arXiv subjects

Jannik Luboeinski

Publications and source records attributed to Jannik Luboeinski.

5 recordsLinked to original sources

arXiv2026

Road to scalability for efficient graph search on massively parallel neuromorphic hardware

Efficient computation of shortest paths in weighted graphs is a fundamental problem with many applications. Neuromorphic hardware platforms promise massively parallel, efficient computation, changing parallelism tradeoffs. In this work, we introduce NEURO-MAPP (Neuromorphic-based Min-Add Parallel Propagation), a distributed shortest path algorithm designed to use the local computation and network communication available in neuromorphic systems. We provide an optimized implementation of the algorithm on the SpiNNaker 2 platform and evaluate its performance on a selection of synthetic and real-world graphs. These results are compared to Dijkstra's algorithm on a modern CPU. We find that the NEURO-MAPP implementation scales favorably in terms of runtime for many graph types while consuming less energy per shortest-path query than the CPU implementation in almost all cases. These findings highlight the potential of neuromorphic hardware featuring sparse, spike-based communication as a scalable and energy-efficient platform for computation in graph search and related tasks.

cs.NE↗

arXiv2026

Multi-timescale synaptic plasticity on analog neuromorphic hardware

As numerical simulations grow in complexity, their demands on computing time and energy increase. Accelerators for numerical computation offer significant efficiency gains in many computationally-intensive scientific fields, but their use in simulating spiking neural networks in computational neuroscience is hindered by challenges, mainly in effective parallelism and efficient use of memory in the presence of sparse representations and sparse communication. The BrainScaleS architectures are neuromorphic substrates that can emulate spiking neural networks at accelerated timescales compared to real time, which offers an advantage for studying complex plasticity rules that require extended simulation runtimes. This work presents the implementation of a calcium-based plasticity rule that integrates calcium dynamics based on the synaptic tagging-and-capture hypothesis on the BrainScaleS-2 system. The implementation of the plasticity rule for a single synapse involves incorporating the calcium dynamics and the plasticity rule equations. The calcium dynamics are mapped to the analog circuits of BrainScaleS-2, while the plasticity rule equations are numerically solved on its embedded digital processors. The main hardware constraints include the speed of the processors and the use of integer arithmetic. By adjusting the timestep of the numerical solver and introducing stochastic rounding, we demonstrate that BrainScaleS-2 accurately emulates a single synapse following a calcium-based plasticity rule across four established stimulation protocols and validate our implementation against a software reference model.

q-bio.QM↗

arXiv2025

Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systems

Heterogeneity is a ubiquitous property of many biological systems and has profound implications for computation. While it is conceivable to optimize neuronal and synaptic heterogeneity for a specific task, such top-down optimization is biologically implausible, prone to catastrophic forgetting, and both data- and energy-intensive. In contrast, biological organisms, with remarkable capacity to perform numerous tasks with minimal metabolic cost, exhibit a heterogeneity that is inherent, stable during adulthood, and task-unspecific. Inspired by this intrinsic form of heterogeneity, we investigate the utility of variations in neuronal time constants for solving hundreds of distinct temporal tasks of varying complexity. Our results show that intrinsic heterogeneity significantly enhances performance and robustness in an implementation-independent manner, indicating its usefulness for both (rate-based) machine learning and (spike-coded) neuromorphic applications. Importantly, only skewed heterogeneity profiles-reminiscent of those found in biology-produce such performance gains. We further demonstrate that this computational advantage eliminates the need for large networks, allowing comparable performance with substantially lower operational, metabolic, and energetic costs, respectively in silico, in vivo, and on neuromorphic hardware. Finally, we discuss the implications of intrinsic (rather than task-induced) heterogeneity for the design of efficient artificial systems, particularly novel neuromorphic devices that exhibit similar device-to-device variability.

cs.LG↗

arXiv2025

Adding numbers with spiking neural circuits on neuromorphic hardware: A building block for future hybrid systems

Progress in neuromorphic computing requires efficient implementation of standard computational problems, like adding numbers. Here we implement a variety of sequential and parallel binary adders in the Lava software framework, and deploy them to the neuromorphic chip Loihi 2. To the best of our knowledge, up to now, a neuromorphic implementation of such parallel adders has not been reported. We describe the time complexity, neuron and synaptic resources, as well as constraints on the bit width of the numbers that can be added with the current implementations. Further, we measure the time required for the addition operation on-chip. Importantly, we encounter trade-offs in terms of time complexity and required chip resources for the three considered adders. While sequential adders have linear time complexity $\mathcal{O}(n)$ and require a linearly increasing number of neurons and synapses with number of bits $n$, the parallel adders have constant time complexity $\mathcal{O}(1)$ and also require a linearly increasing number of neurons, but nonlinearly increasing synaptic resources (scaling with $n^2$ or $n \sqrt{n}$). This trade-off between compute time and chip resources may inform decisions in application development, and the implementations we provide may serve as a building block for further progress towards efficient neuromorphic algorithms.

cs.NE↗

arXiv2025

Plastic Arbor: a modern simulation framework for synaptic plasticity -- from single synapses to networks of morphological neurons

Arbor is a software library designed for efficient simulation of large-scale networks of biological neurons with detailed morphological structures. It combines customizable neuronal and synaptic mechanisms with high-performance computing, supporting multi-core CPU and GPU systems. In humans and other animals, synaptic plasticity processes play a vital role in cognitive functions, including learning and memory. Recent studies have shown that intracellular molecular processes in dendrites significantly influence single-neuron dynamics. However, for understanding how the complex interplay between dendrites and synaptic processes influences network dynamics, computational modeling is required. To enable the modeling of large-scale networks of morphologically detailed neurons with diverse plasticity processes, we have extended the Arbor library to support simulations of a large variety of spike-driven plasticity paradigms. To showcase the features of the extended framework, we present examples of computational models, beginning with single-synapse dynamics, progressing to multi-synapse rules, and finally scaling up to large recurrent networks. While cross-validating our implementations by comparison with other simulators, we show that Arbor allows simulating plastic networks of multi-compartment neurons at nearly no additional cost in runtime compared to point-neuron simulations. In addition, we demonstrate that Arbor is highly efficient in terms of runtime and memory use as compared to other simulators. Using the extended framework, as an example, we investigate the impact of dendritic structures on network dynamics across a timescale of several hours, finding a relation between the length of dendritic trees and the ability of the network to efficiently store information. By our extension of Arbor, we aim to provide a valuable tool that will support future studies on the impact of synaptic plasticity, especially, in conjunction with neuronal morphology, in large networks.

cs.CE↗