Micah Richert
Affiliations: | Salk Institute for Biological Studies, La Jolla, CA, United States |
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"Micah Richert"Mean distance: 14.04 (cluster 29) | S | N | B | C | P |
Parents
Sign in to add mentor| Bart Krekelberg | grad student | Salk Institute | ||
| Thomas D. Albright | grad student | 2008 | UCSD | |
| (Classical and surround receptive field structure in cortical area MT as revealed by reverse correlation.) | ||||
| Jeffrey L. Krichmar | post-doc | UC Irvine | ||
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Publications
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| Beyeler M, Richert M, Dutt ND, et al. (2014) Efficient spiking neural network model of pattern motion selectivity in visual cortex. Neuroinformatics. 12: 435-54 |
| Richert M, Albright TD, Krekelberg B. (2013) The complex structure of receptive fields in the middle temporal area. Frontiers in Systems Neuroscience. 7: 2 |
| Richert M, Szatmary B, Izhikevich E. (2013) Self-tuning spike-timing dependent plasticity curves to simplify models and improve learning Bmc Neuroscience. 14 |
| Meier PM, Richert M, Nageswaran JM, et al. (2013) Temporally evolving surround suppression helps decoding in a spiking model of motion processing Bmc Neuroscience. 14 |
| Petre C, Richert M, Szatmary B, et al. (2013) Beyond inhibition: lateral modulation of plasticity of feedforward synapses in a spiking model of V1 Bmc Neuroscience. 14 |
| Richert M, Nageswaran JM, Sokol S, et al. (2013) A spiking model of superior colliculus for bottom-up saliency Bmc Neuroscience. 14 |
| Piekniewski F, Richert M, Fisher D, et al. (2013) Balanced excitation and inhibition in a spiking model of V1 Bmc Neuroscience. 14 |
| Izhikevich EM, Piekniewski F, Nageswaran J, et al. (2013) Spontaneous emergence of simple and complex receptive fields in a spiking model of V1 Bmc Neuroscience. 14 |
| Carlson KD, Richert M, Dutt N, et al. (2013) Biologically plausible models of homeostasis and STDP: Stability and learning in spiking neural networks Proceedings of the International Joint Conference On Neural Networks |
| Richert M, Nageswaran JM, Dutt N, et al. (2011) An efficient simulation environment for modeling large-scale cortical processing. Frontiers in Neuroinformatics. 5: 19 |