A Gradient-based yet Spike-Timing-Dependent Solution to the Feedback Learning Problem in Neural Microcircuits

• URL: http://arxiv.org/abs/2609.08070v2
• Date: Thu, 10 Sep 2026 07:32:41 GMT
• Title: A Gradient-based yet Spike-Timing-Dependent Solution to the Feedback Learning Problem in Neural Microcircuits
• Abstract summary: How neural microcircuits (NMCs) solve temporal credit assignment using local spike timing remains a fundamental open question.<n>Here, we reformulate temporal credit assignment as a state separation problem.<n>This enables an online feedback learning framework for NMCs through a tunneling gradient (GT) algorithm and the lead-lag expansion technique.<n> Experimentally, GT-trained NMCs excel at long-timescale evidence integration and noise-robust memory retention.
• Score: 43.880352532823956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timing remains a fundamental open question. Dominant spiking neural network (SNN) approaches circumvent this by approximating backpropagation through surrogate gradients, decoupling learning from biological spike timing. Here, we reformulate temporal credit assignment as a state separation problem: extracting task-required components induced by historical perturbations directly from the current neural state. This enables an online feedback learning framework for NMCs through a gradient tunneling (GT) algorithm and the lead-lag expansion technique that derives credit assignment from local synaptic spike timing, while remaining compatible with ANN-SNN hybrid architectures. Experimentally, GT-trained NMCs excel at long-timescale evidence integration and noise-robust memory retention, and perform comparably to leading SNN online learning methods on real-world benchmarks with far fewer parameters. The proposed framework addresses the two-decade-old NMC feedback learning problem and suggests a computationally plausible explanation for the brain's learning mechanisms.

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