References and models cited by this paper | References and models that cite this paper | |||
Barto AG, Sutton RS, Anderson CW (1983) Neuronlike elements that can solve difficult learning control problems IEEE Trans Systems Man Cybern 13:835-846 Cauwnberghs G (1993) A fast stochastic error-descent algorithm for supervised learning and optimization Advances in neural information processing stystems, Giles Col:Hanson SJ:Cowan JD, ed. pp.244 Chance FS, Abbott LF, Reyes AD (2002) Gain modulation from background synaptic input. Neuron 35:773-82 [PubMed] Chauvin Y, Rumelhart DE (1995) Back propagation: Theory, architectures, and applications, Chauvin Y:Rumelhart DE, ed. Compte A, Brunel N, Goldman-Rakic PS, Wang XJ (2000) Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb Cortex 10:910-23 [PubMed] Dayan P, Abbott L (2001) Neural encoding: Firing rates and spike statistics Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems Doiron B, Longtin A, Berman N, Maler L (2001) Subtractive and divisive inhibition: effect of voltage-dependent inhibitory conductances and noise. Neural Comput 13:227-48 [PubMed] Doya K, Sejnowski TJ (1995) A novel reinforcement model of birdsongvocalization learning Advances in neural information processing systems, Tesauro G:Touretzky D:Alspector J, ed. Hertz J, Krogh A, Palmer RG (1991) Introduction to the Theory of Neural Computation. Jabri M, Flower B (1992) Weight perturbation: an optimal architecture and learning technique for analog VLSI feedforward and recurrent multilayer networks. IEEE Trans Neural Netw 3:154-7 [Journal] [PubMed] Lukashin AV, Wilcox GL, Georgopoulos AP (1994) Overlapping neural networks for multiple motor engrams. Proc Natl Acad Sci U S A 91:8651-4 [PubMed] Mazzoni P, Andersen RA, Jordan MI (1991) A more biologically plausible learning rule for neural networks. Proc Natl Acad Sci U S A 88:4433-7 [PubMed] Miller KD (1996) Receptive fields and maps in the visual cortex: Models of ocular dominance and orientation columns Models of neural networks III, Donnay E:van_Hemmen J:Schulten K, ed. pp.55 Oreilly RC (1996) Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm Neural Comput 8:895-938 Poggio T (1990) A theory of how the brain might work. Cold Spring Harb Symp Quant Biol 55:899-910 [PubMed] Prescott SA, De Koninck Y (2003) Gain control of firing rate by shunting inhibition: roles of synaptic noise and dendritic saturation. Proc Natl Acad Sci U S A 100:2076-81 [Journal] [PubMed] Salinas E, Abbott LF (2000) Do simple cells in primary visual cortex form a tight frame? Neural Comput 12:313-35 [PubMed] Schultz W, Dayan P, Montague PR (1997) A neural substrate of prediction and reward. Science 275:1593-9 [PubMed] Seung HS (2003) Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40:1063-73 [PubMed] Seung HS, Lee DD, Reis BY, Tank DW (2000) Stability of the memory of eye position in a recurrent network of conductance-based model neurons. Neuron 26:259-71 [PubMed] Widrow B, Hoff M (1960) Adaptive switching circuits Western Electronic Show And Convention Reco 4:96-104 Widrow B, Stearns SD (1985) Adaptive signal processing Williams RJ (1992) Simple statistical gradient-following algorithms for connectionist reinforcement learning Mach Learn 8:229-256 Xie X, Seung HS (2004) Learning in neural networks by reinforcement of irregular spiking. Phys Rev E Stat Nonlin Soft Matter Phys 69:041909 [Journal] [PubMed] | Stroud JP, Porter MA, Hennequin G, Vogels TP (2018) Motor primitives in space and time via targeted gain modulation in cortical networks. Nat Neurosci 21:1774-1783 [Journal] [PubMed]
|