Maass, W. and Zador, A. (1999). Computing and learning with dynamic synapses. In Maass, W. and Bishop, C. M., editors, Pulsed Neural Networks, chapter 6, pages 157--178. MIT Press, Boston, MA. ISBN 0-262-13350-4.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....retain activity over time, and produce non zero output only when activity increases beyond some specified threshold. Recent results show that such Spiking Networks [13] are formally more powerful than those using more traditional network units with continuous inputoutput transfer relationships [12], due to the possibility of spiking nodes encoding both amplitude and phase data. The activity, A n i , of node i at time step n is a function of the sum of its inputs and activity on the previous time step (0 A n i 1) as described by equations 6.1 and 6.2. The output, O n i , of a node is ....

Maass, W., Computing and learning with dynamic synapses, In: Maass, W. and Bishop, C.M. (eds), Pulsed Neural Networks, pp321-336, MIT Press, 1998.

....Fn . This lower bound can easily be derived from the lower bound from [Maass, 1997] for another boolean function CDn ( x; y) from f0; 1g 2n into f0; 1g which gives output 1 if and only if x i y i 2 for some i 2 f1; ng , since CDn ( x; y) Fn ( 1 Gamma x; y) The article [Maass and Zador, 1998a] surveys empirical data on the temporal dynamics of synapses and theoretical investigations of their possible computational role. One fundamental open problem for the theory of neural computation is the question how information is encoded in spike trains. There appears to be no unique answer. ....

Maass, W. and Zador, A. (1998a). Computing and learning with dynamic synapses. In Maass, W. and Bishop, C., editors, Pulsed Neural Networks. MIT-Press, Cambridge.

....at the Salk Institute for its hospitality. 1 for certain parameters #, # 0 that vary from synapse to synapse. A few other models have been proposed for synaptic dynamics (see e.g. Dobrunz and Stevens, 1997] Murthy et al. 1997] Tsodyks et al. 1998] Koch, 1999] Maass and Zador, 1998] [Maass and Zador, 1999]) that are all quite similar. Closely related models had already been proposed and investigated in [Grossberg, 1969] Grossberg, 1972] Grossberg, 1984] Francis et al. 1994] Our analysis in this article is primarily based on the model of [Varela et al. 1997] However we will prove that our ....

....in a biological interpretation. This formal symmetry between excitatory and inhibitory synapses is not adequate for most biological neural systems, for example the cortex of primates, where just 15 of the synapses are inhibitory. We would like to point out in this section that according to [Maass, 1999a] one can replace the dynamic networks considered in the preceding sections by an alternative type of network where just the dynamics of excitatory synapses matters which can be just depressing, just facilitating, or depressing and facilitating, like in the preceding sections. 14 The key ....

[Article contains additional citation context not shown here]

Maass, W. and Zador, A. (1999). Computing and learning with dynamic synapses. In Maass, W. and Bishop, C., editors, Pulsed Neural Networks. MIT-Press, Cambridge.

....i (t Gamma ) Delta e Gamma =fl d (2) for certain parameters ae; fl 0 that vary from synapse to synapse. A few other models have been proposed for synaptic dynamics (see e.g. Dobrunz and Stevens, 1997] Murthy et al. 1997] Tsodyks et al. 1998] Koch, 1999] Maass and Zador, 1998] [Maass and Zador, 1999]) that are all quite similar. Closely related models had already been proposed and investigated in [Grossberg, 1969] Grossberg, 1972] Grossberg, 1984] Francis et al. 1994] Our analysis in this article is primarily based on the model of [Varela et al. 1997] However we will prove that our ....

....in a biological interpretation. This formal symmetry between excitatory and inhibitory synapses is not adequate for most biological neural systems, for example the cortex of primates, where just 15 of the synapses are inhibitory. We would like to point out in this section that according to [Maass, 1999a] one can replace the dynamic networks considered in the preceding sections by an alternative type of network where just the dynamics of excitatory synapses matters which can be just depressing, just facilitating, or depressing and facilitating, like in the preceding sections. The key ....

[Article contains additional citation context not shown here]

Maass, W. and Zador, A. (1999). Computing and learning with dynamic synapses. In Maass, W. and Bishop, C., editors, Pulsed Neural Networks. MIT-Press, Cambridge.

....a separate row for each neuron. For comparison we have shaded an interval of 150 msec. This time span is known to suffice for the completion of some complex multilayer cortical computations. if x i y i 2 for some i 2 f1; ng , since CDn ( x; y) Fn ( 1 Gamma x; y) The article [Maass and Zador, 1998a] surveys empirical data on the temporal dynamics of synapses and theoretical investigations of their possible computational role. One fundamental open problem for the theory of neural computation is the question how information is encoded in spike trains. There appears to be no unique answer. ....

Maass, W. and Zador, A. (1998a). Computing and learning with dynamic synapses. In Maass, W. and Bishop, C., editors, Pulsed Neural Networks. MIT-Press, Cambridge.

No context found.

Maass, W. and Zador, A. (1999). Computing and learning with dynamic synapses. In Maass, W. and Bishop, C. M., editors, Pulsed Neural Networks, chapter 6, pages 157--178. MIT Press, Boston, MA. ISBN 0-262-13350-4.

No context found.

Maass, W. and Zador, A. (1999). Computing and learning with dynamic synapses. In Maass, W. and Bishop, C. M., editors, Pulsed Neural Networks, chapter 6, pages 157--178. MIT Press, Boston, MA. ISBN 0-262-13350-4.

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