TL;DR: The entire SNN agent fits within a single Loihi 2 neuromorphic core, avoiding the inter-core routing and synchronization overhead that is often the real bottleneck on neuromorphic chips, and suggesting that neuromorphic hardware can host competitive, stable RTS agents.
Spiking neural networks (SNNs) have demonstrated competence in board games, but their application to real-time strategy (RTS) games—which demand simultaneous multi-unit control, resource management, and long-horizon planning—remains unexplored. We present the first SNN agent capable of playing a complete RTS game. Our agent uses 2,494 leaky integrate-and-fire (LIF) neurons organized in a hierarchical architecture with a recurrent strategic core, trained via proximal policy optimization (PPO) with a three-stage curriculum. In a controlled comparison with parameter-matched multilayer perceptron (MLP) and gated recurrent unit (GRU) baselines across 5 random seeds, the unmodified SNN achieves the highest peak tournament performance but collapses in all 5 seeds during extended training, while a GRU baseline achieves perfect stability (\(100\%\) win rate, 0/5 collapses). A stabilized variant combining cosine learning rate decay, an entropy floor, and checkpoint reversion eliminates collapse entirely (0/5 seeds) and dominates a 12-agent tournament (\(89.5 \pm 8.8\%\) win rate, rank 1). A per-mechanism ablation identifies checkpoint reversion as the critical stabilizer. A timestep ablation shows that 8 internal steps match 16-step peak performance with reduced collapse (2/5 vs. 5/5), ruling out a pure compute-volume confound. The entire SNN agent fits within a single Loihi 2 neuromorphic core (2,494 of 8,192 neurons per core), avoiding the inter-core routing and synchronization overhead that is often the real bottleneck on neuromorphic chips, and suggesting that neuromorphic hardware can host competitive, stable RTS agents.
TL;DR: A sleep-inspired, replay-driven memory consolidation-based temporal learning framework that reconstructs context from replayed memory during brief sleep periods for temporal context learning in resource-constrained edge systems is proposed.
In edge computing applications, storing long temporal sequences is memory and energy intensive, while sensory data arrives sequentially, requiring online learning. Although online updates reduce storage requirements, they lack access to broader temporal context needed for accurat…
TL;DR: A compact 2D convolutional neural network framework based on wavelet synchrosqueezing transform (WSST) time–frequency (TF) representations for EEG-based dementia classification demonstrates that the WSST-based representation provides an effective and robust input for CNN-based dementia classification under strict subject-disjoint validation.
Electroencephalography (EEG)-based discrimination of Alzheimer’s disease (AD), frontotemporal dementia (FTD), and cognitively normal controls (CN) remains challenging under clinically realistic subject-disjoint evaluation. This study presents a compact 2D convolutional neural net…
TL;DR: This work adopts a biologically plausible saliency-informed dropout technique as an explainable alternative to the unstructured standard random dropout approach and presents empirical results on regimes where such structured dynamic and static sparsities interact optimally to prune a ResNet model on the Imagenette dataset.
Biological vision has evolved to make efficient use of the limited information processing capability and tight energy budget of the brain by preferentially processing the most salient features of visual scenes. In contrast, modern deep vision models rely on expansive, high-dimens…
TL;DR: Shem is presented, a framework that enables gradient-based optimization of system-level objectives such as robustness and accuracy on user-defined analog compute models and introduces an autoparallelization algorithm that reduces total optimization runtime by 49–91%.
Analog compute paradigms are gaining attention for their potential to overcome the energy and latency limits of digital systems. Analog computations are modeled as dynamical systems for design and optimization, but this process is challenging: the dynamics are governed by nonline…
TL;DR: This work proposes heterogeneous neural networks that combine spiking neural networks (SNNs) and artificial neural networks (ANNs) at bandwidth-limited regions, such as chip boundaries, where spike-based communication reduces data transfer overhead.
Efficient communication is central to both biological and artificial intelligence (AI) systems. In biological brains, the challenge of long-range communication across regions is addressed through sparse, spike-based signaling, minimizing energy and latency. Conversely, modern AI…
TL;DR: The research rigorously investigates how neuromorphic architectures encode and integrate temporal information by conducting a comprehensive ablation study using a hybrid network, and demonstrates that shallow neuromorphic integration effectively maximizes the gains from temporal integration while mitigating the information loss inherent in binary spike quantization.
While neuromorphic systems offer a promising path for processing dynamic, event-based data, current benchmarks often fail to isolate the specific impact of temporal integration on model performance. To address this, our research rigorously investigates how neuromorphic architectu…