AI Paper Atlas

Research map / Probabilistic and scientific modeling / Dynamical systems

Impression learning: Online representation learning with synaptic plasticity

NeurIPS 2021 · Probabilistic and scientific modeling › Dynamical systems · approach: Recurrent circuit models of inference · 7 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace NVI* three-factor update with impression learning updateImpression learning · Artificial stimulus taskgradient SNRHelped
Replace NVI* with impression learning as stimulus dimension growsImpression learning · Artificial stimuli, varying dimensionalityasymptotic negative ELBOHelped
Impression learning versus backpropagationImpression learning · Artificial stimuli, varying dimensionalityasymptotic negative ELBONo clear effect
Lengthen phase duration K from 2 to 32 (ablation)Impression learning · Artificial stimulinegative ELBO lossNo clear effect
Lengthen phase duration K from 2 to 32 (ablation)Impression learning · Artificial stimulicorrelation with pure inference-mode activityHurt
Lengthen inference phase while keeping short generative bursts (ablation)Impression learning · Artificial stimuliperceptual disturbance and learning timeMixed
Use impression learning instead of NVI* in two-layer networkImpression learning · Free Spoken Digits Datasettest lossHelped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy