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Talk by Ross Gayler for the Redwood Center for Theoretical Neuroscience at UC Berkeley. ABSTRACT. This talk is about computing with discrete compositional data structures in analog computers. A core issue for both computer science and cognitive neuroscience is the degree of match between a class of computer designs and a class of computations. In cognitive science, it is manifested in the apparent mismatch between the neural network hardware of the brain (essentially, a massively parallel analog computer) and the computational requirements of higher cognition (statistical constraint processing with compositional discrete data structures to implement facilities such as language and analogical reasoning). Historically, analog computers have been considered ill-suited for implementing computational processes on discrete compositional data structures. Neural networks can be construed as analog computers -- a class of computer design with a long history, but now relatively unknown. Historically, analog computation had advantages over digital computation in speed and parallelism. Computational problems were cast as dynamical systems and modelled by differential equations, which was relatively straightforward for models of physical problems such as flight dynamics. However, it was far less clear how to translate computations on discrete compositional data structures such as trees and graphs into dynamical systems. This is especially true for problems where the data structures evolve over time, implying the need to rewire the analog computer on the fly. This is particularly relevant to cognitive science because new concepts and relations can be created on the fly, and under the standard conception of neural networks this implies that neurons and connections would be created impossibly rapidly. In this talk I describe Vector Symbolic Architectures, a family of mathematical techniques for analog computation in hyperdimensional vector spaces that map naturally onto neural network implementations. VSAs naturally support computation on discrete compositional data structures and a form of virtualisation that breaks the nexus between the items to be represented and the hardware that supports the representation. This means that computations on evolving data structures do not require physical rewiring of the implementing hardware. I illustrate this approach with a VSA system that finds isomorphisms between graphs and where different problems to be solved are represented by different initial states of the fixed hardware rather than by rewiring the hardware. Graph isomorphism is at the heart of the standard model of analogical reasoning and motivates this example, although that aspect is not explored in this talk. BIO. Ross Gayler has PhD in psychology from University of Queensland, with a long and far-reaching interest in the mysteries of cognitive computing. His day job is as a statistician in the finance industry.

Talk by Ross Gayler for the Redwood Center for Theoretical Neuroscience at UC Berkeley. ABSTRACT. This talk is about computing with discrete compositional data structures in analog computers. A core issue for both computer science and cognitive neuroscience is the degree of match between a class of computer designs and a class of computations. In cognitive science, it is manifested in the apparent mismatch between the neural network hardware of the brain (essentially, a massively parallel analog computer) and the computational requirements of higher cognition (statistical constraint processing with compositional discrete data structures to implement facilities such as language and analogical reasoning). Historically, analog computers have been considered ill-suited for implementing computational processes on discrete compositional data structures. Neural networks can be construed as analog computers -- a class of computer design with a long history, but now relatively unknown. Historically, analog computation had advantages over digital computation in speed and parallelism. Computational problems were cast as dynamical systems and modelled by differential equations, which was relatively straightforward for models of physical problems such as flight dynamics. However, it was far less clear how to translate computations on discrete compositional data structures such as trees and graphs into dynamical systems. This is especially true for problems where the data structures evolve over time, implying the need to rewire the analog computer on the fly. This is particularly relevant to cognitive science because new concepts and relations can be created on the fly, and under the standard conception of neural networks this implies that neurons and connections would be created impossibly rapidly. In this talk I describe Vector Symbolic Architectures, a family of mathematical techniques for analog computation in hyperdimensional vector spaces that map naturally onto neural network implementations. VSAs naturally support computation on discrete compositional data structures and a form of virtualisation that breaks the nexus between the items to be represented and the hardware that supports the representation. This means that computations on evolving data structures do not require physical rewiring of the implementing hardware. I illustrate this approach with a VSA system that finds isomorphisms between graphs and where different problems to be solved are represented by different initial states of the fixed hardware rather than by rewiring the hardware. Graph isomorphism is at the heart of the standard model of analogical reasoning and motivates this example, although that aspect is not explored in this talk. BIO. Ross Gayler has PhD in psychology from University of Queensland, with a long and far-reaching interest in the mysteries of cognitive computing. His day job is as a statistician in the finance industry.

Talk by Ross Gayler for the Redwood Center for Theoretical Neuroscience at UC Berkeley. ABSTRACT. This talk is about computing with discrete compositional data structures in analog computers. A core issue for both computer science and cognitive neuroscience is the degree of match between a class of computer designs and a class of computations. In cognitive science, it is manifested in the apparent mismatch between the neural network hardware of the brain (essentially, a massively parallel analog computer) and the computational requirements of higher cognition (statistical constraint processing with compositional discrete data structures to implement facilities such as language and analogical reasoning). Historically, analog computers have been considered ill-suited for implementing computational processes on discrete compositional data structures. Neural networks can be construed as analog computers -- a class of computer design with a long history, but now relatively unknown. Historically, analog computation had advantages over digital computation in speed and parallelism. Computational problems were cast as dynamical systems and modelled by differential equations, which was relatively straightforward for models of physical problems such as flight dynamics. However, it was far less clear how to translate computations on discrete compositional data structures such as trees and graphs into dynamical systems. This is especially true for problems where the data structures evolve over time, implying the need to rewire the analog computer on the fly. This is particularly relevant to cognitive science because new concepts and relations can be created on the fly, and under the standard conception of neural networks this implies that neurons and connections would be created impossibly rapidly. In this talk I describe Vector Symbolic Architectures, a family of mathematical techniques for analog computation in hyperdimensional vector spaces that map naturally onto neural network implementations. VSAs naturally support computation on discrete compositional data structures and a form of virtualisation that breaks the nexus between the items to be represented and the hardware that supports the representation. This means that computations on evolving data structures do not require physical rewiring of the implementing hardware. I illustrate this approach with a VSA system that finds isomorphisms between graphs and where different problems to be solved are represented by different initial states of the fixed hardware rather than by rewiring the hardware. Graph isomorphism is at the heart of the standard model of analogical reasoning and motivates this example, although that aspect is not explored in this talk. BIO. Ross Gayler has PhD in psychology from University of Queensland, with a long and far-reaching interest in the mysteries of cognitive computing. His day job is as a statistician in the finance industry.

Talk by Ross Gayler for the Redwood Center for Theoretical Neuroscience at UC Berkeley. ABSTRACT. This talk is about computing with discrete compositional data structures in analog computers. A core issue for both computer science and cognitive neuroscience is the degree of match between a class of computer designs and a class of computations. In cognitive science, it is manifested in the apparent mismatch between the neural network hardware of the brain (essentially, a massively parallel analog computer) and the computational requirements of higher cognition (statistical constraint processing with compositional discrete data structures to implement facilities such as language and analogical reasoning). Historically, analog computers have been considered ill-suited for implementing computational processes on discrete compositional data structures. Neural networks can be construed as analog computers -- a class of computer design with a long history, but now relatively unknown. Historically, analog computation had advantages over digital computation in speed and parallelism. Computational problems were cast as dynamical systems and modelled by differential equations, which was relatively straightforward for models of physical problems such as flight dynamics. However, it was far less clear how to translate computations on discrete compositional data structures such as trees and graphs into dynamical systems. This is especially true for problems where the data structures evolve over time, implying the need to rewire the analog computer on the fly. This is particularly relevant to cognitive science because new concepts and relations can be created on the fly, and under the standard conception of neural networks this implies that neurons and connections would be created impossibly rapidly. In this talk I describe Vector Symbolic Architectures, a family of mathematical techniques for analog computation in hyperdimensional vector spaces that map naturally onto neural network implementations. VSAs naturally support computation on discrete compositional data structures and a form of virtualisation that breaks the nexus between the items to be represented and the hardware that supports the representation. This means that computations on evolving data structures do not require physical rewiring of the implementing hardware. I illustrate this approach with a VSA system that finds isomorphisms between graphs and where different problems to be solved are represented by different initial states of the fixed hardware rather than by rewiring the hardware. Graph isomorphism is at the heart of the standard model of analogical reasoning and motivates this example, although that aspect is not explored in this talk. BIO. Ross Gayler has PhD in psychology from University of Queensland, with a long and far-reaching interest in the mysteries of cognitive computing. His day job is as a statistician in the finance industry.

In this time period, previous work on the construction of an oscillating neural network 'computer' that could recognize sequences of characters of a grammar was extended to employ selective 'attentional' control of synchronization to direct the flow of communication and computation within the architecture. This selective control of synchronization was used to solve a more difficult grammatical inference problem than we had previously attempted. Further performance improvement was demonstrated by the use of a temporal context hierarchy in the hidden and context units of the architecture. These form a temporal counting hierarchy which allows representations of the input variations to form at different temporal scales for learning sequences with with long temporal dependencies. We further explored the analog system identification capabilities of these systems where the output modules take on analog values. We were able to learn a mapping from the acoustic cepstral values of speech to articulatory parameters such as jaw and lip movement. This is a model speech processing problem which allows us to test the usefulness of our systems for speech recognition preprocessing.

This report describes the application of a neural computing approach for automated initial identification of seismic phases (P or S) recorded by 3- component stations. We use a 3-layer back-propagation neural network to identify phases on the basis of their polarization attributes. This approach is much easier to develop than a more traditional rule-based system because of the high-dimensionality of the input (8-10 polarization attributes), and because the data are station-dependent. The neural network approach also performs 3-7% better than a linear multivariate method. Most of the gain is for signals with low signal-to-noise ratio since the non-linear neural network classifier is less sensitive to outliers (or noisy data) than the linear multivariate method. Another advantage of the neural network approach is that it is easily adapted to data recorded by new stations. For example, we find that we achieve 75-80% identification accuracy for a new station without system retraining (e.g., using a network derived from data from a different station). The data required for retraining can be accumulated in about two weeks of continuous operation of the new station, and training takes less than one hour on a Sun4 Sparc station. After this retraining, the identification accuracy increases to 90%. We have recently added context (e.g., the number of arrivals before and after the arrival under consideration) to the input of the neural network, and we have found that this further improves the identification accuracy by 3-5%. This neural network approach performs better than competing technologies for automated initial phase identification, and it is amenable to machine-learning techniques to automate the process of acquiring new knowledge.