High capacity neural networks far familiarity discrimination

Bogacz, Rafał; Brown, Mackenzie; Giraud‐Carrier, Christophe

doi:10.1049/cp:19991205

Cited by 25 publications

(26 citation statements)

References 5 publications

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“…This hypothesis has been confirmed in some related experiments, in which subjects made familiarity decisions faster than recollect decisions [34], [46], [47]. The computational efficiency of a recognition system dedicated for familiarity discrimination has also been demonstrated by the simulated neural networks [48]. Compared with systems relying on associative learning, familiarity decision can be made accurate and faster by a specially designed network with a smaller size of neuron population.…”

Section: ) Familiarity Recognition Is Rapid and Accurate And Onlymentioning

confidence: 57%

Introducing Memory and Association Mechanism Into a Biologically Inspired Visual Model

Qiao

Tang

et al. 2014

IEEE Trans. Cybern.

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A famous biologically inspired hierarchical model (HMAX model), which was proposed recently and corresponds to V1 to V4 of the ventral pathway in primate visual cortex, has been successfully applied to multiple visual recognition tasks. The model is able to achieve a set of position- and scale-tolerant recognition, which is a central problem in pattern recognition. In this paper, based on some other biological experimental evidence, we introduce the memory and association mechanism into the HMAX model. The main contributions of the work are: 1) mimicking the active memory and association mechanism and adding the top down adjustment to the HMAX model, which is the first try to add the active adjustment to this famous model and 2) from the perspective of information, algorithms based on the new model can reduce the computation storage and have a good recognition performance. The new model is also applied to object recognition processes. The primary experimental results show that our method is efficient with a much lower memory requirement.

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Section: ) Familiarity Recognition Is Rapid and Accurate And Onlymentioning

confidence: 57%

Introducing Memory and Association Mechanism Into a Biologically Inspired Visual Model

Qiao

Tang

et al. 2014

IEEE Trans. Cybern.

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“…Particularly relevant work to this article is Bogacz et al (1999). Although they describe the task as familiarity discrimination, they essentially represent a set of previously seen elements using a Hopfield network.…”

Section: Familiarity Discriminationmentioning

confidence: 99%

Efficient and robust associative memory from a generalized Bloom filter

Sterne

2012

Biol Cybern

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We develop a variant of a Bloom filter that is robust to hardware failure and show how it can be used as an efficient associative memory. We define a measure of the information recall and show that our new associative memory is able to recall more than twice as much information as a Hopfield network. The extra efficiency of our associative memory is all the more remarkable as it uses only bits while the Hopfield network uses integers.

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“…The combination of a SOM neural network with a Hopfield network for combination that seem to be beneficial to our problem: i) a self-organizing map as our DG, used to categorize the prototypes; ii) an associative memory, namely a Hopfield network as our CA3, originated to manage pattern recall of already stored patterns; and iii) the detection of novel patterns as in the CA1, is managed by calculating the energy level change in the Hopfield network in the first iteration after presenting an unfamiliar pattern [2] to the network.…”

Section: Self-organizing Map (Kohonen Network) Associative Memory Hopmentioning

confidence: 99%

Hippocampal-like categorization of object views: A self-organizing learning approach to vision modeling using stochastic grammar inference and associative memory

Goebel

Vincze

Favre-Bulle

2008

2008 IEEE International Conference on Emerging Technologies and Factory Automation

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The categorization problem in object recognition is the assignment of semantic categories to objects or parts of objects. Here, the best categorization performance provide mammalian brain functions that motivate to partly mimic such a formation for the application to machine learning and automation. Curiosity and experimental willingness support human recognizing and detecting in relation to still unknown objects. In order to learn an understanding of the topology of such objects, objects are observed from a series of different viewpoints. This results in a collection of object views that afterward can be used by cognitive processes. State-of-the-art systems provide learning structures that are subsequently utilized for object recognition and tracking tasks. However, most of these systems aim at very specific goals in restricted domains, and therefore, it remains no room for learning and understanding of object structures. In the work of this paper, we propose a new robot vision model for neural categorization. This is close to our initial idea of mimicking mammalian brain functions for robot vision. We use the combinatorial solution of our cognitive framework with an embedding of a recently presented stochastic n-gram model, supported by a three-dimensional grammar model on a discrete three-dimensional lattice. Furthermore, we use ant colony optimization heuristics for collecting the transition probabilities of the n-gram model. The proposed solution is exemplified by applying the method to several object view series of 69 polytopes generated out of the five platonic solids by truncation; thus, generating images from 32 viewpoints each, yields an object set of 2208 images. These set is further expanded to four sets perturbed by Gaussian-noise with varying σ = {0, 0.1, 0.5, 1, 2}. Finally, we show results for selected objects and conclude with an outlook on further work.

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High capacity neural networks far familiarity discrimination

Cited by 25 publications

References 5 publications

Introducing Memory and Association Mechanism Into a Biologically Inspired Visual Model

Introducing Memory and Association Mechanism Into a Biologically Inspired Visual Model

Efficient and robust associative memory from a generalized Bloom filter

Hippocampal-like categorization of object views: A self-organizing learning approach to vision modeling using stochastic grammar inference and associative memory

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