David Rotermund
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Object recognition from sparse simulated phosphenes and curved segments Open
Cortical prostheses offer the potential for partial vision restoration in individuals with blindness by stimulating neurons to produce phosphenes. However, the low number of discrete phosphenes that can be simultaneously elicited in practi…
View article: Interleaving cortex-analog mixing improves deep non-negative matrix factorization networks
Interleaving cortex-analog mixing improves deep non-negative matrix factorization networks Open
Considering biological constraints in artificial neural networks has led to dramatic improvements in performance. Nevertheless, to date, the positivity of long-range signals in the cortex has not been shown to yield improvements. While Non…
Including local feature interactions in deep non-negative matrix factorization networks improves performance Open
The brain uses positive signals as a means of signaling. Forward interactions in the early visual cortex are also positive, realized by excitatory synapses. Only local interactions also include inhibition. Non-negative matrix factorization…
Minimizing the number of phosphenes required for object recognition under prosthetic vision Open
Cortical prostheses offer the potential for partial vision restoration in individuals with blindness by stimulating V1 neurons to produce phosphenes. However, the low number of phosphenes that can be elicited in practice makes encoding of …
Which fragments support object recognition best? Open
Visual inputs to the human brain are very rich, originating from about 130 million photoreceptors. Despite this high resolution, little information is actually necessary for recognizing objects. For example, just a few fragments of an obje…
View article: Competitive performance and superior noise robustness of a non-negative deep convolutional spiking network
Competitive performance and superior noise robustness of a non-negative deep convolutional spiking network Open
Networks of spiking neurons promise to combine energy efficiency with high performance. However, spiking models that match the performance of current state-of-the-art networks while requiring moderate computational resources are still lack…
View article: Accelerating Spike-by-Spike Neural Networks on FPGA With Hybrid Custom Floating-Point and Logarithmic Dot-Product Approximation
Accelerating Spike-by-Spike Neural Networks on FPGA With Hybrid Custom Floating-Point and Logarithmic Dot-Product Approximation Open
Spiking neural networks (SNNs) represent a promising alternative to conventional neural networks. In particular, the so-called Spike-by-Spike (SbS) neural networks provide exceptional noise robustness and reduced complexity. However, deep …
Open Hardware for neuro-prosthesis research: A study about a closed-loop multi-channel system for electrical surface stimulations and measurements Open
Recent progress in neuro-prosthetic technology gives rise to the hope that in the future blind people might regain some degree of visual perception. It was shown that electrically stimulating the brain can be used to produce simple visual …
Back-Propagation Learning in Deep Spike-By-Spike Networks Open
Artificial neural networks (ANNs) are important building blocks in technical applications. They rely on noiseless continuous signals in stark contrast to the discrete action potentials stochastically exchanged among the neurons in real bra…
Biologically plausible learning in a deep recurrent spiking network Open
Artificial deep convolutional networks (DCNs) meanwhile beat even human performance in challenging tasks. Recently DCNs were shown to also predict real neuronal responses. Their relevance for understanding the neuronal networks in the brai…
Back-propagation learning in deep Spike-By-Spike networks Open
Neural networks are important building blocks in technical applications. These artificial neural networks (ANNs) rely on noiseless continuous signals in stark contrast to the discrete action potentials stochastically exchanged among the ne…
Massively Parallel FPGA Hardware for Spike-By-Spike Networks Open
While inspired by the brain, currently successful artificial neural networks lack key features of the biological original. In particular, the deep convolutional networks (DCNs) neither use pulses as signals exchanged among neurons, nor do …
Attention Selectively Gates Afferent Signal Transmission to Area V4 Open
Selective attention allows focusing on only part of the incoming sensory information. Neurons in the extrastriate visual cortex reflect such selective processing when different stimuli are simultaneously present in their large receptive fi…
Open Hardware for neuro-prosthesis research: A study about a closed-loop multi-channel system for electrical surface stimulations and measurements Open
1 Abstract Recent progress in neuro-prosthetic technology gives rise to the hope that in the future blind people might regain some degree of visual perception. It was shown that electrically stimulating the brain can be used to produce sim…
Implications for a Wireless, External Device System to Study Electrocorticography Open
Implantable neuronal interfaces to the brain are an important keystone for future medical applications. However, entering this field of research is difficult since such an implant requires components from many different areas of technology…
Open Hardware: Towards a Fully-Wireless Sub-Cranial Neuro-Implant for Measuring Electrocorticography Signals Open
Implantable neuronal interfaces to the brain are an important keystone for future medical applications. However, entering this field of research is difficult since such an implant requires components from many different areas of technology…