{"paper":{"title":"Digital Multiplier-less Event-Driven Spiking Neural Network Architecture for Learning a Context-Dependent Task","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"q-bio.NC","authors_text":"BabakMazloom-Nezhad Maybodi, Hajar Asgari, Raphaela Kreiser, Yulia Sandamirskaya","submitted_at":"2019-06-24T10:17:16Z","abstract_excerpt":"Neuromorphic engineers aim to develop event-based spiking neural networks (SNNs) in hardware. These SNNs closer resemble dynamics of biological neurons than todays' artificial neural networks and achieve higher efficiency thanks to the event-based, asynchronous nature of processing. Learning in SNNs is more challenging, however. Since conventional supervised learning methods cannot be ported on SNNs due to the non-differentiable event-based nature of their activation, learning in SNNs is currently an active research topic. Reinforcement learning (RL) is particularly promising method for neurom"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.09835","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}