red: iGIF-Na. Gray areas indicate one standard deviation across cells for the iGIF-Na model. (B) Passive membrane filter κm(t). Inset ...
Author Summary Over the last decades, a variety of simplified spiking models have been shown to achieve a surprisingly high performance in predicting the neuronal responses to in vitro somatic current injections. Because of the complex adaptive behavior featured by cortical neurons, this success is however restricted to limited stimulus ranges: model parameters optimized for a specific input regime are often inappropriate to describe the response to input currents with different statistical properties. In the present study, a new spiking neuron model is introduced that captures single-neuron computation over a wide range of input statistics and explains different aspects of the neuronal dynamics within a single framework. Our results indicate that complex forms of single neuron adaptation are mediated by the nonlinear dynamics of the firing threshold and that the input-output transformation performed by cortical pyramidal neurons can be intuitively understood in terms of an enhanced Generalized Linear Model in which both the input filter and the spike-history filter adapt to the input statistics.