DNA Reservoir Computing: A Novel Molecular Computing Approach

Goudarzi, Alireza; Lakin, Matthew R.; Stefanović, Darko

doi:10.1007/978-3-319-01928-4_6

Cited by 27 publications

(42 citation statements)

References 36 publications

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“…Among other techniques, recurrent neural networks (RNNs) of reservoir computing type, known as universal computations [19,20], appear appropriate for estimating (nonlinear) temporal or history-based data [21]. This kind of recurrent neural network consists of three layers (input, hidden, and output).…”

Section: Introductionmentioning

confidence: 99%

Neural computational model GrowthEstimate: A model for studying living resources through digestive efficiency

Rungruangsak‐Torrissen

Manoonpong

2019

PLoS ONE

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The neural computational model GrowthEstimate is introduced with focusing on new perspectives for the practical estimation of weight specific growth rate (SGR, % day –1 ). It is developed using recurrent neural networks of reservoir computing type, for estimating SGR based on the known data of three key biological factors relating to growth. These factors are: (1) weight (g) for specifying the age of the growth stage; (2) digestive efficiency through the pyloric caecal activity ratio of trypsin to chymotrypsin (T/C ratio) for specifying genetic differences in food utilization and growth potential, basically resulting from food consumption under variations in food quality and environmental conditions; and (3) protein growth efficiency through the condition factor (CF, 100 × g cm –3 ), as higher dietary protein level affecting higher skeletal growth (length) and resulting in lower CF. The computational model was trained using four datasets of different salmonids with size variations. It was evaluated with 15% of each dataset, resulting in an acceptable range of SGR outputs. Additional tests with different species indicated similarity between the estimated SGR outputs and the real SGR values, and the same ranking of wild population growth. The developed model GrowthEstimate is exceptionally useful for the precise and comparable growth estimation of living resources at individual levels, especially in natural ecosystems where the studied individuals, environmental conditions, food availability and consumption rates cannot be controlled. It is a revelation and will help to minimize uncertainty in wild stock assessment process. This will improve our knowledge in nutritional ecology, through the biochemical effects of climate change and environmental impact on the growth performance quality of aquatic living resources in the wild, as well as in aquaculture. The original GrowthEstimate software is available at GitHub repository ( https://github.com/RungruangsakTorrissenManoonpong/GrowthEstimate ). All other relevant data are within the paper. It will be improved for generality for future use, and required co-operations of the biodata collections of different species from different climate zones. Therefore, a co-operation will be available.

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Section: Introductionmentioning

confidence: 99%

Neural computational model GrowthEstimate: A model for studying living resources through digestive efficiency

Rungruangsak‐Torrissen

Manoonpong

2019

PLoS ONE

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“…The ring is the simplest network to implement in physical hardware as the number of connections required is small. Ring structures with various connection types have been applied to many reservoir computing systems, including Simple Cycle Reservoirs (SCR) (Rodan and Tiňo 2011), DNA reservoirs (deoxyribozyme oscillators) (Goudarzi et al 2013), and delay-line reservoirs using a single non-linear node with a delay line (Appeltant et al 2011;Paquot et al 2012).…”

Section: Topologiesmentioning

confidence: 99%

Reservoir computing quality: connectivity and topology

et al. 2020

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We explore the effect of connectivity and topology on the dynamical behaviour of Reservoir Computers. At present, considerable effort is taken to design and hand-craft physical reservoir computers. Both structure and physical complexity are often pivotal to task performance, however, assessing their overall importance is challenging. Using a recently developed framework, we evaluate and compare the dynamical freedom (referring to quality) of neural network structures, as an analogy for physical systems. The results quantify how structure affects the behavioural range of networks. It demonstrates how high quality reached by more complex structures is often also achievable in simpler structures with greater network size. Alternatively, quality is often improved in smaller networks by adding greater connection complexity. This work demonstrates the benefits of using dynamical behaviour to assess the quality of computing substrates, rather than evaluation through benchmark tasks that often provide a narrow and biased insight into the computing quality of physical systems.

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“…A basic ring topology is the simplest network to implement in physical hardware as the number of connections required is small. Ring structures with various connection types have been applied to reservoir computing systems, including minimum complexity echo state networks (ESN) [24], DNA reservoirs (deoxyribozyme oscillators) [12], Cycle reservoirs with regular jumps (CRJ) [23], and delay-based reservoirs using a single non-linear node with a delay line [3,21].…”

Section: Simulated Network Topologiesmentioning

confidence: 99%

The Role of Structure and Complexity on Reservoir Computing Quality

Dale

Dewhirst

O’Keefe

et al. 2019

Unconventional Computation and Natural Computation

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We explore the effect of structure and connection complexity on the dynamical behaviour of Reservoir Computers (RC). At present, considerable effort is taken to design and hand-craft physical reservoir computers. Both structure and physical complexity are often pivotal to task performance, however, assessing their overall importance is challenging. Using a recently proposed framework, we evaluate and compare the dynamical freedom (referring to quality) of neural network structures, as an analogy for physical systems. The results quantify how structure affects the range of behaviours exhibited by these networks. It highlights that high quality reached by more complex structures is often also achievable in simpler structures with greater network size. Alternatively, quality is often improved in smaller networks by adding greater connection complexity. This work demonstrates the benefits of using abstract behaviour representation, rather than evaluation through benchmark tasks, to assess the quality of computing substrates, as the latter typically has biases, and often provides little insight into the complete computing quality of physical systems.

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DNA Reservoir Computing: A Novel Molecular Computing Approach

Cited by 27 publications

References 36 publications

Neural computational model GrowthEstimate: A model for studying living resources through digestive efficiency

Neural computational model GrowthEstimate: A model for studying living resources through digestive efficiency

Reservoir computing quality: connectivity and topology

The Role of Structure and Complexity on Reservoir Computing Quality

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