Learning by stimulation avoidance scales to large neural networks

TL;DR: This work defines the conditions necessary in a network with spike-timing dependent plasticity for the organism to go from reactive to proactive behavior and proposes an evolutionary path for neural networks, leading an organism from reactive behavior to simple proactive behavior.

TL;DR: It is demonstrated that spiking neural networks with random structure spontaneously learn to predict temporal sequences of stimuli based solely on STDP.

TL;DR: In this paper, the authors showed that if the agent cannot learn an action to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes, as if to ignore the uncontrollable input.

TL;DR: In this paper, the authors demonstrate that spiking neural networks with random structure spontaneously learn to predict temporal sequences of stimuli based solely on spike-timing dependent plasticity (STDP).

TL;DR: A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons and combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons.

TL;DR: The biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons are discussed and their applicability to large-scale simulations of cortical neural networks is compared.

TL;DR: It is proposed that working memory is sustained by calcium-mediated synaptic facilitation in the recurrent connections of neocortical networks by using the presynaptic residual calcium as a buffer that is loaded, refreshed, and read out by spiking activity.