2,920 research outputs found
Semantic learning in autonomously active recurrent neural networks
The human brain is autonomously active, being characterized by a
self-sustained neural activity which would be present even in the absence of
external sensory stimuli. Here we study the interrelation between the
self-sustained activity in autonomously active recurrent neural nets and
external sensory stimuli.
There is no a priori semantical relation between the influx of external
stimuli and the patterns generated internally by the autonomous and ongoing
brain dynamics. The question then arises when and how are semantic correlations
between internal and external dynamical processes learned and built up?
We study this problem within the paradigm of transient state dynamics for the
neural activity in recurrent neural nets, i.e. for an autonomous neural
activity characterized by an infinite time-series of transiently stable
attractor states. We propose that external stimuli will be relevant during the
sensitive periods, {\it viz} the transition period between one transient state
and the subsequent semi-stable attractor. A diffusive learning signal is
generated unsupervised whenever the stimulus influences the internal dynamics
qualitatively.
For testing we have presented to the model system stimuli corresponding to
the bars and stripes problem. We found that the system performs a non-linear
independent component analysis on its own, being continuously and autonomously
active. This emergent cognitive capability results here from a general
principle for the neural dynamics, the competition between neural ensembles.Comment: Journal of Algorithms in Cognition, Informatics and Logic, special
issue on `Perspectives and Challenges for Recurrent Neural Networks', in
pres
Attractor Metadynamics in Adapting Neural Networks
Slow adaption processes, like synaptic and intrinsic plasticity, abound in
the brain and shape the landscape for the neural dynamics occurring on
substantially faster timescales. At any given time the network is characterized
by a set of internal parameters, which are adapting continuously, albeit
slowly. This set of parameters defines the number and the location of the
respective adiabatic attractors. The slow evolution of network parameters hence
induces an evolving attractor landscape, a process which we term attractor
metadynamics. We study the nature of the metadynamics of the attractor
landscape for several continuous-time autonomous model networks. We find both
first- and second-order changes in the location of adiabatic attractors and
argue that the study of the continuously evolving attractor landscape
constitutes a powerful tool for understanding the overall development of the
neural dynamics
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
Generating functionals for autonomous latching dynamics in attractor relict networks
Coupling local, slowly adapting variables to an attractor network allows to destabilize all attractors, turning them into attractor ruins. The resulting attractor relict network may show ongoing autonomous latching dynamics. We propose to use two generating functionals for the construction of attractor relict networks, a Hopfield energy functional generating a neural attractor network and a functional based on information-theoretical principles, encoding the information content of the neural firing statistics, which induces latching transition from one transiently stable attractor ruin to the next. We investigate the influence of stress, in terms of conflicting optimization targets, on the resulting dynamics. Objective function stress is absent when the target level for the mean of neural activities is identical for the two generating functionals and the resulting latching dynamics is then found to be regular. Objective function stress is present when the respective target activity levels differ, inducing intermittent bursting latching dynamics
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