{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sequence-approximation-using-feedforward","title":"Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods","arxiv_id":null,"date":"2021-09-29","proceeding":"ICLR 2022 4","authors":["Xueyuan She","Saurabh Dash","Saibal Mukhopadhyay"],"abstract":"A dynamical system of spiking neurons with only feedforward connections can classify spatiotemporal patterns without recurrent connections. However, the theoretical construct of a feedforward Spiking Neural Network (SNN) for approximating a temporal sequence remains unclear, making it challenging to optimize SNN architectures for learning complex spatiotemporal patterns. \nIn this work, we establish a theoretical framework to understand and improve sequence approximation using a feedforward SNN. \nOur framework shows that a feedforward SNN with one neuron per layer and skip-layer connections can approximate the mapping function between any arbitrary pairs of input and output spike train on a compact domain. Moreover, we prove that heterogeneous neurons with varying dynamics and skip-layer connections improve sequence approximation using feedforward SNN. Consequently, we propose SNN architectures incorporating the preceding constructs that are trained using supervised backpropagation-through-time (BPTT) and unsupervised spiking-timing-dependent plasticity (STDP) algorithms for classification of spatiotemporal data. A Dual Search-space Bayseian Optimization method is developed to optimize architecture and parameters of the proposed SNN with heterogeneous neuron dynamics and skip-layer connections. ","url_abs":"https://openreview.net/forum?id=bp-LJ4y_XC","url_pdf":"https://openreview.net/pdf?id=bp-LJ4y_XC","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"learning-theory","task_name":"Learning Theory"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/gesture-recognition-on-dvs128-gesture","task":"Gesture Recognition","dataset":"DVS128 Gesture","model":"mMND (BPTT)","rank_in_archive_order":2,"of":14,"metrics":{"Accuracy (%)":"98.0"},"uses_additional_data":false},{"leaderboard":"/sota/gesture-recognition-on-dvs128-gesture","task":"Gesture Recognition","dataset":"DVS128 Gesture","model":"mMND (STDP)","rank_in_archive_order":10,"of":14,"metrics":{"Accuracy (%)":"96.6"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-n-caltech-101","task":"Image Classification","dataset":"N-Caltech 101","model":"mMND (STDP)","rank_in_archive_order":1,"of":3,"metrics":{"Accuracy":"58.1"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-n-caltech-101","task":"Image Classification","dataset":"N-Caltech 101","model":"mMND (BPTT)","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"71.2"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}