Cognitive Computation

, Volume 8, Issue 5, pp 924–934 | Cite as

A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems



We present a biologically inspired architecture for incremental learning that remains resource-efficient even in the face of very high data dimensionalities (>1000) that are typically associated with perceptual problems. In particular, we investigate how a new perceptual (object) class can be added to a trained architecture without retraining, while avoiding the well-known catastrophic forgetting effects typically associated with such scenarios. At the heart of the presented architecture lies a generative description of the perceptual space by a self-organized approach which at the same time approximates the neighborhood relations in this space on a two-dimensional plane. This approximation, which closely imitates the topographic organization of the visual cortex, allows an efficient local update rule for incremental learning even in the face of very high dimensionalities, which we demonstrate by tests on the well-known MNIST benchmark. We complement the model by adding a biologically plausible short-term memory system, allowing it to retain excellent classification accuracy even under incremental learning in progress. The short-term memory is additionally used to reinforce new data statistics by replaying previously stored samples during dedicated “sleep” phases.


Perceptual learning Self-organization Incremental learning Biological modeling 


Compliance with Ethical Standards

Conflict of Interest

Alexander Gepperth and Cem Karagouz declare that they have no conflict of interest.

Informed Consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

Human and Animals Rights

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.ENSTA ParisTechUIIS Lab University of Paris-SaclayPalaiseauFrance
  2. 2.INRIA FLOWERSTalenceFrance

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