20. Wu, Q.X., McGinnity, T.M., Maguire, L.P., Glackin, B., Belatreche, A.: Learning Mechanism in Networks of Spiking Neurons. Studies in Computational Intelligence, vol. 35, pp. 171.197. Springer, Heidelberg (2006) 21. Wu, Q.X., McGinnity, T.M., Maguire, L.P., Belatreche, A., Glackin, B.: Adaptive Co- Ordinate Transformation Based on Spike Timing-Dependent Plasticity Learning Paradigm. In: Wang, L., Chen, K.S., Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 420.428. Springer, Heidelberg (2005) [PDF] Correlation Between Uncoupled Conductance-Based Integrate-and-Fire ... => done [PS] Some generalizations of integrate-and-fire models Arnaud Tonnelier ... => done [PDF] Balanced inhibition underlies tuning and sharpens spike timing in ... => done Multiplicative Gain Changes Are Induced by Excitation or ...BK Murphy 著 - 2003 - 引用元 39 - 関連記事 - 全 9 バージョン We simulate a cortical neuron using two models: a conductance-based, integrate-and-fire model and a Hodgkin-Huxley-type model. Integrate-and-fire model. The integrate-and-fire model is described by the following equation: => done also pdf A phenomenological model of peripheral and central neural ... - [ このページを訳す ]PC Nelson 著 - 2004 - 引用元 30 - 関連記事 Implementing a conductance-based integrate-and-fire model neuron would allow us to include other realistic properties of cells in the auditory brainstem and midbrain. For example, some neurons may act as coincidence detectors; ... www.ncbi.nlm.nih.gov ? Journal List ? NIHPA Author Manuscripts => done also pdf Impact of Correlated Synaptic Input on Output Firing Rate and ...E Salinas 著 - 2000 - 引用元 177 - 関連記事 - 全 8 バージョン Responses of the conductance-based integrate-and-fire model to current injection. In these simulations, Equation 21 was used with Rm = 40 M Omega (gL = 25 nS). The applied current IAPP was varied, but no synaptic inputs were included, ... www.jneurosci.org/cgi/content/full/20/16/6193 => done also pdf -------------------------------------------------------------------------------------------------- "Based on spiking neuron model and axonal delay [10-14], a neuronal circuit is proposed to explain how a spiking neural network can detect moving objects in an image sequence." "Neuroscientists have found that ... the axonal delay causes a phase shift for a spike train [10-14]." 10. Lin, J.W., Faber, D.S.: Modulation of Synaptic Delay during Synaptic Plasticity. Trends Neurosci. 25(9), 44.55 (2002) => Not available but important 11. Pena, J.L., Kazuo, S., VSaberi, F.K., Konishi, M.: Cochlear and Neural Delays for Coincidence Detection in Owls. The Journal of Neuroscience 21(23), 9455.9459 (2001) => done 12. Senn, W., Schneider, M., Ruf, B.: Activity-Dependent Development of Axonal and Dendritic Delays, or, Why Synaptic Transmission Should Be Unreliable. Neural Computation 14, 583.619 (2002) => done 13. Carr, C.E., Konishi, M.: Axonal Delay Lines for Time Measurement in the Owl's Brainstem. Proceedings of the National Academy of Sciences of the United States of America 85(21), 8311.8315 (1988) => done 14. Crook, S.M., Ermentrout, G.B., Vanier, M.C., Bower, J.M.: The Role of Axonal Delay in the Synchronization of Networks of Coupled Cortical Oscillators. Journal of Computational Neuroscience 4(2), 157.6873 (1997) => Not available but important "Inspired by the axonal delay mechanism, a spiking neural network model is proposed to detect moving objects." -------------------------------------------------------------------------------------------------- "Results in [7] demonstrate that information for segmenting scenes by relative motion is represented as early as visual cortex V1." 7. Reppas, J.B., Niyogi, S., Dale, A.M., Sereno, M.I., Tootell, B.H. (1997) "Representation of Motion Boundaries in Retinotopic Human Visual Cortical Areas." Nature 388(6638), 175-186 => OK -------------------------------------------------------------------------------------------------- "The findings in [8] show how a population of ganglion cells selective for differential motion can rapidly flag moving objects, and even segregate multiple moving objects." 8. Olveczky, B.P., Baccus, S.A., Meister, M.: Segregation of Object and Background Motion in the Retina. Nature 423(6938), 401.408 (2003) => Not available (important) -------------------------------------------------------------------------------------------------- Therefore, the gray scale changes of pixels in the image are reflected in the output neuron layer, i.e. Neuron N(x’, y’) will fire if Neuron N1 or Neuron N2 fires. Therefore, the moving object corresponds to high firing-rate -------------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------------- The authors claimed "Simulation results show that the conductance-based integrate-and-fire model is very close to the Hodgkin and Huxley neuron model [16-21]." But Izhikevich wrote ...... [Inplement] "input(x y) is connected intermediate n1(x y) connected through "excitatory synapse without delay & inhibitory synapse with delay" and n2(zxy) is connected through excitatory with delay and inhibitory without delay" "both excitatory and inhibitory synapses are adjusted so that neuro N1 does not fire when g(x,y,t)=g(x y t-dt)" Let S_{xy}(t) represents spike train generated by Neuron N_{xy} in output layer. The firing rate for Neuron N_{xy} is calculated by the following expression. r_{xy}(t)=(1/T)\sum_t^{t+T} S_{xy}(t) Plotting r_{xy}(t) as a grey image, white areas indicate neuron groups with high firing rate. Drawing the outside boundaries of firing neuron groups, boundaries of moving objects are extracted. The edges of firing neuron groups are used to determine the boundaries of the moving objects.