This paper introduces a continuous model for Multi-cellular Developmental Design. The cells are fixed on a 2D grid and exchange "chemicals" with their neighbors during the growth process. The quantity of chemicals that a cell produces, as well as the differentiation value of the cell in the phenotype, are controlled by a Neural Network (the genotype) that takes as inputs the chemicals produced by the neighboring cells at the previous time step. In the proposed model, the number of iterations of the growth process is not pre-determined, but emerges during evolution: only organisms for which the growth process stabilizes give a phenotype (the stable state), others are declared nonviable. The optimization of the controller is done using the NEAT algorithm, that optimizes both the topology and the weights of the Neural Networks. Though each cell only receives local information from its neighbors, the experimental results of the proposed approach on the 'flags' problems (the phenotype must match a given 2D pattern) are almost as good as those of a direct regression approach using the same model with global information. Moreover, the resulting multi-cellular organisms exhibit almost perfect self-healing characteristics.
However, there are even greater similarities between the present work and that in @cite_5 . In both works, the grid is filled with cells at iteration 0 of the growth process (i.e. no replication is allowed) and chemicals are propagated only in a cell-cell fashion without the diffusion mechanisms used in @cite_8 @cite_2 . Indeed, a pure cell-cell communication is theoretically sufficient for modelling any kind of temporal diffusion function, since diffusion in the substrata is the result of successive transformation with non-linear functions (such as the ones implemented by sigmoidal neural networks with hidden neurons). However, this means that the optimization algorithm must tune both the diffusion reaction and the differentiation of the cells. On the other hand, whereas @cite_5 only consider the average of the chemical concentrations of the neighboring cells (i.e. is totalistic in the Cellular Automata terminology), our approach does take into account the topology of the organism at the controller level, de facto benefitting from orientation information. This results in a more general approach, though probably less efficient to reach symmetrical targets. Here again, further experiments must be run to give a solid answer.
@cite_5 @cite_2 @cite_8
1622908138 2006456190 2022178479 2101130703
We consider a channel model based on the diffusion of particles in the medium which is motivated by the natural communication mechanisms between biological cells based on exchange of molecules. In this model, the transmitter secretes particles into the medium via a particle dissemination rate. The concentration of particles at any point in the medium is a function of its distance from the transmitter and the particle dissemination rate. The reception process is a doubly stochastic Poisson process whose rate is a function of the concentration of the particles in the vicinity of the receiver. We derive a closed-form for the mutual information between the input and output processes in this communication scenario and establish useful properties about the mutual information. We also provide a signaling strategy using which we derive a lower bound on the capacity of the diffusion channel with Poisson reception process under average and peak power constraints. Furthermore, it is shown that the capacity of discretized diffusion channel can be a negligible factor of the capacity of continuous time diffusion channel. Finally, the application of the considered model to the molecular communication systems is discussed. Abstract Simulation-based and information theoretic models for a diffusion-based short-range molecular communication channel between a nano-transmitter and a nano-receiver are constructed to analyze information rates between channel inputs and outputs when the inputs are independent and identically distributed (i.i.d.). The total number of molecules available for information transfer is assumed to be limited. It is also assumed that there is a maximum tolerable delay bound for the overall information transfer. Information rates are computed via simulation-based methods for different time slot lengths and transmitter–receiver distances. The rates obtained from simulations are then compared to those computed using information theoretic channel models which provide upper bounds for information rates. The results indicate that a 4-input–2-output discrete channel model provides a very good approximation to the nano-communication channel, particularly when the time slot lengths are large and the distance between the transmitter and the receiver is small. It is shown through an extensive set of simulations that the information theoretic channel capacity with i.i.d. inputs can be achieved when an encoder adjusts the relative frequency of binary zeros to be higher (between 50 and 70 for the scenarios considered) than binary ones, where a ‘zero’ corresponds to not releasing and a ‘one’ corresponds to releasing a molecule from the transmitter. Quantitative analysis of biochemical networks often requires consideration of both spatial and stochastic aspects of chemical processes. Despite significant progress in the field, it is still computationally prohibitive to simulate systems involving many reactants or complex geometries using a microscopic framework that includes the finest length and time scales of diffusion-limited molecular interactions. For this reason, spatially or temporally discretized simulations schemes are commonly used when modeling intracellular reaction networks. The challenge in defining such coarse-grained models is to calculate the correct probabilities of reaction given the microscopic parameters and the uncertainty in the molecular positions introduced by the spatial or temporal discretization. In this paper we have solved this problem for the spatially discretized Reaction-Diffusion Master Equation; this enables a seamless and physically consistent transition from the microscopic to the macroscopic frameworks of reaction-diffusion kinetics. We exemplify the use of the methods by showing that a phosphorylation-dephosphorylation motif, commonly observed in eukaryotic signaling pathways, is predicted to display fluctuations that depend on the geometry of the system. In this paper, a decentralized and self-organizing mechanism for small cell networks (such as micro-, femto- and picocells) is proposed. In particular, an application to the case in which small cell networks aim to mitigate the interference caused to the macrocell network, while maximizing their own spectral efficiencies, is presented. The proposed mechanism is based on new notions of reinforcement learning (RL) through which small cells jointly estimate their time-average performance and optimize their probability distributions with which they judiciously choose their transmit configurations. Here, a minimum signal to interference plus noise ratio (SINR) is guaranteed at the macrocell user equipment (UE), while the small cells maximize their individual performances. The proposed RL procedure is fully distributed as every small cell base station requires only an observation of its instantaneous performance which can be obtained from its UE. Furthermore, it is shown that the proposed mechanism always converges to an epsilon Nash equilibrium when all small cells share the same interest. In addition, this mechanism is shown to possess better convergence properties and incur less overhead than existing techniques such as best response dynamics, fictitious play or classical RL. Finally, numerical results are given to validate the theoretical findings, highlighting the inherent tradeoffs facing small cells, namely exploration exploitation, myopic foresighted behavior and complete incomplete information.