arXiv subjects

David Kappel

Publications and source records attributed to David Kappel.

31 records · Page 2Linked to original sources

arXiv2022

Decision Making by a Neuromorphic Network of Volatile Resistive Switching Memories

The necessity of having an electronic device working in relevant biological time scales with a small footprint boosted the research of a new class of emerging memories. Ag-based volatile resistive switching memories (RRAMs) feature a spontaneous change of device conductance with a similarity to biological mechanisms. They rely on the formation and self-disruption of a metallic conductive filament through an oxide layer, with a retention time ranging from a few milliseconds to several seconds, greatly tunable according to the maximum current which is flowing through the device. Here we prove a neuromorphic system based on volatile-RRAMs able to mimic the principles of biological decision-making behavior and tackle the Two-Alternative Forced Choice problem, where a subject is asked to make a choice between two possible alternatives not relying on a precise knowledge of the problem, rather on noisy perceptions.

cs.ET↗

arXiv2021

A synapse-centric account of the free energy principle

The free energy principle (FEP) is a mathematical framework that describes how biological systems self-organize and survive in their environment. This principle provides insights on multiple scales, from high-level behavioral and cognitive functions such as attention or foraging, down to the dynamics of specialized cortical microcircuits, suggesting that the FEP manifests on several levels of brain function. Here, we apply the FEP to one of the smallest functional units of the brain: single excitatory synaptic connections. By focusing on an experimentally well understood biological system we are able to derive learning rules from first principles while keeping assumptions minimal. This synapse-centric account of the FEP predicts that synapses interact with the soma of the post-synaptic neuron through stochastic synaptic releases to probe their behavior and use back-propagating action potentials as feedback to update the synaptic weights. The emergent learning rules are regulated triplet STDP rules that depend only on the timing of the pre- and post-synaptic spikes and the internal states of the synapse. The parameters of the learning rules are fully determined by the parameters of the post-synaptic neuron model, suggesting a close interplay between the synaptic and somatic compartment and making precise predictions about the synaptic dynamics. The synapse-level uncertainties automatically lead to representations of uncertainty on the network level that manifest in ambiguous situations. We show that the FEP learning rules can be applied to spiking neural networks for supervised and unsupervised learning and for a closed loop learning task where a behaving agent interacts with an environment.

q-bio.NC↗

arXiv2020

Adaptive Extreme Edge Computing for Wearable Devices

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions towards smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g. memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

cs.ET↗

arXiv2020

Structural plasticity on an accelerated analog neuromorphic hardware system

In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations. Their neural connectivity and synaptic capacity depends on their specific design choices, but is always intrinsically limited. Here, we present a strategy to achieve structural plasticity that optimizes resource allocation under these constraints by constantly rewiring the pre- and gpostsynaptic partners while keeping the neuronal fan-in constant and the connectome sparse. In particular, we implemented this algorithm on the analog neuromorphic system BrainScaleS-2. It was executed on a custom embedded digital processor located on chip, accompanying the mixed-signal substrate of spiking neurons and synapse circuits. We evaluated our implementation in a simple supervised learning scenario, showing its ability to optimize the network topology with respect to the nature of its training data, as well as its overall computational efficiency.

q-bio.NC↗

arXiv2020

Embodied Synaptic Plasticity with Online Reinforcement learning

The endeavor to understand the brain involves multiple collaborating research fields. Classically, synaptic plasticity rules derived by theoretical neuroscientists are evaluated in isolation on pattern classification tasks. This contrasts with the biological brain which purpose is to control a body in closed-loop. This paper contributes to bringing the fields of computational neuroscience and robotics closer together by integrating open-source software components from these two fields. The resulting framework allows to evaluate the validity of biologically-plausibe plasticity models in closed-loop robotics environments. We demonstrate this framework to evaluate Synaptic Plasticity with Online REinforcement learning (SPORE), a reward-learning rule based on synaptic sampling, on two visuomotor tasks: reaching and lane following. We show that SPORE is capable of learning to perform policies within the course of simulated hours for both tasks. Provisional parameter explorations indicate that the learning rate and the temperature driving the stochastic processes that govern synaptic learning dynamics need to be regulated for performance improvements to be retained. We conclude by discussing the recent deep reinforcement learning techniques which would be beneficial to increase the functionality of SPORE on visuomotor tasks.

cs.NE↗

arXiv2019

Attention on Abstract Visual Reasoning

Attention mechanisms have been boosting the performance of deep learning models on a wide range of applications, ranging from speech understanding to program induction. However, despite experiments from psychology which suggest that attention plays an essential role in visual reasoning, the full potential of attention mechanisms has so far not been explored to solve abstract cognitive tasks on image data. In this work, we propose a hybrid network architecture, grounded on self-attention and relational reasoning. We call this new model Attention Relation Network (ARNe). ARNe combines features from the recently introduced Transformer and the Wild Relation Network (WReN). We test ARNe on the Procedurally Generated Matrices (PGMs) datasets for abstract visual reasoning. ARNe excels the WReN model on this task by 11.28 ppt. Relational concepts between objects are efficiently learned demanding only 35% of the training samples to surpass reported accuracy of the base line model. Our proposed hybrid model, represents an alternative on learning abstract relations using self-attention and demonstrates that the Transformer network is also well suited for abstract visual reasoning.

cs.LG↗

arXiv2019

Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype

Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon substrate, since brain-inspired algorithms often make extensive use of complex functions such as random number generators, that are expensive to compute on standard general purpose hardware. The prototype chip of the 2nd generation SpiNNaker system is designed to overcome this problem. Low-power ARM processors equipped with a random number generator and an exponential function accelerator enable the efficient execution of brain-inspired algorithms. We implement the recently introduced reward-based synaptic sampling model that employs structural plasticity to learn a function or task. The numerical simulation of the model requires to update the synapse variables in each time step including an explorative random term. To the best of our knowledge, this is the most complex synapse model implemented so far on the SpiNNaker system. By making efficient use of the hardware accelerators and numerical optimizations the computation time of one plasticity update is reduced by a factor of 2. This, combined with fitting the model into to the local SRAM, leads to 62% energy reduction compared to the case without accelerators and the use of external DRAM. The model implementation is integrated into the SpiNNaker software framework allowing for scalability onto larger systems. The hardware-software system presented in this work paves the way for power-efficient mobile and biomedical applications with biologically plausible brain-inspired algorithms.

cs.NE↗

arXiv2018

Deep Rewiring: Training very sparse deep networks

Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them. But also generic hardware and software implementations of deep learning run more efficiently for sparse networks. Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints. We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network. DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded. We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance. DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior.

cs.NE↗

arXiv2018

CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling

Synaptic plasticity is implemented and controlled through over thousand different types of molecules in the postsynaptic density and presynaptic boutons that assume a staggering array of different states through phosporylation and other mechanisms. One of the most prominent molecule in the postsynaptic density is CaMKII, that is described in molecular biology as a "memory molecule" that can integrate through auto-phosporylation Ca-influx signals on a relatively large time scale of dozens of seconds. The functional impact of this memory mechanism is largely unknown. We show that the experimental data on the specific role of CaMKII activation in dopamine-gated spine consolidation suggest a general functional role in speeding up reward-guided search for network configurations that maximize reward expectation. Our theoretical analysis shows that stochastic search could in principle even attain optimal network configurations by emulating one of the most well-known nonlinear optimization methods, simulated annealing. But this optimization is usually impeded by slowness of stochastic search at a given temperature. We propose that CaMKII contributes a momentum term that substantially speeds up this search. In particular, it allows the network to overcome saddle points of the fitness function. The resulting improved stochastic policy search can be understood on a more abstract level as Hamiltonian sampling, which is known to be one of the most efficient stochastic search methods.

cs.NE↗

arXiv2018

A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning

Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity. These data are inconsistent with common models for network plasticity, and raise the questions how neural circuits can maintain a stable computational function in spite of these continuously ongoing processes, and what functional uses these ongoing processes might have. Here, we present a rigorous theoretical framework for these seemingly stochastic spine dynamics and rewiring processes in the context of reward-based learning tasks. We show that spontaneous synapse-autonomous processes, in combination with reward signals such as dopamine, can explain the capability of networks of neurons in the brain to configure themselves for specific computational tasks, and to compensate automatically for later changes in the network or task. Furthermore we show theoretically and through computer simulations that stable computational performance is compatible with continuously ongoing synapse-autonomous changes. After reaching good computational performance it causes primarily a slow drift of network architecture and dynamics in task-irrelevant dimensions, as observed for neural activity in motor cortex and other areas. On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as continuous sampling of network configurations.

q-bio.NC↗

arXiv2017

Laboratory simulations of the Vis-NIR spectra of comet 67P using sub-μm sized cosmochemical analogues

Laboratory spectral measurements of relevant analogue materials were performed in the framework of the Rosetta mission in order to explain the surface spectral properties of comet 67P. Fine powders of coal, iron sulphides, silicates and their mixtures were prepared and their spectra measured in the Vis-IR range. These spectra are compared to a reference spectrum of 67P nucleus obtained with the VIRTIS/Rosetta instrument up to 2.7 μm, excluding the organics band centred at 3.2 μm. The species used are known to be chemical analogues for cometary materials which could be present at the surface of 67P. Grain sizes of the powders range from tens of nanometres to hundreds of micrometres. Some of the mixtures studied here actually reach the very low reflectance level observed by VIRTIS on 67P. The best match is provided by a mixture of sub-micron coal, pyrrhotite, and silicates. Grain sizes are in agreement with the sizes of the dust particles detected by the GIADA, MIDAS and COSIMA instruments on board Rosetta. The coal used in the experiment is responsible for the spectral slope in the visible and infrared ranges. Pyrrhotite, which is strongly absorbing, is responsible for the low albedo observed in the NIR. The darkest components dominate the spectra, especially within intimate mixtures. Depending on sample preparation, pyrrhotite can coat the coal and silicate aggregates. Such coating effects can affect the spectra as much as particle size. In contrast, silicates seem to play a minor role.

astro-ph.EP↗

arXiv2016

The global surface composition of 67P/CG nucleus by Rosetta/VIRTIS. I) Prelanding mission phase

From August to November 2014 the Rosetta orbiter has performed an extensive observation campaign aimed at the characterization of 67P/CG nucleus properties and to the selection of the Philae landing site. The campaign led to the production of a global map of the illuminated portion of 67P/CG nucleus. During this prelanding phase the comet's heliocentric distance decreased from 3.62 to 2.93 AU while Rosetta was orbiting around the nucleus at distances between 100 to 10 km. VIRTIS-M, the Visible and InfraRed Thermal Imaging Spectrometer - Mapping channel (Coradini et al. 2007) onboard the orbiter, has acquired 0.25-5.1 micron hyperspectral data of the entire illuminated surface, e.g. the north hemisphere and the equatorial regions, with spatial resolution between 2.5 and 25 m/pixel. I/F spectra have been corrected for thermal emission removal in the 3.5-5.1 micron range and for surface's photometric response. The resulting reflectance spectra have been used to compute several Cometary Spectral Indicators (CSI): single scattering albedo at 0.55 micron, 0.5-0.8 micron and 1.0-2.5 micron spectral slopes, 3.2 micron organic material and 2.0 micron water ice band parameters (center, depth) with the aim to map their spatial distribution on the surface and to study their temporal variability as the nucleus moved towards the Sun. Indeed, throughout the investigated period, the nucleus surface shows a significant increase of the single scattering albedo along with a decrease of the 0.5-0.8 and 1.0-2.5 micron spectral slopes, indicating a flattening of the reflectance. We attribute the origin of this effect to the partial removal of the dust layer caused by the increased contribution of water sublimation to the gaseous activity as comet crossed the frost-line.

astro-ph.EP↗

arXiv2015

Network Plasticity as Bayesian Inference

General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference. But a model for that has been missing. We propose that inherently stochastic features of synaptic plasticity and spine motility enable cortical networks of neurons to carry out probabilistic inference by sampling from a posterior distribution of network configurations. This model provides a viable alternative to existing models that propose convergence of parameters to maximum likelihood values. It explains how priors on weight distributions and connection probabilities can be merged optimally with learned experience, how cortical networks can generalize learned information so well to novel experiences, and how they can compensate continuously for unforeseen disturbances of the network. The resulting new theory of network plasticity explains from a functional perspective a number of experimental data on stochastic aspects of synaptic plasticity that previously appeared to be quite puzzling.

cs.NE↗