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Nr. Titel Autor Jahr
1 Robustness of Biologically Grounded Neural Networks Against Image Perturbations Teichmann, Michael* et al. 2024
2 Robustness of biologically grounded neural networks against image perturbations Teichmann, Michael et al. 2022
3 Achieving stable recurrent excitation by homeostatic setpoints in Hebbian synaptic plasticity Teichmann, Michael et al. 2021
4 Inhibitory plasticity - one mechanism to shape representational and metabolic efficiency Larisch, René et al. 2021
5 Performance of biologically grounded models of the early visual system on standard object recognition tasks Teichmann, Michael et al. 2021
6 Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity Larisch, René* et al. 2021
7 Building a less artificial deep neural network with biologically grounded plasticities Teichmann, Michael et al. 2020
8 Performance of biologically grounded models of the early visual system on standard object recognition tasks Teichmann, Michael et al. 2019
9 Performance of biologically grounded models of the early visual system on standard object recognition tasks Teichmann, Michael et al. 2019
10 A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement Larisch, René* et al. 2018
11 A plastic multilayer network of the early visual system inspired by the neocortical circuit Teichmann, Michael 2018
12 Specific connectivity in a model of V1 and V2 combining synaptic, intrinsic, and structural plasticity Teichmann, Michael et al. 2018
13 Voltage-based STDP and inhibitory plasticity cooperate to improve stimulus coding in a model of V1 simple-cells Larisch, René et al. 2018
14 Learning Stable Recurrent Excitation in Simulated Biological Neural Networks Teichmann, Michael* et al. 2017
15 Biologically plausible Hebbian learning in deep neural networks: being more close to the nature than CNNs Teichmann, Michael* et al. 2016
16 Combination of voltage­-based STDP with symmetric iSTDP to learn V1 simple­-cells Larisch, René et al. 2016
17 Spatial Synaptic Growth and Removal for Learning Individual Receptive Field Structures Teichmann, Michael* et al. 2016
18 A computational model of the perisaccadic updating of spatial attention Teichmann, Michael et al. 2015
19 A computational model of the perisaccadic updating of spatial attention Schuster, Julia et al. 2015
20 A Recurrent Multilayer Model with Hebbian Learning and Intrinsic Plasticity Leads to Invariant Object Recognition and Biologically Plausible Receptive Fields Teichmann, Michael et al. 2015
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