One of the first challenges in understanding neural information processing is the identification of the functional roles of neural network elements. Aiming at this goal, lesion studies have been classically used in neuroscience, most of which have employed single lesions which are limited in their ability to reveal the significance of interacting elements. The recently developed Functional Contribution Analysis (FCA) method has addressed the functional localization challenge by analyzing data composed of multiple lesioning experiments and corresponding functional performance levels, using an operative minimization approach. This paper presents the Multi-lesion Shapley value Ana/ysis (MSA), an axiomatic, scalable and rigorous method for deducing causal function localization from multiple lesioning data, overcoming several shortcomings of the FCA. The MSA, based on fundamental concepts from game theory, accurately quantifies the contributions of network elements and their interactions. While the original game theoretical definition and calculation of the Shapley value requires a data set of a potentially vast number of all multiple lesion experiments, we developed several MSA prediction and estimation variants which use only a relatively small set of experiments. The successful working of the MSA is demonstrated in a theoretical test case, in artificially evolved neurocontrollers and for the analysis of an example of biological, reversible deactivation data. MSA has a wide range of potential applications in neuroscience for the analysis of reversible deactivation experiments and transcranial magnetic stimulation "virtual lesions", and in biology in general, for the analysis of gene networks via "multi-knockout" experiments.

Abstract. The artificial life approach to evolutionary robotics is used as a fundamental framework for the development of a modular neural control of autonomous mobile robots. The applied evolutionary technique is especially designed to grow different neural structures with complex dynamical properties. This is due to a modular neurodynamics approach to cognitive systems, stating that cognitive processes are the result of interacting dynamical neuro-modules. The evolutionary algorithm is described, and a few examples for the versatility of the procedures are given. Besides solutions for standard tasks like exploration, obstacle avoidance and tropism, also the sequential evolution of morphology and control of a biped is demonstrated. A further example describes the co-evolution of different neuro-controllers co-operating to keep a gravitationally driven art-robot in constant rotation.

Abstract One of the major challenges in the field of neurally driven evolved autonomous agents is deciphering the neural mechanisms underlying their behavior. Aiming at this goal, we have developed the multi-perturbation Shapley value analysis (MSA)—the first axiomatic and rigorous method for deducing causal function localization from multiple-perturbation data, substantially improving on earlier approaches. Based on fundamental concepts from game theory, the MSA provides a formal way of defining and quantifying the contributions of network elements, as well as the functional interactions between them. The previously presented versions of the MSA require full knowledge (or at least an approximation) of the network’s performance under all possible multiple perturbations, limiting their applicability to systems with a small number of elements. This article focuses on presenting new scalable

this paper, we present a new, Informational Lesiontrig Method (ILM) which views a lesion as a noisy channel and applies controlled lesion to the network by varying the lesioning level from large to arbitrarily small magnitudes. Studying the ILM within the FCA framework, our main results are threefold: First, that lower lesioning levels permit more accurate FCA predictions. Second, that the usage of minute ILM lesioning levels can uncover the long-term effects of elements on the network's functioning. And third, that as the lesioning level decreases, the contribution values tend to approach limit values, reflecting the importance of these elements in the intact, normal functioning neurocontroller

This paper investigates the evolution of autonomous agents that solve a memorydependent counting task. Two types of neurocontrollers are evolved: networks of McCulloch-Pitts neurons, and spiking Integrate-And-Fire networks. The results demonstrate the superiority of the spiky model in terms of evolutionary success and network simplicity. The combination of spiking dynamics with incremental evolution leads to the successful evolution of agents counting over very long periods. Analysis of the evolved networks unravels the counting mechanism and demonstrates how the spiking dynamics are utilized. Using new measures of spikiness we find that even in agents with spiking dynamics, these are usually truly utilized only when they are really needed, i.e., in the evolved subnetwork responsible for counting.

Spiky neural networks are widely used in neural modeling, due to their biological relevance and high computational power. In this paper we investigate the usage of spiking dynamics in embedded artificial neural networks, that serve as a control mechanism for evolved autonomous agents performing a delayed-response task. The synaptic weights and spiking dynamics are evolved using a genetic algorithm. We compare evolved spiky networks with evolved McCulloch-Pitts networks, while confronting new questions about the nature of "spikiness" and its contribution to the neurocontroller's processing. On the behavioral level, we show that in a memory-dependent task, network solutions that incorporate spiking dynamics can be less complex and easier to evolve than neurocontrollers involving McCulloch-Pitts neurons. On the functional level, we identify and rigorously characterize two distinct properties of spiking dynamics in embedded agents: spikiness evident influence and spikiness functional contribution.

Identifying the functional roles of elements of a neural network is one of the first challenges in understanding neural information processing. Aiming at this goal, lesion studies have been used in neuroscience, most of which employing single lesions and hence, limited in their ability to reveal the significance of interacting elements. This paper presents the Multi-lesion Shapley value Analysis (MSA), an axiomatic, scalable and rigorous method, addressing the challenge of calculating the contributions of network elements from a multi-lesion data set. The successful workings of the MSA are demonstrated on artificial and biological data. MSA is a novel method for causal function localization, with a wide range of potential applications for the analysis of reversible aleactivation experiments and TMS-induced "virtual lesions".

The analysis of previously evolved rhythmic asynchronous random Boolean networks (Di Paolo, 2001) reveals common topological characteristics indicating that rhythm originates from a circular functional structure. The rhythm generating core of the network has the form of a closed ring which operates as a synchronization substrate by supporting a travelling wave of state change; the size of the ring corresponds well with the period of oscillation. The remaining nodes in the network are either stationary or follow the activity of the ring without feeding back into it so as to form a coherent whole. Rings are typically formed early on in the evolutionary search process. Alternatively, long chains of nodes are favoured before they close upon themselves to stabilize. Analysis of asynchronous networks with de-correlated (non-rhythmic, non-stationary) attractors reveals no such common topological characteristics. These results have been obtained using statistical analysis and a specifically developed bottom-up pruning algorithm. This algorithm works from local interactions to global configuration by eliminating redundant links. The suitability of the algorithm has been confirmed by both numerical and single lesion analysis. The ring topology solution for the generation of rhythm implies that it will be harder to evolve rhythmic networks for big sizes and small periods and for bigger number of connections per node. These trends are confirmed empirically. Finally, the identified mechanisms are utilized to handcraft rhythmic networks of different periods showing that a low number of connections suffices for a large variety of rhythms. Random asynchronous update forces the evolved solutions to be highly robust maintaining their performance in the presence of intrinsic noise. The biological implications of such robust designs for molecular clocks are discussed.

Abstract — The current work addresses the development of cognitive abilities in artificial organisms, a topic that has attracted many research efforts recently. In our approach, neural networkbased agent structures are employed to represent distinct brain areas. We introduce a Hierarchical Collaborative CoEvolutionary (HCCE) approach to design autonomous, yet cooperating agents. Thus, partial brain models consisting of many substructures can be designed. Replication of lesion studies is used as a means to increase reliability of brain model, highlighting the distinct roles of agents. The HCCE is appropriately designed to support systematic modelling of brain structures, able to reproduce biological lesion data. The proposed approach designs cooperating agents properly, by considering the desired pre- and post- lesion performance of the model. The effectiveness of the proposed approach is illustrated on the design of a computational model of Primary Motor cortex and Premotor cortex interactions in the mammalian brain. The model is successfully tested in driving a simulated robot, with different pre- and post- lesion performance. I.

Abstract- The current work addresses the development of cognitive abilities in artificial organisms. In the proposed approach, neural network-based agent structures are employed to represent distinct brain areas. We introduce a Hierarchical Collaborative CoEvolutionary (HCCE) approach to design autonomous, yet cooperating agents. Thus, partial brain models consisting of many substructures can be designed. Replication of lesion studies is used as a means to increase reliability of brain model, highlighting the distinct roles of agents. The HCCE is appropriately designed to support systematic modelling of brain structures, able to reproduce biological lesion data. The proposed approach effectively designs cooperating agents by considering the desired pre- and post- lesion performance of the model. In order to verify and asses the implemented model, the latter is embedded in a robotic platform to facilitate its behavioral capabilities. 1