--Spiking Neural Networks (SNNs) are a promising, energy-efficient alternative to standard Artificial Neural Networks (ANNs) and are particularly well-suited to spatio-temporal tasks such as keyword spotting and video classification. However, SNNs have a much lower arithmetic intensity than ANNs and are therefore not well-matched to standard accelerators like GPUs and TPUs. Field Programmable Gate Arrays (FPGAs) are designed for such memory-bound workloads and here we develop a novel, fully-programmable RISC-V-based system-on-chip (FeNN-DMA), tailored to simulating SNNs on modern UltraScale+ FPGAs. We show that FeNN-DMA has comparable resource usage and energy requirements to state-of-the-art fixed-function SNN accelerators, yet it is capable of simulating much larger and more complex models. Using this functionality, we demonstrate state-of-the-art classification accuracy on the Spiking Heidelberg Digits and Neuromorphic MNIST tasks. RTIFICIAL Neural Networks (ANNs) have demonstrated super-human performance in areas ranging from image classification to language modelling. However, training current ANNs, and even simply performing inference with them, come at a high energy cost, meaning they face significant limitations in their practical adoption. The human brain provides a tantalising existence proof that a far more efficient form of neural network is possible, as it runs on only 20 W and is far more powerful and flexible than any current ANN. Some of these properties are encapsulated in a biologically-inspired type of ANN known as Spiking Neural Networks (SNNs), in which individual neurons are stateful, dynamical systems and communicate with each other using spatio-temporally sparse events known as spikes. The main energy savings in SNNs come from this event-based communication because, by removing the continuous exchange of activations, the costly matrix multiplication of weights and activations at the heart of ANN computation is replaced by simply adding the weights associated with spiking neurons. This is particularly effective when spikes are rare events.

Large - language models (LLMs) maintain coherence over a few thousand tokens but degrade sharply in multi - hundred - turn conversations. We present a hippocampus - inspired dual - memory architecture that separates dialogue context into (1) Compact Memory, a continuously updated one - sentence summary that preserves the global narrative, and (2) Vector Memory, an episodic store of chunk embeddings queried via cosine similarity. Integrated with an onull - the - shelf 6 B - parameter transformer, the system sustains > 300 - turn dialogues while keeping the prompt under 3.5 K tokens. On long - form QA and story - continuation benchmarks, Compact + Vector Memory elevates factual - recall accuracy from 41 % to 87 % and human - rated coherence from 2.7 to 4.3. Precision - recall analysis shows that, with 10 K indexed chunks, Vector Memory achieves P@5 0.80 and R@50 0.74, doubling the area under the PR curve relative to a summarisation - only baseline. Ablation experiments reveal that (i) semantic forgetting -- age - weighted pruning of low - salience chunks -- cuts retrieval latency by 34 % with < 2 pp reca ll loss, and (ii) a two - level summary - of - summaries eliminates cascade errors that otherwise emerge after 1,000 turns. By reconciling verbatim recall with semantic continuity, our architecture offers a practical path toward scalable, privacy - aware conversational AI capable of engaging in months - long dialogue without retraining the underlying model.

Drones and other autonomous robots require mobile and efficient solutions to real-life issues, from mundane package transportation to urgent search and rescue missions. Using machine learning and a vector-based navigation system inspired by insects, agents could navigate to key locations without relying on a GPS -- becoming truly autonomous. Robots could learn to navigate independently to wildfires based on environmental sensory cues, using information from cameras and other sensors. Since vectors are represented in a geocentric context, multiple agents could communicate locations with each other, which could, for example, speed up efforts to perform rescues and put out fires. Such flexibility and speed of coordination would largely improve the success and efficiency of rescue missions during natural disasters -- and save lives.