1,631 research outputs found
Spiking neurons with short-term synaptic plasticity form superior generative networks
Spiking networks that perform probabilistic inference have been proposed both
as models of cortical computation and as candidates for solving problems in
machine learning. However, the evidence for spike-based computation being in
any way superior to non-spiking alternatives remains scarce. We propose that
short-term plasticity can provide spiking networks with distinct computational
advantages compared to their classical counterparts. In this work, we use
networks of leaky integrate-and-fire neurons that are trained to perform both
discriminative and generative tasks in their forward and backward information
processing paths, respectively. During training, the energy landscape
associated with their dynamics becomes highly diverse, with deep attractor
basins separated by high barriers. Classical algorithms solve this problem by
employing various tempering techniques, which are both computationally
demanding and require global state updates. We demonstrate how similar results
can be achieved in spiking networks endowed with local short-term synaptic
plasticity. Additionally, we discuss how these networks can even outperform
tempering-based approaches when the training data is imbalanced. We thereby
show how biologically inspired, local, spike-triggered synaptic dynamics based
simply on a limited pool of synaptic resources can allow spiking networks to
outperform their non-spiking relatives.Comment: corrected typo in abstrac
Reactive direction control for a mobile robot: A locust-like control of escape direction emerges when a bilateral pair of model locust visual neurons are integrated
Locusts possess a bilateral pair of uniquely identifiable visual neurons that respond vigorously to
the image of an approaching object. These neurons are called the lobula giant movement
detectors (LGMDs). The locust LGMDs have been extensively studied and this has lead to the
development of an LGMD model for use as an artificial collision detector in robotic applications.
To date, robots have been equipped with only a single, central artificial LGMD sensor, and this
triggers a non-directional stop or rotation when a potentially colliding object is detected. Clearly,
for a robot to behave autonomously, it must react differently to stimuli approaching from
different directions. In this study, we implement a bilateral pair of LGMD models in Khepera
robots equipped with normal and panoramic cameras. We integrate the responses of these LGMD
models using methodologies inspired by research on escape direction control in cockroaches.
Using ‘randomised winner-take-all’ or ‘steering wheel’ algorithms for LGMD model integration,
the khepera robots could escape an approaching threat in real time and with a similar
distribution of escape directions as real locusts. We also found that by optimising these
algorithms, we could use them to integrate the left and right DCMD responses of real jumping
locusts offline and reproduce the actual escape directions that the locusts took in a particular
trial. Our results significantly advance the development of an artificial collision detection and
evasion system based on the locust LGMD by allowing it reactive control over robot behaviour.
The success of this approach may also indicate some important areas to be pursued in future
biological research
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