A brain can perform tasks more complex than the sum of the brain parts involved We measure this difference (Phi) Phi correlates well with fitness / evolution (Edlund 2011) Fitness implies minimally required Phi Only engineered or unrealistic systems have been measured Phi could be used to quantitatively diagnose brain states (Fig.1) Here we measure Phi of a detailed model organism interacting with a fully modelled physical environment There are several potential challenges Will the temporal effect of Phi be problematic? We expect a Minimum Phi Fitness effect. Is this true? Toward the Measure of a Brain J. Schossau, C. Adami - Department of Computer Science & Engineering - Michigan State University Conclusions The measure called Phi could be useful to aid basic research on cognition and consciousness or help doctors quantitatively diagnose states of mental existence. However, complexity of performing this measure mandates a controlled environment and neuron-precision measurements. We've shown the first application of this measure to a sufficiently realistic simulation of a highly detailed modelled forward locomoting C. elegans and realistic environment. This work is the first step toward making the detailed version of Phi measure a biological reality and we validate the model by comparison to previous work. Introduction 1 Used a detailed differential equation based neurological model of C. elegans forward locomotion by Jan Karbowski et al Results All double and single ablations collectively show the characteristic Minimum Phi Fitness effect (red line). The system performs similarly to other systems that are a product of evolution. There is no large temporal effect on measuring Phi in a real system, which was originally thought to be biggest problem. Phi as a measure of information integration can be useful in many other contexts, such as evolutionary computation. For instance, we're studying Phi's effect on evolvability concerning a primary fitness criterion. Another direct application of Phi which we're pursuing is to aid in developing technology to automatically detect breast cancer. Fig. 1 Consciousness is not on or off, but a gradient. Phi could help us quantitatively measure this gradient. (Noirhomme 2010) Brain death Coma VS MCS LIS Fig. 2 Original model by Karbowski. Continuoum of Consciousness Fig. 3 Recreated DiffEQ results. Muscle activations over time. Fig. 4 Ablated Neurons to test Phi measure and behavioral effect. 3 Created a fully 3-D virtual world and an articulated virtual worm body with muscles to enable environmental interaction driven by the mathematical neural model 2 Implemented this model as a computational simulation and recreated their results 4 Recorded neural activity as the worm wiggled through the environment, then calculated Phi from the recordings Fig. 4 Initial 3-D virtual worm. 5 Performed neural ablation experiments (knockouts) to validate experimental setup. We expected the resulting diversity of architectures to exhibit the Minimum Phi Fitness effect. (see Fig. 5) Fig. 5 Current 3-D virtual worm. Delta Distance Fig. 5 Minimum Phi Fitness effect 0 Delta Phi Each dot represents one replicate from all neural ablation experimental conditions. Some are single ablation and others are double ablations. The distribution shows that better task ability requires a minimum of ability to integrate information (phi) and suggests the system is a reasonable representation of an evolved decision-making network. (Edlund 2011) [ Jory @ msu . edu ] Thanks To BEACON — Paul Allen Foundation — ICER Methods