2,649 research outputs found
A novel plasticity rule can explain the development of sensorimotor intelligence
Grounding autonomous behavior in the nervous system is a fundamental
challenge for neuroscience. In particular, the self-organized behavioral
development provides more questions than answers. Are there special functional
units for curiosity, motivation, and creativity? This paper argues that these
features can be grounded in synaptic plasticity itself, without requiring any
higher level constructs. We propose differential extrinsic plasticity (DEP) as
a new synaptic rule for self-learning systems and apply it to a number of
complex robotic systems as a test case. Without specifying any purpose or goal,
seemingly purposeful and adaptive behavior is developed, displaying a certain
level of sensorimotor intelligence. These surprising results require no system
specific modifications of the DEP rule but arise rather from the underlying
mechanism of spontaneous symmetry breaking due to the tight
brain-body-environment coupling. The new synaptic rule is biologically
plausible and it would be an interesting target for a neurobiolocal
investigation. We also argue that this neuronal mechanism may have been a
catalyst in natural evolution.Comment: 18 pages, 5 figures, 7 video
Metastability, Criticality and Phase Transitions in brain and its Models
This essay extends the previously deposited paper "Oscillations, Metastability and Phase Transitions" to incorporate the theory of Self-organizing Criticality. The twin concepts of Scaling and Universality of the theory of nonequilibrium phase transitions is applied to the role of reentrant activity in neural circuits of cerebral cortex and subcortical neural structures
Emergent communication enhances foraging behaviour in evolved swarms controlled by Spiking Neural Networks
Social insects such as ants communicate via pheromones which allows them to
coordinate their activity and solve complex tasks as a swarm, e.g. foraging for
food. This behavior was shaped through evolutionary processes. In computational
models, self-coordination in swarms has been implemented using probabilistic or
simple action rules to shape the decision of each agent and the collective
behavior. However, manual tuned decision rules may limit the behavior of the
swarm. In this work we investigate the emergence of self-coordination and
communication in evolved swarms without defining any explicit rule. We evolve a
swarm of agents representing an ant colony. We use an evolutionary algorithm to
optimize a spiking neural network (SNN) which serves as an artificial brain to
control the behavior of each agent. The goal of the evolved colony is to find
optimal ways to forage for food and return it to the nest in the shortest
amount of time. In the evolutionary phase, the ants are able to learn to
collaborate by depositing pheromone near food piles and near the nest to guide
other ants. The pheromone usage is not manually encoded into the network;
instead, this behavior is established through the optimization procedure. We
observe that pheromone-based communication enables the ants to perform better
in comparison to colonies where communication via pheromone did not emerge. We
assess the foraging performance by comparing the SNN based model to a rule
based system. Our results show that the SNN based model can efficiently
complete the foraging task in a short amount of time. Our approach illustrates
self coordination via pheromone emerges as a result of the network
optimization. This work serves as a proof of concept for the possibility of
creating complex applications utilizing SNNs as underlying architectures for
multi-agent interactions where communication and self-coordination is desired.Comment: 27 pages, 16 figure
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
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