Speaker
Description
Computational, theoretical, and systems neuroscience increasingly rely on abstract models, simulations, and large-scale datasets. This creates a responsibility to properly train students and early-stage researchers, the next generation of neuroscientists, who will encounter such data in their scientific practice. Before analysing complex neural datasets, learners should have the opportunity to develop an intuitive understanding of how neural signals are generated, recorded, transformed, and shaped by experimental conditions. Without this foundation, there is a risk that large-scale datasets become detached from the biological and methodological realities that produced them.
Here, we present Spikeling, an open-source hardware and software platform built around artificial spiking neurons, designed to make core concepts in computational, theoretical, and systems neuroscience experimentally accessible. Through a hands-on interface, users can manipulate parameters related to excitability, stimulation, synaptic input, noise, threshold, and firing dynamics, while observing neural-like activity in real time. This allows learners to connect model assumptions and parameter regimes to observable electrophysiological signals.
Spikeling supports the exploration of experimental recording workflows, from electrophysiology-inspired voltage traces to calcium-imaging-like representations and downstream analysis. In this way, the platform helps learners understand not only what neural datasets represent, but also how they are collected, processed, and constrained by the experimental methods used. As a result, this provides a practical intermediate step before working with larger and more complex neural datasets.
Because the hardware and software are open-source, Spikeling is intended to support reproducible education, transparent methods, and community-driven modification. It also contributes to 3Rs-compatible teaching by reducing the need for live animal demonstrations in introductory contexts, while preparing students more effectively for biological experiments.
We propose Spikeling as a practical bridge between equations, simulations, recordings, and analysis: a tool for making theoretical and computational neuroscience tangible before moving to complex experimental systems.
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