34,810 research outputs found
Closed-form control with spike coding networks
Efficient and robust control using spiking neural networks (SNNs) is still an
open problem. Whilst behaviour of biological agents is produced through sparse
and irregular spiking patterns, which provide both robust and efficient
control, the activity patterns in most artificial spiking neural networks used
for control are dense and regular -- resulting in potentially less efficient
codes. Additionally, for most existing control solutions network training or
optimization is necessary, even for fully identified systems, complicating
their implementation in on-chip low-power solutions. The neuroscience theory of
Spike Coding Networks (SCNs) offers a fully analytical solution for
implementing dynamical systems in recurrent spiking neural networks -- while
maintaining irregular, sparse, and robust spiking activity -- but it's not
clear how to directly apply it to control problems. Here, we extend SCN theory
by incorporating closed-form optimal estimation and control. The resulting
networks work as a spiking equivalent of a linear-quadratic-Gaussian
controller. We demonstrate robust spiking control of simulated
spring-mass-damper and cart-pole systems, in the face of several perturbations,
including input- and system-noise, system disturbances, and neural silencing.
As our approach does not need learning or optimization, it offers opportunities
for deploying fast and efficient task-specific on-chip spiking controllers with
biologically realistic activity.Comment: Under review in an IEEE journa
Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDP
Spiking neural networks (SNNs) are good candidates to produce
ultra-energy-efficient hardware. However, the performance of these models is
currently behind traditional methods. Introducing multi-layered SNNs is a
promising way to reduce this gap. We propose in this paper a new threshold
adaptation system which uses a timestamp objective at which neurons should
fire. We show that our method leads to state-of-the-art classification rates on
the MNIST dataset (98.60%) and the Faces/Motorbikes dataset (99.46%) with an
unsupervised SNN followed by a linear SVM. We also investigate the sparsity
level of the network by testing different inhibition policies and STDP rules
Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments
One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment
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