Short Communication EVOLUTIONARY NEURAL GAS: A SCALE-FREE SELF- ORGANIZING NEURAL NET WITHOUT TOPOLOGICAL CONSTRAINT

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1 Short Communication EVOLUTIONARY NEURAL GAS: A SCALE-FREE SELF- ORGANIZING NEURAL NET WITHOUT TOPOLOGICAL CONSTRAINT Luigi Lella 1, Ignazio Licata 1) Università Politecnica delle Marche, D.E.I.T., via Brecce Bianche,6131, Ancona, Italy l.lella@inform.unian.it. ) Istituto di Cibernetica Non-Lineare per lo Studio dei Sistemi Complessi, via Favorita 9,915 Marsala (TP), Italy licata@programmazione.it Introduction The self-organizing nets are particularly fit to carry out cluster analysis of non-hierarchic type, by using competitive learning strategies. It makes them the ideal model for the categorizing processes in natural and artificial intelligent systems. The most patent limits of such models lie in making use of deterministic rules and dimensional constraints (1). In observing the evolutive processes, we can see that the system-environment interaction does not let us foresee beforehand what structural organization the net will take, on the contrary it is emerging during the process and often varies along time. These requirements have stimulated some alternative approaches, see the TRN () or the GNG (3), but they all are based upon deterministic rules, which thing leads to the coming out of topologies whose final structure is a Delaunay Triangulation (4). From a biological viewpoint, this aspect is scarcely plausible because, in some way, it pilots the input categorization towards simple schemes whereas the inputs themselves are, in nature, a not well-defined, noise affected and highly aleatory set, so the emerging structures are generally less rigid, endowed with wide local connectivity but quite extended with respect to the clustering nodes. We delineate here a model without topological constraints led by simple probabilistic rules. The net is considered as a population of nodes where the main events conditioning its evolution, such as the creation or elimination of links and units, depend on the amount of the local and global available resources. In the model defined during the training phase, which is the assimilation of inputs, it has been adopted a winner takes all strategy (5). The links of the stronger units are not directly reinforced, when there are low resources the life probability of the weak nodes simply decays. In so doing it comes out a scale-free graph which is a mark, strongly significant from the viewpoint of physics, of self-organizing processes and information amplifying. It is known that this kind of graph can be found in many natural and artificial systems endowed with logical and thermodynamic openness (6, 7). We will see then that the above mentioned structure justifies in accordance with physics the used term of gas. 43

2 Evolutionary Algorithm The net nodes can be considered as individuals of a population living in an ecosystem. The population surviving is granted by two kinds of resources: one is global, given by the available space the net can get M P units at most -, and the other ones are local, given by the distortion errors D equal to the distances between the input vectors and the vectors (centres or weight vectors) associated to the net units closest to them (the winner units). The individuals are divided in groups developing around the winners, but they can also establish a link with other groups. The interaction modalities among individuals depend on the amount of available resources. When there is scarcity of resources the individuals show a tendency to compete each other, when there is abundance of resources the individuals show a tendency to reproduce. The evolution stops when it is reached the minimization of the expected quantization error D the average of the distances between the centres of the winners and the corresponding input vectors [1] - which represents an high modelizing level of the inputs and, consequently, a good adaptive outcome. K! D = 1 x i " w K j [1] i= 1 Each net training step, corresponding to the introducing of the succession of the input signals (an epoch), can be divided in three phase. Winner units selecting (a) They are selected the units getting the centres closest to the introduced inputs (WTA strategy). So the winners are the units closest to the local resources and, consequently, they are the strongest population units showing a tendency to stay in their own zone and no need to establish links with other groups. Centres updating (b) During this phase they are updated both the centres of the winners and the ones connected to them. The centres of the winners shift towards the corresponding inputs. The shift is equal to a fraction of the distance (difference vector) separating the centres of the winners from the corresponding inputs. The centres of the units linked to the winners are modified too, but we have now a fraction minor than the distance which separates them from the inputs. Lets put x the input corresponding to the winner characterized by w centre, and w ij the centres of the units linked to the winners: w( t + 1) = w( t) + #( x! w( t)) wi ( t + 1) = wi ( t) + " ( x! w ( t)) i [] Each unit is characterized not only by a centre, but also by a variable d representing the quadratic distance from the closest local resource. Such variable value mirrors the individual weakness. The smaller the variable value, the greater the individual surviving possibilities. At each evolutionary step, the value of this variable is set to its maximum. After each updating of the centre w of a unit, which happens when a given input x is introduced, it is 44

3 calculated the quadratic distance between the two vectors ( x-w ). If the quadratic distance is less than the node weakness, it will become its new value. Population evolutionary phase (c) The population of the net nodes evolves by producing new descendants, establishing new connections and eliminating the weaker unities. How it is shown in the figure [1], all these events are characterized by a probability depending on the availability of the system resources. Each unit i, i = [1 N(t)] where N(t) is the actual net dimension (total number of units), can meet the closest winner j with probability P m. If the meeting takes place the two units establish a link and can interact by reproducing with probability P r. In this case two new units are created and their centres will be: wi + w j wi + w 1 = wi + w j w j + w = [3] Fig. 1 - Algorithm Evolutionary Phase 45

4 If, due to the lack of resources, reproduction does not take place the weakest unit of the population, i.e. the one with the highest debility, is removed. If the i unit does not meet any winner, it can interact with its closest unit k with probability P r so producing a new unit whose centre is the following: wi + wk w = [4] if we fix a maximum population size, the ratio between N(t)/N max can be seen as a measure of the ecosystem global resources. For example, if the population size is low, the reproduction rate will be high. So we can reasonably put P r = 1 - N(t)/N max. On the contrary, if the population size is high, the higer will be the possibility of connection between two individuals, so we can put P m = N(t)/N max. We can also take into consideration a local resource linked to the expected quantization error: each unit i could meet a winner with a probability given by P = N( t) / N )(1! D / d ), and P r = 1 - P m, where D min is the average we aim to reach. m ( max min i Obviously, it can happen that the weakness d of the node taken into consideration is inferior than D min, so we have to set: D min /d < 1. The dynamic course of the population size can be studied by two models. The first one takes into consideration only the global resources, the second one takes into account the local resources according to the way they have been defined: ( 1) ( ) m r ( ) m ( 1 r ) ( ) ( 1 m) ( ) ( 1 m) d ( ) N P N ( t) N t + = N t + P P N t! P! P N t +! P N t!! P P N t = =! = t m ( ) " # ( ) ( ) N t = N ( t) 1! $ X ( t + 1) = X ( t) 1! X ( t) ( first model) % M & ' p ( " N t " Dmin # # " " Dmin # # = N ( t) 1! 1! $ X ( t + 1) = X ( t) 1! X ( t) 1! ( se cond model) % % & % & M p ' D ( & % ' D ( & ' ( ' ( [5] where X(t) is the normalized ratio N(t)/ N max. Except for the factor (1- D min /D), the formula reminds the one of the quadratic logistic map by Annunziato and Pizzuti (8) : X t + 1 = ax t 1! X t [6] ( ) ( ) ( ) ( ) The outcome is in concordance with the premises. In fact, the [5] describes an evolutionary history where the self-organization of the initial growing process takes place and it is consequently fallowed saturation, which is linked not only to the global resources but also both to the peculiar distribution within the net of the winners and the configuration of various strength units in the neighbourhood. Annunziato and Pizzuti proved that at the parameter varying different regimes arise. For a < 1.7 there is no chaotic behaviour and we have a simple attractor. For the interval between 1.7< a <.1 there arise chaotic regimes with a sequence of attractors localized in different zones of the phase space. 46

5 Simulations We have compared the performances of the ENG with the GNG ones in categorizing bidimensional inputs which are uniformly distributed on two different regions. In the former case, the inputs are localized within four square regions; in the latter one, inputs are in a ring region. As stopping criterion we have chosen the minimization of D (the D min threshold size is equal to ). For the GNG the formulae parameters for the updating of the centres are α=.5, β=.5, at each λ =3 steps a new unit is introduced and the maximum age of links is equal to 88. For the two models of ENG the formulae parameters for the centres updating are α=.5, β=.6 and the maximum size has be chosen to be equal to N max =1. How it is shown in the Figure 1, after the training the GNG vectors are all placed within the input domain, which is to say that the net tends to follow the exact topology of the input signals. On the contrary, in the ENG some units fall outside the input domain, but the net remains connected by few hubs which give it a scale-free graph structure (Fig. ). The net structural parameters appear actually to be the typical ones of a scale-free graph. In fact, the low average free path characterizes the net micro-world structure, the high clustering coefficient shows the presence of considerable aggregations of net units and the distribution of probabilities (links amount) of the nodes s displays a really slow decaying tail, which is to say there exists a restricted amount of nodes establishing much more links than the average. Fig.1 - Growing Neural Gas simulations 47

6 Fig. a - ENG simulations (first model) Fig. b - ENG simulations (second model) How it is shown in the 3 and 4 figures, in the GNG the maximum that a node can take is about 5, whereas in the ENG we can have nodes establishing more than 8 links. 48

7 y = 37,74x -, y = 68,961x -, Fig. 3 Average distribution in GNG (two different input manifolds) y = 3,75x -1, y =,69x -1, Fig. 4a Average distribution in ENG (first model, two different input manifolds) y = 4,415x -1, y = 1,98x -1, Fig. 4b Average distribution in ENG (second model, two different input manifolds) In the 1 and tables instead we have the average value of the two nets structural parameters. We got such value by averaging the values of the parameters relative to 3 different nets of the same type and dimension, which has been trained by the same inputs. The GNG get high average path length and low clustering coefficient, the ENG show short average path length and high clustering coefficient linked to the power law ruling the distribution, that s a confirmation of the scale-free features. 49

8 Tab. 1 Comparison of structural parameters (average values, first input manifold) Average path length Clustering coefficient Power law exponent GNG ENG (1st) ENG (nd) Tab. - Comparison of structural parameters (average values, second input manifold) Average path length Clustering coefficient Power law exponent GNG ENG (1st) ENG (nd) The two ENG models share the same structure, in both models they are the winners which create the most of links. They are the privileged units by which each node try to create a link. By making the probabilities also depending on the local D error (Second ENG Model), we obtain a structure more GNG-like, i.e. more gas-like. In fact the conditions to create a new link get much more restrictive, which thing consequently makes the interaction between any of the subset of units and the rest of the net decreasing. So the structure of links seems to extend more uniformly along the region where the inputs are defined, how it is shown in the b figure (more patent in the ring distribution of inputs). In the 5 figure it is represented the dynamics of the populations of the two ENG models. In the first model, the population size seems to converge at the final value equal to.7n max, so confirming Annunziato and Pizzuti experimental outcomes. 1 1 network size network size epochs epochs Fig. 5 Network size evolution of the two ENG models (first input manifold) Considering that d tends to gradually decrease during the training, how fig. 6 points out, the influence of the (1-D min /d) factor tends instead to increase so reducing the effects of the negative effects of the [5] and [6] formulae. It justifies the sudden population increase at the training final steps in the second model. 5

9 D D epochs epochs Fig. 6 Average D error of the two ENG models (first input manifold) The sudden increase of units can be mainly recorded around the winners (which get a low d). It means that at the final steps of training new units and links keep on growing around the winners, but not among the subgroups of units which became more and more isolated. If we visualize the phase plane (X(t),X(t+1)), we can immediately notice that the punctiform characteristics of the attractor become more marked in the second model. It means that the system tends to mostly converge at a fixed final state. Such behaviour is just due to the scarce interaction among the groups of the net units at the final stages of training. Fig. 7 Population dynamics (X(t),X(t+1)) of the two ENG models (first input manifold). 51

10 Conclusions : a physical picture Future Developments The algorithm here developed essentially represents a process of experimental selection analogous with the selection phase in Edelman s model (9, 1). The population evolution of nodes is led by an evolutive strategy which does not use the classical learning rules such as the Hebb law (11). The adopting of a discrete probabilistic model finds its motive in the recent experimental studies showing how groups of neuronal units get wide functional versatility in responding to different stimuli and, vice versa, same stimuli give rise to extremely diversified responses. It points out that a certain random component of the cerebral activity is laid on a much more radical level than noise and it need a class of models different from the deterministic traditional - linear and non ones (1). The ENG algorithm lets groups of units to form, but does not integrate and synchronize their activity. So the system behaves like a gas made by neuronal molecules weakly interacting each others. The problem of adopting a winner-takes-all strategy is that it is so favoured a process of information localizing, which is totally different from the Edelman dynamic nucleus hypothesis. A dynamic nucleus is a process, not a specified net element and it is defined by neural interactions rather than by specific localization. In the ENG model is quite easy to get the formation of the dynamic nuclei by connecting selected winners by the diffusion on the activation signal. Such solution favours the synchronization of the activities of the groups of the net units. In consonance with the gas metaphor, we can say that the activation signal crystallizes the system around the original scale-free structure, increasing the co-operative integration and the neural complexity essential to the autopoietic large-scale processes of the neural activity. The authors thank their old pal mousy for the linguistic revision. References 1. Kohonen T. Self-organized Formation of Topologically Correct Feature Maps, Biological Cybernetics, 198;43: Martinetz TM, Schultzen KJ. Topology Representing Networks, Neural Networks, 1994:7(3); Fritzke B. Growing Cell Structures A Self-Organizing Network for Unsupervised and Supervised Learning, Neural Networks, 1994;7(9): Delaunay B. Bullettin of the Academy of Sciences USSR, 1934;VII: Chialvo DR, Bak P. Learning From Mistakes, Neuroscience, 1999;9(4): Steyvers M, Tenenbaum J. The Large-Scale Structure of Semantic Networks. Working draft submitted to Cognitive Science, 1 7. Albert R, Barabasi A. Topology of Evolving Networks: Local Events and Universality, Physical Review Letters, ;85: Annunziato M, Pizzuti S. Adaptive Parametrization of Evolutionary Algorithms Driven by Reproduction and Competition, Proceedings of ESIT, Aachen, Germany. 9. Edelman GM. Group Selection and Phasic Reentrant Signaling: A Theory of Higher Brain Function, in The Mindful Brain (eds Edelman G.M. and Mountcastle V.), Cambridge: MIT Press;1978: Edelman GM. Neural Darwinism: The Theory of Neuronal Group Selection, New York: Basic Books;1987 5

11 11. Martinetz TM. Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps, ICANN 93: International Conference on Artificial Neural Networks, Amsterdam: Springer; 1993; Zbilut JP. Unstable Singularities and Randomness, Elsevier Books; 4 53