neuromorphic engineers

Abstract: Neural simulation is an essential methodology for studying biological nervous systems, but it remains limited by the computational costs of repeatedly updating neural and synaptic states over many timesteps. Neuromorphic hardware is a promising substrate for this class of workloads because such devices combine event-driven execution, localized memory, and spike-based communication. However, neuromorphic \textit{software} remains comparatively fragmented and difficult to integrate with the model-description standards used by the computational neuroscience community. This disconnect limits reproducible experimentation and makes direct comparison between conventional and neuromorphic simulation workflows difficult. This paper introduces ``NeuroML2Loihi'', an open-source framework for translating NeuroML model descriptions into Loihi~2-deployable Lava primitives, enabling standardized biological models to be executed on Intel's Loihi~2 neuromorphic chip. We evaluate our framework using c302-derived \textit{C. elegans} neural models across three connectome-scale configurations. Cross-platform similarity is assessed using spike-train and voltage-trace metrics, while latency, power, and energy consumption are benchmarked against the conventional reference workflow. Across the evaluated models, Loihi~2 execution enabled through NeuroML2Loihi achieves 514--1211$\times$ lower latency and a 1.8--1.9$\times$ reduction in mean wattage relative to the NEURON reference, producing substantially lower total energy consumption primarily through reduced execution time. These results demonstrate that standards-based neuromorphic neural simulation is feasible on Loihi~2 and can support meaningful comparison against established computational neuroscience workflows.

Abstract: Understanding the extent to which measured synaptic wiring determines computation remains a central challenge. Here, we couple the proofread adult Drosophila melanogaster connectome to an anatomically faithful model of its eye. Visual information is inputted in the eye model, then passed to the connectome, and finally read from a Kenyon-cell-centered linear decoder. This creates a connectome-only model in which the anatomical graph and eye geometry are fixed and only scalar synaptic gains and neuronal thresholds may be learned. The model supports multitask vision, including color discrimination, shape classification, and numerical discrimination that follows a ratio-dependent scaling characteristic of approximate number perception. To test whether precise connectivity is consequential under wiring economy, we compare the biological graph to randomized ensembles that increasingly preserve biological synaptic constraints. At matched wiring cost, the biological network consistently yields higher accuracy, whereas less constrained rewiring surpasses it at the cost of inflated wiring. These findings indicate that the measured connectivity and eye geometry jointly set efficient operating points for visual computation.

Abstract: Biological sensory neurons have selective receptive fields organized along meaningful stimulus coordinates, such as frequency, motion direction, or retinotopic position. Such structure may arise from efficient coding and biological constraints on activity, connectivity, and wiring, as computational studies of simple neurons have shown across modalities. This raises a question: do structured receptive fields confer a computational advantage beyond resource efficiency itself, and does this advantage persist when individual neurons are highly expressive? We address this question in recurrent networks of Expressive Leaky Memory neurons, where we can independently vary neuronal complexity and the organization of feed-forward receptive fields. Across auditory and event-based visual classification tasks, receptive fields aligned with a task-relevant sensory coordinate improve test accuracy relative to budget-matched random receptive fields. This advantage disappears when sensory coordinates are scrambled, or when receptive fields follow task-irrelevant coordinates, showing that the benefit comes from alignment with task geometry rather than restricted connectivity alone. Increasing neuronal complexity reduces the performance advantage of structured receptive fields. Finally, generic synaptic sparsity regularization induces input selectivity and partially recovers performance, but remains substantially below explicitly structured receptive fields, suggesting that sparsity alone is insufficient to recover the full computational benefit of task-aligned receptive fields. Together, our results show that appropriate receptive fields can serve as a computational prior beyond sparsity itself, and that their value depends on the computational expressivity of individual neurons.