Biology subjects

Rotermund, D.

Publications and source records attributed to Rotermund, D..

6 recordsLinked to original sources

bioRxiv2024

Minimizing the number of phosphenes required for object recognition under prosthetic vision

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 whole objects difficult, and the round shape of phosphenes lack the contour cues necessary for perceptual grouping. We propose a minimalistic encoding approach that focuses on essential visual information. We fragmented objects contours into either phosphenes or curved segments, providing either low or high local visual information. 46 participants identified these fragmented objects in a free-naming task. The number of fragments gradually increased to quantify the minimum number of phosphenes and segments necessary to recognize objects. Most objects could be recognized with only 65 phosphenes, which is in the range of implantable electrodes in human patients. Participants required 27% fewer segments than phosphenes to recognize objects. Including individual objects as a random effect in a linear mixed model substantially increased the explained variance, suggesting that the minimal number of fragments required for object recognition in prosthetic vision strongly depends on the particular object. Our results demonstrate that a minimalistic approach can substantially reduce the number of phosphenes required for recognition, emphasizing the importance of identifying critical object features to minimize brain stimulation in visual prostheses.

neuroscience↗

bioRxiv2023

Competitive performance and superior noise robustness of a non-negative deep convolutional spiking network

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 lacking. Here we present an alternative framework to deep convolutional networks (CNNs), the "Spike by Spike" network (SbS), together with an efficient backpropagation algorithm. SbS implements networks based on non-negative matrix factorisation (NNMF), but uses discrete events as signals instead of real values. On clean data, the performance of CNNs is matched by both NNMF-based networks and SbS. SbS are found to be most robust when the data is corrupted by noise, specially when this noise was not seen before.

neuroscience↗

bioRxiv2019

Biologically plausible learning in a deep recurrent spiking network

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 brain, however, remains questionable. In contrast to the unidirectional architecture of DCNs neurons in cortex are recurrently connected and exchange signals by short pulses, the action potentials. Furthermore, learning in the brain is based on local synaptic mechanisms, in stark contrast to the global optimization methods used in technical deep networks. What is missing is a similarly powerful approach with spiking neurons that employs local synaptic learning mechanisms for optimizing global network performance. Here, we present a framework consisting of mutually coupled local circuits of spiking neurons. The dynamics of the circuits is derived from first principles to optimally encode their respective inputs. From the same global objective function a local learning rule is derived that corresponds to spike-timing dependent plasticity of the excitatory inter-circuit synapses. For deep networks built from these circuits self-organization is based on the ensemble of inputs while for supervised learning the desired outputs are applied in parallel as additional inputs to output layers.\n\nGenerality of the approach is shown with Boolean functions and its functionality is demonstrated with an image classification task, where networks of spiking neurons approach the performance of their artificial cousins. Since the local circuits operate independently and in parallel, the novel framework not only meets a fundamental property of the brain but also allows for the construction of special hardware. We expect that this will in future enable investigations of very large network architectures far beyond current DCNs, including also large scale models of cortex where areas consisting of many local circuits form a complex cyclic network.

neuroscience↗

bioRxiv2019

Back-propagation learning in deep Spike-By-Spike networks

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 neurons in real brains. A promising approach towards bridging this gap are the Spike-by-Spike (SbS) networks which represent a compromise between non-spiking and spiking versions of generative models that perform inference on their inputs. What is still missing are algorithms for finding weight sets that would optimize the output performances of deep SbS networks with many layers.\n\nHere, a learning rule for hierarchically organized SbS networks is derived. The properties of this approach are investigated and its functionality demonstrated by simulations. In particular, a Deep Convolutional SbS network for classifying handwritten digits (MNIST) is presented. When applied together with an optimizer this learning method achieves a classification performance of roughly 99.3% on the MNIST test data. Thereby it approaches the benchmark results of ANNs without extensive parameter optimization. We envision that with this learning rule SBS networks will provide a new basis for research in neuroscience and for technical applications, especially when they become implemented on specialized computational hardware.

bioinformatics↗

bioRxiv2018

Massively Parallel FPGA Hardware for Spike-By-Spike Networks

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 they include recurrent connections which are both core properties of real neuronal networks. This not only puts to question the relevance of DCNs for explaining information processing in nervous systems but also limits their potential for modeling natural intelligence.\n\nSpike-By-Spike (SbS) networks are a promising new approach that combines the computational power of artificial networks with biological realism. Instead of separate neurons they consist of neuronal populations performing inference. Even though the underlying equations are rather simple implementations of such networks on currently available hardware are several orders of magnitude slower than for comparable non-spiking deep networks.\n\nHere, we develop and investigate a framework for SbS networks on chip. Thanks to the communication via spikes, already moderately sized deep networks based on the SbS approach allows a parallelization into thousands of simple and fully independent computational cores. We demonstrate the feasibility of our design on a Xilinx Virtex 6 FPGA while avoiding proprietary cores (except block memory) that cannot be realized on a custom-designed ASIC. We present memory access optimized circuits for updating the internal variables of the neurons based on incoming spikes as well as for learning the connections strength. The optimized computational circuits as well as the representation of variables fully exploit the non-negative properties of all data in the SbS approach. We compare the sizes of the arising circuits for floating and fixed point numbers. In addition we show how to minimize the number of components that are required for the computational cores by reusing their components for different functions.

bioinformatics↗

bioRxiv2017

Open Hardware For Neuro-Prosthesis Research: A Study About A Closed-Loop Multi-Channel System For Electrical Surface Stimulations And Measurements

1Recent 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 impressions of light blobs (phosphenes). However, this perception is very far away from natural sight. For developing the next generation of visual prostheses, real-time closed-loop stimulators which measure the actual neuronal activities and on this basis determine the required stimulation pattern. This leads to the challenge to design a system that can produce arbitrary stimulation-patterns with up to {+/-}70V and with up to 25mA while measuring neuronal signals with amplitudes in the order of mV. Furthermore, the interruption of the measurement by stimulation must be as short as possible and the system needs to scale to hundreds of electrodes. We discuss how such a system and especially its current pumps and input protection need to be designed and which problems arise. We condense our findings into an example design for which we provide all design files (boards, firmwares and software) as open-source. This is a first step in taking the existing open-source www.open-ephys.org recording system and converting it into a closed-loop experimental setup for neuro-prosthetic research.

neuroscience↗