Aktive computation in offenen Systemen: Lerndynamiken in biologischen Systemen: vom Netzwerk zum Organismus

In this thesis the action-perception-cycle, which is inherent in every cognitive system, is investigated by several special examples. These examples are taken from different levels of description and are biological motivated. Moreover, a novel Hebb-like learning rule for neural networks is introduced, which has not only interesting features by itself but establishes a connection to the action-perception-cycle and demonstrates the interplay between different levels of description. The novel Hebb-like learning rule is introduced in chapter 3. The learning rule is based on the introduction of a new degree of freedom, assigned to each neuron in form of a neuron counter. In combination with an external reinforcement signal the synapses which are involved in bad computations are allowed to change according to a stochastic update mechanism which selects synapses according to the counter states of the surrounding neuron counters. The features of the Hebb-like learning rule are investigated in a multilayer neural network which has to learn the exclusive-or (XOR) problem. Different forms of network dynamics are investigated and the results are compared to another Hebb-like learning rule which was introduced by [Klemm et al., 2000]. The results obtained by numerical simulations give similar good results as [Klemm et al., 2000] but under usage of less free parameters. Moreover, a qualitative biological interpretation in form of heterosynaptic long-term potentiation (LTD) can be given. In chapter 4 a method is developed which allows the assignment of an approximative temperature to a noisy winner-take-all mechanism similar to a temperature in a softmax mechanism. In the second part of the chapter this method is used to assign an approximative temperature at each time step to a multilayer neural network which is trained to learn the exclusive-or (XOR) problem. Results show that the time course of the temperature is annealed the better the neural network solves the problem. This indicates that there is a correspondence between the time course of mean error and the approximative temperature which is annealed in a selforganized way without any additional scheduling besides the learning rule to train the network. In chapter 5 the generic character of the novel Hebb-like learning rule is demonstrated by training a class of neural networks to learn the problem of timing. The class of neural networks is given by an algorithm of [Watts und Strogatz, 1998] and includes small world networks. Moreover, the influence of the action-selection-mechanism on the convergence time of the neural networks and the interplay with the learning rule is investigated. This establishes a connection to the action-perception-cycle. Besides this, a new error measure for an outer observer is introduced to strictly separate information of the neural network and the outer observer. The work is completed in chapter 6 by an investigation of an agent in a space like environment. The mathematical model which describes the action-perception-cycle of the agent in a one dimensional grid is presented in detail. Especially the action-selection-mechanisms used and a measure for an outer observer, similar to chapter 5, are thoroughly introduced and discussed. Results show that different kinds of action-selection-mechanisms lead to great differences in the convergence times although all parts of the whole system are identical besides the action-selection-mechanism used. The chapter ends with a biological interpretation of the presented model which can partly be related to the consilidation process of spacial memories in the hippocampus and the neuroanatomical structures therein. Aktive Computation in offenen Systemen Lerndynamiken in biologischen Systemen: Vom Netzwerk zum Organismus Dissertation zur Erlangung des Grades Doktor der Naturwissenschaften