Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Synchronization of pulse-coupled biological oscillators
SIAM Journal on Applied Mathematics
Image segmentation based on oscillatory correlation
Neural Computation
Collective excitation phenomena and their applications
Pulsed neural networks
Modular and hierarchical learning systems
The handbook of brain theory and neural networks
Synchronization of neuronal responses as a putative binding mechanism
The handbook of brain theory and neural networks
Synchrony and desynchrony in integrate-and-fire oscillators
Neural Computation
Semantic Networks in Artificial Intelligence
Semantic Networks in Artificial Intelligence
The constructivist learning architecture: a model of cognitive development for robust autonomous robots
Dynamics of Strongly Coupled Spiking Neurons
Neural Computation
IEEE Transactions on Neural Networks
Grouping synchronization in a pulse-coupled network of chaotic spiking oscillators
IEEE Transactions on Neural Networks
Neural mechanisms of the mind, Aristotle, Zadeh, and fMRI
IEEE Transactions on Neural Networks
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This paper presents a new approach to higher-level information fusion in which knowledge and data are represented using semantic networks composed of coupled spiking neuron nodes. Networks of simulated spiking neurons have been shown to exhibit synchronization, in which sub-assemblies of nodes become phase locked to one another. This phase locking reflects the tendency of biological neural systems to produce synchronized neural assemblies, which have been hypothesized to be involved in binding of low-level features in the perception of objects. The approach presented in this paper embeds spiking neurons in a semantic network, in which a synchronized sub-assembly of nodes represents a hypothesis about a situation. Likewise, multiple synchronized assemblies that are out-of-phase with one another represent multiple hypotheses. The initial network is hand-coded, but additional semantic relationships can be established by associative learning mechanisms. This approach is demonstrated by simulation of proof-of-concept scenarios involving the tracking of suspected criminal vehicles between meeting places in an urban environment. Our results indicate that synchronized sub-assemblies of spiking nodes can be used to represent multiple simultaneous events occurring in the environment and to effectively learn new relationships between semantic items in response to these events. In contrast to models of synchronized spiking networks that use physiologically realistic parameters in order to explain limits in human short-term memory (STM) capacity, our networks are not subject to the same limitations in representational capacity for multiple simultaneous events. Simulations demonstrate that the representational capacity of our networks can be very large, but as more simultaneous events are represented by synchronized sub-assemblies, the effective learning rate for establishing new relationships decreases. We propose that this effect could be countered by speeding up the spiking dynamics of the networks (a tactic of limited availability to biological systems). Such a speedup would allow the number of simultaneous events to increase without compromising the learning rate.