Real-time computing without stable states: a new framework for neural computation based on perturbations
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...In fact, some popular RNN algorithms restricted credit assignment to a single step backwards (Elman, 1990; Jordan, 1986, 1997), also inmore recent studies (Jaeger, 2001, 2004; Maass et al., 2002)....
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...Early variants of the recurrent neural network included the echo-state network [219], which is also referred to as the liquid-state machine [304]....
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...Echo-state networks are also referred to as liquid-state machines [304], except that the latter uses spiking neurons with binary outputs, whereas echo-state networks use conventional activations like the sigmoid and the tanh functions....
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...Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks....
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...This information is processed by extremely complexbut surprisingly stereotypic microcircuits that can perform a wide spectrum of tasks (Shepherd, 1988; Douglas & Martin, 1998; von Melchner, Pallas, & Sur, 2000)....
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"Real-time computing without stable ..." refers background or methods in this paper
...…inevitable consequence of collapsing the high-dimensional space of liquid states into a single dimension, but what is surprising is that the equivalence classes are meaningful in terms of the task, allowing invariant and appropriately scaled readout responses and therefore real-time computation on…...
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...In the case of a synaptic connection from a to b, we modeled the synaptic dynamics according to the model proposed in Markram, Wang, and Tsodyks (1998), with the synaptic parameters U (use), D (time constant for depression), and F (time constant for facilitation) randomly chosen from gaussian…...
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