Team:USP-UNESP-Brazil/Associative Memory/Introduction
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They implemented a Hopfield associative memory with four fully connected artificial neurons that, after training in silico, remembers four single-stranded DNA patterns and recalls the most similar one when presented with an incomplete pattern. | They implemented a Hopfield associative memory with four fully connected artificial neurons that, after training in silico, remembers four single-stranded DNA patterns and recalls the most similar one when presented with an incomplete pattern. | ||
- | The final goal of this project is to build an associative memory network in a populational system of ''E.coli''. In our design, a population of bacteria represents a “neuron” of the network. Each neuron communicates with the whole network by quorum sensing molecules (QSM). Once all "neurons" are connected, it defines a called Hopfield associative memory architecture. | + | The final goal of this project is to build an associative memory network in a populational system of ''E.coli''. In our design, a population of bacteria represents a “neuron” of the network. Each neuron communicates with the whole network by quorum sensing molecules (QSM) and the interactions can be repressive or excitatory. Once all "neurons" are connected, it defines a called Hopfield associative memory architecture. |
<!-- The inhibition or stimulation of GFP production is based on a transcriptional regulation mechanism. The communication between bacterial populations occurs by means of quorum sensing substances and the information (inhibiting or exciting) will be defined by which transcriptional regulator the substance will promote. In summary, at the moment when the connections between the neurons are defined, we the system should return a predetermined response pattern. --> | <!-- The inhibition or stimulation of GFP production is based on a transcriptional regulation mechanism. The communication between bacterial populations occurs by means of quorum sensing substances and the information (inhibiting or exciting) will be defined by which transcriptional regulator the substance will promote. In summary, at the moment when the connections between the neurons are defined, we the system should return a predetermined response pattern. --> |
Revision as of 14:11, 26 September 2012
Network
Objectives
The objective of this project is to build an associative memory network using E.coli populations that should be able to memorize a given pattern and converge to this pattern when exposed to a similar one. This is a demonstration of a systemic memory storage in synthetic biology.
Background
Synthetic biology is a powerful tool for the construction of mechanisms capable of executing routines for processing and storing information in vivo. For example, Quian et al. [http://www.nature.com/nature/journal/v475/n7356/full/nature10262.html?WT.ec_id=NATURE-20110721] built a small neural network using DNA strand displacement cascades. They implemented a Hopfield associative memory with four fully connected artificial neurons that, after training in silico, remembers four single-stranded DNA patterns and recalls the most similar one when presented with an incomplete pattern.
The final goal of this project is to build an associative memory network in a populational system of E.coli. In our design, a population of bacteria represents a “neuron” of the network. Each neuron communicates with the whole network by quorum sensing molecules (QSM) and the interactions can be repressive or excitatory. Once all "neurons" are connected, it defines a called Hopfield associative memory architecture.
Application
In addition to be one of the first Hopfield Network models made in vivo, this project shows different lineages of bacteria communicating with each other, establishing balance through their systemic memory. This could be useful in the production of bioproducts, such as biofuels - for instance, a biosystem producing some compound inside a reactor could regulate itself according to specific parameter changes, such as temperature or nutrient concentration - it would perform that by communicating within its network and restoring the pattern stored in its systemic memory. In the future, self-controlled biosystems might be possible, cheap and ecologically friendly alternatives for the industry.