A single spike suffices: the simplest form of stochastic resonance in model neurons

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deep learning in neural networks: An overview (2014, neutral): “…1996, 1997; Maex & Orban, 1996; Nessler, Pfeiffer, Buesing, & Maass, 2013; Rezende & Gerstner, 2014; Seung, 2003; Song, Miller, & Abbott, 2000; Stemmler, 1996; Stoop, Schindler, & Bunimovich, 2000; Tsodyks, Pawelzik, & Markram, 1998; Tsodyks, Skaggs, Sejnowski, & McNaughton, 1996; Zipser,…”
What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology (2009, background): “Another often-used measure is Fisher information [57,99], which is useful when the goal is to estimate an input signal (or its parameters) from an observed output signal.”
Deep Learning With Spiking Neurons: Opportunities and Challenges (2018, background): “…in biology where even a single spike or small temporal variations of single neuron ﬁring may trigger diﬀerent reactions (Gerstner et al., 1996; Stemmler, 1996; Rieke, 1999; Machens et al., 2003), because often decisions have to be made before a reliable estimate of a spike rate can be…”
A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties (2006, methodology): “…of stochastic resonance in neural models, including the perfect integrator (Bulsara et al. 1994), the Fitzhugh– Nagomo model (Longtin 1993; Chialvo et al. 1997), a bi-stable neuron model (Longtin et al. 1994), and the leaky integrate-and-ﬁre neuron model (Bulsara et al. 1996; Stemmler 1996).”