151 research outputs found
Entrenched time delays versus accelerating opinion dynamics: are advanced democracies inherently unstable?
Modern societies face the challenge that the time scale of opinion formation
is continuously accelerating in contrast to the time scale of political
decision making. With the latter remaining of the order of the election cycle
we examine here the case that the political state of a society is determined by
the continuously evolving values of the electorate. Given this assumption we
show that the time lags inherent in the election cycle will inevitable lead to
political instabilities for advanced democracies characterized both by an
accelerating pace of opinion dynamics and by high sensibilities (political
correctness) to deviations from mainstream values. Our result is based on the
observation that dynamical systems become generically unstable whenever time
delays become comparable to the time it takes to adapt to the steady state. The
time needed to recover from external shocks grows in addition dramatically
close to the transition. Our estimates for the order of magnitude of the
involved time scales indicate that socio-political instabilities may develop
once the aggregate time scale for the evolution of the political values of the
electorate falls below 7-15 months.Comment: European Physical Journal B (in press
Autonomous Dynamics in Neural networks: The dHAN Concept and Associative Thought Processes
The neural activity of the human brain is dominated by self-sustained
activities. External sensory stimuli influence this autonomous activity but
they do not drive the brain directly. Most standard artificial neural network
models are however input driven and do not show spontaneous activities.
It constitutes a challenge to develop organizational principles for
controlled, self-sustained activity in artificial neural networks. Here we
propose and examine the dHAN concept for autonomous associative thought
processes in dense and homogeneous associative networks. An associative
thought-process is characterized, within this approach, by a time-series of
transient attractors. Each transient state corresponds to a stored information,
a memory. The subsequent transient states are characterized by large
associative overlaps, which are identical to acquired patterns. Memory states,
the acquired patterns, have such a dual functionality.
In this approach the self-sustained neural activity has a central functional
role. The network acquires a discrimination capability, as external stimuli
need to compete with the autonomous activity. Noise in the input is readily
filtered-out.
Hebbian learning of external patterns occurs coinstantaneous with the ongoing
associative thought process. The autonomous dynamics needs a long-term
working-point optimization which acquires within the dHAN concept a dual
functionality: It stabilizes the time development of the associative thought
process and limits runaway synaptic growth, which generically occurs otherwise
in neural networks with self-induced activities and Hebbian-type learning
rules
Self-generated neural activity : models and perspective
Poster presentation: The brain is autonomously active and this self-sustained neural activity is in general modulated, but not driven, by the sensory input data stream [1,2]. Traditionally one has regarded this eigendynamics as resulting from inter-modular recurrent neural activity [3]. Understanding the basic modules for cognitive computation is, in this view, the primary focus of research and the overall neural dynamics would be determined by the the topology of the intermodular pathways. Here we examine an alternative point of view, asking whether certain aspects of the neural eigendynamics have a central functional role for overall cognitive computation [4,5]. Transiently stable neural activity is regularly observed on the cognitive time-scale of 80–100 ms, with indications that neural competition [6] plays an important role in the selection of the transiently stable neural ensembles [7], also denoted winning coalitions [8]. We report on a theory approach which implements these two principles, transient-state dynamics and neural competition, in terms of an associative neural network with clique encoding [9]. A cognitive system [10] with a non-trivial internal eigendynamics has two seemingly contrasting tasks to fulfill. The internal processes need to be regular and not chaotic on one side, but sensitive to the afferent sensory stimuli on the other side. We show, that these two contrasting demands can be reconciled within our approach based on competitive transient-state dynamics, when allowing the sensory stimuli to modulate the competition for the next winning coalition. By testing the system with the bars problem, we find an emerging cognitive capability. Only based on the two basic architectural principles, neural competition and transient-state dynamics, with no explicit algorithmic encoding, the system performs on its own a non-linear independent component analysis of input data stream. The system has rudimentary biological features. All learning is local Hebbian-style, unsupervised and online. It exhibits an ever-ongoing eigendynamics and at no time is the state or the value of synaptic strengths reset or the system restarted; there is no separation between training and performance. We believe that this kind of approach – cognitive computation with autonomously active neural networks – to be an emerging field, relevant both for system neuroscience and synthetic cognitive systems
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