WEBCA: Weakly-Electric-Fish Bioinspired Cognitive Architecture
June 29, 2018 Β· Declared Dead Β· π Biologically Inspired Cognitive Architectures
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Amit Kumar Mishra
arXiv ID
1806.11401
Category
cs.AI: Artificial Intelligence
Citations
0
Venue
Biologically Inspired Cognitive Architectures
Last Checked
4 months ago
Abstract
Neuroethology has been an active field of study for more than a century now. Out of some of the most interesting species that has been studied so far, weakly electric fish is a fascinating one. It performs communication, echo-location and inter-species detection efficiently with an interesting configuration of sensors, neu-rons and a simple brain. In this paper we propose a cognitive architecture inspired by the way these fishes handle and process information. We believe that it is eas-ier to understand and mimic the neural architectures of a simpler species than that of human. Hence, the proposed architecture is expected to both help research in cognitive robotics and also help understand more complicated brains like that of human beings.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Artificial Intelligence
π
π
The Cartographer
R.I.P.
π»
Ghosted
Explanation in Artificial Intelligence: Insights from the Social Sciences
R.I.P.
π»
Ghosted
Federated Machine Learning: Concept and Applications
R.I.P.
π»
Ghosted
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
R.I.P.
π»
Ghosted
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
R.I.P.
π»
Ghosted
Rainbow: Combining Improvements in Deep Reinforcement Learning
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted