@inproceedings{
 title = {Learning distance-behavioural preferences using a single sensor in a spiking neural network},
 type = {inproceedings},
 year = {2017},
 keywords = {Robotic simulation,Sensory calibration,Spike timing dependent plasticity,Spiking neural networks},
 pages = {110-118},
 volume = {10613 LNCS},
 id = {365d472c-6c36-38e4-9aff-ba114d9f318d},
 created = {2022-03-23T20:17:32.797Z},
 file_attached = {true},
 profile_id = {1ea772e9-fff0-3239-a501-64d3568c4fff},
 last_modified = {2022-03-24T13:14:55.905Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Actions from autonomous agents demand adaptive rules rather than being hard coded. Contrary to using multiple pre-calibrated sensors, utilizing a single non-calibrated sensor in combination with neural elements could provide flexibility through learning, to effectively cope with changing environments. The objective of this study was to design an adaptive system with the potential capability of learning behavioural preferences in relation to distinct distances from a wall using only a single ultrasonic sensor. Using spike-timing dependent plasticity (STDP) as a learning mechanism in a spiking neural network (SNN), the agent displayed the correct behaviour and was successful in learning the desired behavioural preference at a medium distance. However, the agent treated far and close distances as ambiguous inputs from the sensory environment, despite the presentation of reinforcement cues during learning.},
 bibtype = {inproceedings},
 author = {Ross, Matt and Berberian, Nareg and Cyr, André and Thériault, Frédéric and Chartier, Sylvain},
 doi = {10.1007/978-3-319-68600-4_14},
 booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

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However, the agent treated far and close distances as ambiguous inputs from the sensory environment, despite the presentation of reinforcement cues during learning.","bibtype":"inproceedings","author":"Ross, Matt and Berberian, Nareg and Cyr, André and Thériault, Frédéric and Chartier, Sylvain","doi":"10.1007/978-3-319-68600-4_14","booktitle":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","bibtex":"@inproceedings{\n title = {Learning distance-behavioural preferences using a single sensor in a spiking neural network},\n type = {inproceedings},\n year = {2017},\n keywords = {Robotic simulation,Sensory calibration,Spike timing dependent plasticity,Spiking neural networks},\n pages = {110-118},\n volume = {10613 LNCS},\n id = {365d472c-6c36-38e4-9aff-ba114d9f318d},\n created = {2022-03-23T20:17:32.797Z},\n file_attached = {true},\n profile_id = {1ea772e9-fff0-3239-a501-64d3568c4fff},\n last_modified = {2022-03-24T13:14:55.905Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Actions from autonomous agents demand adaptive rules rather than being hard coded. 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