Finite-State Computation in Analog Neural Networks: Steps towards Biologically Plausible Models?

Forcada, Mikel L.; Carrasco, Rafael C.

doi:10.1007/3-540-44597-8_34

Cited by 9 publications

(7 citation statements)

References 30 publications

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“…All the remaining vertices (i.e., vertices not in HP, but in RHP) lie on the other side of hyperplane (11):…”

Section: Proofmentioning

confidence: 99%

“…Construction of innovative methods for BNN learning has been a hot topic of research in the last decade. [6][7][8][9][10] These developments have resulted in new approaches for VLSI 3 implementations, as well as learning theory [11][12][13] and artificial intelligence. 11 The applications of BNNs in these areas have now resulted in well-developed methods.…”

Section: Introductionmentioning

confidence: 99%

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Binary Neural Network Training Algorithms Based on Linear Sequential Learning

Wang

Chaudhari

2003

Int. J. Neur. Syst.

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A key problem in Binary Neural Network learning is to decide bigger linear separable subsets. In this paper we prove some lemmas about linear separability. Based on these lemmas, we propose Multi-Core Learning (MCL) and Multi-Core Expand-and-Truncate Learning (MCETL) algorithms to construct Binary Neural Networks. We conclude that MCL and MCETL simplify the equations to compute weights and thresholds, and they result in the construction of simpler hidden layer. Examples are given to demonstrate these conclusions.

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“…All the remaining vertices (i.e., vertices not in HP, but in RHP) lie on the other side of hyperplane (11):…”

Section: Proofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Binary Neural Network Training Algorithms Based on Linear Sequential Learning

Wang

Chaudhari

2003

Int. J. Neur. Syst.

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“…In addition, the significant increase in computing power exhibited over the past few decades has invigorated the field of machine learning, especially neural networks research. Neural networks are highly parallelized structures based on the workings of nervous systems, which show great potential for modelling biological constructs such as brains [2]. At the same time, neurobiology and human-brain research are progressing at a rapid pace.…”

Section: Introductionmentioning

confidence: 99%

Network-on-chip evaluation for a novel neural architecture

Kynigos

Navaridas

Plana

et al. 2018

Proceedings of the 15th ACM International Conference on Computing Frontiers

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This paper provides a performance evaluation and trade-off analysis of a novel chip architecture for neuromorphic computing, especially focused on the memory subsystems and the Network-On-Chip (NoC). More precisely, we study the performance-related effect of the number of memory modules, as well as that of allowing direct core-to-core communication. Our simulation-based experimental work throws many interesting results on the above aspects and allows to ensure that congestion at the NoC-level is unlikely to degrade performance.

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“…Over recent decades, many researchers have explored the relationship between discrete-time recurrent neural networks and finite state machines, either by showing their computational equivalence or by training the former to perform as finite state recognizers [ 1 ]. The relationship between discrete-time recurrent neural networks and finite state machines has very deep roots [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

A Finite State Machine Approach to Algorithmic Lateral Inhibition for Real-Time Motion Detection †

López

Bermúdez

Montero

et al. 2018

Sensors

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Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best-characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The neurally-inspired lateral inhibition method, and its application to motion detection tasks, have been successfully implemented in recent years. In this paper, control knowledge of the algorithmic lateral inhibition (ALI) method is described and applied by means of finite state machines, in which the state space is constituted from the set of distinguishable cases of accumulated charge in a local memory. The article describes an ALI implementation for a motion detection task. For the implementation, we have chosen to use one of the members of the 16-nm Kintex UltraScale+ family of Xilinx FPGAs. FPGAs provide the necessary accuracy, resolution, and precision to run neural algorithms alongside current sensor technologies. The results offered in this paper demonstrate that this implementation provides accurate object tracking performance on several datasets, obtaining a high F-score value (0.86) for the most complex sequence used. Moreover, it outperforms implementations of a complete ALI algorithm and a simplified version of the ALI algorithm—named “accumulative computation”—which was run about ten years ago, now reaching real-time processing times that were simply not achievable at that time for ALI.

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Finite-State Computation in Analog Neural Networks: Steps towards Biologically Plausible Models?

Cited by 9 publications

References 30 publications

Binary Neural Network Training Algorithms Based on Linear Sequential Learning

Binary Neural Network Training Algorithms Based on Linear Sequential Learning

Network-on-chip evaluation for a novel neural architecture

A Finite State Machine Approach to Algorithmic Lateral Inhibition for Real-Time Motion Detection †

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