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BICS 2010 INFORMATIONAL THEORIES OF CONSCOUSNESS: A REVIEW AND EXTENSION Igor Aleksander and David Gamez Imperial College London 1 Tononi, G. Consciousness as Integrated Information: a Provisional . Manifesto. Biological Bulletin 215: 2008. pp 216-242 INFORMATIONAL THEORIES OF CONSCOUSNESS: A REVIEW AND EXTENSION Illustrating how (according to Tononi) does a neural network integrate information? An alternative perspective from neural automata theory Now for some pictures: When we look at them we can assume that we can say that we are conscious of them We can consciously differentiate between the last two images. What about the next two? Not so easy, even if the two are orthogonally different. Computational distinctions 1. AI A semantic net breaks the scene down into a) Very clever semantic net: Italian elements. restaurant … music We become conscious of the images first b) Greek restaurant … music and then might start decomposing them. c) ??? A semantic net is not a useful (predictive) d) ???of what it is to be conscious. model Computational distinctions 2. NON-RECURSIVE NEURAL NETWORKS a) Hamming similarity to a training pattern b) Hamming similarity to a training pattern c) . . . Sensisive to a strong difference d) . Can’t apply static neural theory to the brain. Computational distinctions 3. ATTRACTOR NEURAL NETWORKS a) Hamming similarity: training attractor b) Hamming similarity: training attractor c) . . . Arbitrary attractors d) . A possible route but the states need to have special characteristics. MORE ON DIFFERENTIATION Information: resolution of uncertainty Generate s a vast number of bits Generate s one bit Information integration theory (IIT) IIT argues that consciousness is the capacity of a network of neurons (or other elements) to detect causal relationships. This is called INFORMATION INTEGRATION and is given the symbol Φ MORE ON CAUSAL RELATIONSHIPS AND Φ Can detect causal relationships Φ 0 Cannot detect causal relationships Φ=0 Another example: the difference between what supports the sensation of a visual scene and the way it’s viewed by a digital camera? MORE ON CAUSAL RELATIONSHIPS AND Φ If these are normal mental states the system should not automatically treat those below as normal Φ= Interim Recap: the machine support for a mental state must … Ensure differentiation from other mental states – i.e. maximise information generation by resolving uncertainty (uniqueness). Ensure integration – i.e. maintain causal relationships between elements of a single sensory experience (indivisibility). Calculating the amount of integration Φ network consider an arbitrary partition Calculating the amount of integration Φ NOISE (max entropy) network Measure effective information (entropy of resulting states) Calculating the amount of integration Φ network REPEAT. Φ is the sum of the two – a measure of information crossing the cut Problem 1 : The Φ calculation has to be done for all subsets and all cuts in all subsets to discover the least Φ which is the Φ for the whole network. Gamez has shown that to predict the Φ of a 30-neuron network it would take a state-of-the-art computer 1010 years (!) Information integration theory (IIT) COMPLEXES IIT calculations should come up with areas of a network that have a high Φ. These are called complexes. Complexes can shift with time. Consciousness in the brain is thought to exist in a ‘main complex’. Problem 2 : IIT contains statements about qualia, but they seem not to have representational content. Information integration theory (IIT) QUALIA Described mathematically not only by a high Φ, but also by a representation of the activity of all the combinations of sub mechanisms that produce the high Φ. What we are doing about it: Develop IIT using older ‘neural automata’ ideas Desired Output N inputs Output A lookup table A ‘probabilistic nearest neighbour’ lookup table Aleksander: How to build a mind, Columbia UP, 2000 The liveliness model LIVELINESS A simple way of determining clusters with high causal interactions between elements. Parallels discovery of complexes with high Φ Aleksander, I. and Atlas, P. 1973. Cyclic Activity in Nature: Causes of Stability. International Journal of Neuroscience 6: 45-50. The liveliness model 1 0 AND 0 1 An input line is lively if a change in the input is transmitted to the output The liveliness model 1 0 1 AND 0 The liveliness model 0 0 1 AND 0 The liveliness model 0 0 AND 1 NO 0 The liveliness model 1 0 1 AND 0 The liveliness model 1 1 1 AND 1 The liveliness model 1 0 AND 1 NO YE S 1 The liveliness model 1 0 AND 1 1 Neuron Liveliness : Total liveliness of incoming connections. Above case: neuron liveliness = 1 The liveliness model 1 1 AND 1 1 Neuron Liveliness : Total liveliness of incoming connections. NEW STATE: neuron liveliness = ? The liveliness model 1 1 AND 1 1 Neuron Liveliness : Total liveliness of incoming connections. NEW STATE: neuron liveliness = 3 The liveliness model: RECAP 6 The liveliness model CLUSTERS A cluster for a state is the group of lively neurons linked by lively connections. 2 0 All AND Gates 1 2 1 1 1 2 1 2 1 1 The Cluster The liveliness model CLUSTERS A cluster for a state is the group of lively neurons linked by lively connections. Cluster liveliness calculation: 2 0 All AND Gates 0 2 0 0 0 1.8 λc : cluster liveliness 0 62 2 λmax : maximum 0 liveliness of cluster 0 λa : actual liveliness 15 Clusters 121 and value The liveliness model CLUSTERS A cluster for a state is the group of lively neurons linked by lively connections. 2 0 All AND Gates 1 2 λc =1 1.86 1 1 2 1 2 1 1 The Cluster The liveliness model CLUSTERS A cluster for a state is the group of lively neurons linked by lively connections. 0 0 All AND Gates 1 0 0 0 0 0 0 1 0 0 Another state The liveliness model CLUSTERS A cluster for a state is the group of lively neurons linked by lively connections. 1 0 All AND Gates 0 1 0 0 0 1 0 0 0 1 Another state REPRESENTATIONAL ISSUES Iconic learning: forcing reality representing states on a network (for illustration) An example of what happens to state structure when a cluster learns world events (acquires experiential states) GREEN SQUARE 1 1 0 0 Green Square RED CIRCLE World property sensors 0 0 1 1 Red Circle Complete experience (re-entrant states) Resulting logic and λ GC R G 0 11 0 G R 0 11 0 λ= 1 λ= 1 S 00 d 01 0 10 111 d R G 0 11 0 λ 1/2 0 0 1/2 λ= 1 Resulting State structure 00 01 10 01 10 00 00 00 11 11 10 11 01 00 01 01 10 10 11 01 00 10 11 00 00 11 11 10 p=0.5 01 10 01 11 p=0.5 Fully connected GREEN Φ and λ high SQUARE 1 1 0 0 Green Square RED CIRCLE World property sensors 0 0 1 1 Red Circle Complete experience (re-entrant states) Resulting State structure 00 00 11 10 01 00 10 10 10 00 01 10 10 01 11 01 00 10 11 00 00 11 p=1 01 01 11 11 01 11 10 11 00 01 IMPORTANT STUFF Φ, λ are a function of the connectedness and the learning method. Connectedness determines the result of exposure to reality (qualia?). Are Φ, λ useful? FINALLY Effects of integration in a 10,000 neuron iconically trained weightless net. Embodiment of the ‘presence’ axiom Feedback from other neurons at random Samp Weightless neuron les fro m inp ut Iconic training Embodiment of the ‘presence’ axiom A 98 x 98 network with different connection strategies Training Set (Attractors Created) Falling into an attractor Unique Indivisible Able to hold attractor Unable to hold attractor Time progression Loss of Uniqueness Loss of Indivisibility Conclusions Ideas of information integration expand BUT IT’Shorizons SERIOUS the mathematical of FUN neural systems that address consciousness. This both informs and benefits from older (liveliness, iconic learning) methods. A long way to go in the representational arena, in applications to the neurological brain, and the making conscious