“What-Where” sparse distributed invariant representations of visual patterns

Sa-Couto, Luis; Wichert, Andreas

doi:10.1007/s00521-021-06759-0

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…This capacity is only valid for sparse equally distributed ones [ 18 ]. The description of how to generate efficiently binary sparse codes of visual patterns or other data structures is described in [ 39 , 40 , 41 ]. For example, real vector patterns have to be binarized.…”

Section: Lernmatrixmentioning

confidence: 99%

Quantum Lernmatrix

Wichert

2023

Entropy

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We introduce a quantum Lernmatrix based on the Monte Carlo Lernmatrix in which n units are stored in the quantum superposition of log2(n) units representing On2log(n)2 binary sparse coded patterns. During the retrieval phase, quantum counting of ones based on Euler’s formula is used for the pattern recovery as proposed by Trugenberger. We demonstrate the quantum Lernmatrix by experiments using qiskit. We indicate why the assumption proposed by Trugenberger, the lower the parameter temperature t; the better the identification of the correct answers; is not correct. Instead, we introduce a tree-like structure that increases the measured value of correct answers. We show that the cost of loading L sparse patterns into quantum states of a quantum Lernmatrix are much lower than storing individually the patterns in superposition. During the active phase, the quantum Lernmatrices are queried and the results are estimated efficiently. The required time is much lower compared with the conventional approach or the of Grover’s algorithm.

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Section: Lernmatrixmentioning

confidence: 99%

Quantum Lernmatrix

Wichert

2023

Entropy

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“…In previous work (Sa-Couto and Wichert 2019 , 2022 ), using visual cortex based principles we proposed the “What-Where” encoder. This network can be trained with Hebbian rules without using labels, and the embeddings it generates have been shown to work extremely well for both classification and associative memory tasks (Sa-Couto and Wichert 2020 , 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Self-organizing maps on “what-where” codes towards fully unsupervised classification

Sa-Couto

Wichert

2023

Biol Cybern

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Interest in unsupervised learning architectures has been rising. Besides being biologically unnatural, it is costly to depend on large labeled data sets to get a well-performing classification system. Therefore, both the deep learning community and the more biologically-inspired models community have focused on proposing unsupervised techniques that can produce adequate hidden representations which can then be fed to a simpler supervised classifier. Despite great success with this approach, an ultimate dependence on a supervised model remains, which forces the number of classes to be known beforehand, and makes the system depend on labels to extract concepts. To overcome this limitation, recent work has been proposed that shows how a self-organizing map (SOM) can be used as a completely unsupervised classifier. However, to achieve success it required deep learning techniques to generate high quality embeddings. The purpose of this work is to show that we can use our previously proposed What-Where encoder in tandem with the SOM to get an end-to-end unsupervised system that is Hebbian. Such system, requires no labels to train nor does it require knowledge of which classes exist beforehand. It can be trained online and adapt to new classes that may emerge. As in the original work, we use the MNIST data set to run an experimental analysis and verify that the system achieves similar accuracies to the best ones reported thus far. Furthermore, we extend the analysis to the more difficult Fashion-MNIST problem and conclude that the system still performs.

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“…Willshaw's model of Associative memory is a likely candidate for a computational model of this brain function, but its application on real-world data is hindered by the so-called Sparse Coding Problem. Due to a recently proposed sparse encoding prescription [31], which maps visual patterns into binary feature maps, we were able to analyze the behavior of the Willshaw Network (WN) on real-world data and gain key insights into the strengths of the model. To further enhance the capabilities of the WN, we propose the Multiple-Modality architecture.…”

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Multiple-Modality Associative Memory: a framework for Learning

Simas¹,

Sa-Couto²,

Wichert³

2022

Preprint

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Drawing from memory the face of a friend you have not seen in years is a difficult task. However, if you happen to cross paths, you would easily recognize each other. The biological memory is equipped with an impressive compression algorithm that can store the essential, and then infer the details to match perception. Willshaw's model of Associative memory is a likely candidate for a computational model of this brain function, but its application on real-world data is hindered by the so-called Sparse Coding Problem. Due to a recently proposed sparse encoding prescription [31], which maps visual patterns into binary feature maps, we were able to analyze the behavior of the Willshaw Network (WN) on real-world data and gain key insights into the strengths of the model. To further enhance the capabilities of the WN, we propose the Multiple-Modality architecture. In this new setting, the memory stores several modalities (e.g., visual, or textual) simultaneously. After training, the model can be used to infer missing modalities when just a subset is perceived, thus serving as a flexible framework for learning tasks. We evaluated the model on the MNIST dataset. By storing both the images and labels as modalities, we were able to successfully perform pattern completion, classification, and generation with a single model.

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“What-Where” sparse distributed invariant representations of visual patterns

Cited by 4 publications

References 21 publications

Quantum Lernmatrix

Quantum Lernmatrix

Self-organizing maps on “what-where” codes towards fully unsupervised classification

Multiple-Modality Associative Memory: a framework for Learning

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