Presentation on theme: "Neural Representation, Embodied and Evolved Pete Mandik Chairman, Department of Philosophy Coordinator, Cognitive Science Laboratory William Paterson University,"— Presentation transcript:
Neural Representation, Embodied and Evolved Pete Mandik Chairman, Department of Philosophy Coordinator, Cognitive Science Laboratory William Paterson University, New Jersey USA
2 Abstract: What could representational content be such that appeal to it can be explanatory? I tackle such questions by addressing how representations that explain intelligent behavior might be acquired through processes of Darwinian evolution. I present the results of computer simulations of evolved neural network controllers and discuss the similarity of the simulations to real world examples of neural network control of animal behavior. I argue that focusing on the simplest cases of evolved intelligent behavior, in both simulated and real organisms, reveals that evolved representations must carry information about the creatures’ environments and further can do so only if their neural states are appropriately isomorphic to environmental states. Further, these informational and isomorphism relations are what are tracked by content attributions in folk-psychological and cognitive scientific explanations of these intelligent behaviors.
3 Mental reps in folk-psych George is opening the fridge because: George desires that he drinks some beer George sees that the fridge is in front of him George remembers that he put some beer in the fridge n George’s psychological states cause his behavior n George’s psychological states have representational content
4 Chemotaxis is a representation-hungry problem Sensor Brain Steering Muscles 1-Sensor Creature n left/right stimulus location underdetermined by sensor activity n only proximity directly perceived n Adding memory can help compute gradient
5 Single sensor chemotaxis from the point of view of folk-psychology Suppose you are literally in a fog so dense that while you can ascertain how dense it is where you are, you cannot ascertain in which direction the fog gets less dense. After walking for a while you notice that the fog is much less dense than it was previously. By comparing your current perception of a less dense fog to your memory of a more dense fog, you to infer that you are moving out of the area of greatest concentration.
6 Chemotaxis in Caenorhabditis Elegans Effectively utilizing only a single sensor Orientation network contains reciprocal connections, possibly implementing memory sufficient for computing the time derivative of the sensor activity
7 Artificial Life Experiment n Experimental Set Up u 3 orientation networks: Feed- forward, Recurrent, and Blind u Five runs each, for 240 million steps u Mutations allowed only for neural weights u Fitness defined as lifetime distance u Initial weights: Evolved CPGs with un-evolved (zero weights) orienting networks
8 Synthetic C. Elegans. On the left, front view. On the right, top view.
9 Neural network for the synthetic C. Elegans. Neurons include one sensor (s) and several motor neurons (m) and interneurons (i). Single-headed arrows indicate flow of information from one neuron to the next. A double-headed arrow between two neurons indicates both a feed-forward and a feedback connection between them.
10 Results of the experiment comparing recurrent, feed-forward, and blind networks in an evolutionary simulation of chemotaxis. Results
11 Discussion Worms without recurrent connections were conferred no advantage by sensory input. Without the recurrent connections to constitute a memory, the worms are missing a crucial representation for the computation of the change of the local concentration over time.
12 Heading in the gradient is determined by a computation that takes as inputs both a sensory representation that encodes information about the current local concentration and a memory representation that encodes information about the past local concentration. The existence of a memory mechanism was predicted by the folk psychological explanation and supported by the simulation experiments.
13 What the representations are sensory representations - states of activations in the chemo-sensory input neuron memory representations - signals conveyed along recurrent connections motor representations - states of activation in neurons that output to muscles.
14 What is represented In the sensory case: current local concentration. In the memory case: past local concentration. In the motor case: level of muscular contraction
15 In what consists representing The correlating causally of elements in isomorphic structures (like mercury column heights and temperatures)
16 The sensory and memory states are able drive successful chemotaxis in virtue of the informational relationships that they enter into with current and past levels of local chemical concentration, but they are able to enter into those informational relations because of their participation in isomorphsims between structures defined by ensembles of neural states and structures defined by ensembles of environmental states. In brief, in order to have representational contents that they have they must carry the information that they do and in order to carry the information that they do they must enter into the isomorphisms that they do. The evolvablity of information bearing states is due to the isomorphisms of their embedding structures.