Sugandha Sharma, Aaron R. Voelker, and Chris Eliasmith. A spiking neural bayesian model of life span inference. In Proceedings of the 39th Annual Conference of the Cognitive Science Society. London, UK, 2017. Cognitive Science Society. URL: https://mindmodeling.org/cogsci2017/papers/0591/index.html.
@inproceedings{sharma2017,
title = {A Spiking Neural Bayesian Model of Life Span Inference},
author = {Sugandha Sharma and Aaron R. Voelker and Chris Eliasmith},
booktitle = {Proceedings of the 39th Annual Conference of the Cognitive Science Society},
publisher = {Cognitive Science Society},
address = {London, UK},
url = {https://mindmodeling.org/cogsci2017/papers/0591/index.html},
pdf = {http://compneuro.uwaterloo.ca/files/publications/sharma.2017.pdf},
abstract = {In this paper, we present a spiking neural model of life span
inference. Through this model, we explore the biological
plausibility of performing Bayesian computations in the brain.
Specifically, we address the issue of representing probability
distributions using neural circuits and combining them in
meaningful ways to perform inference. We show that applying
these methods to the life span inference task matches human
performance on this task better than an ideal Bayesian model
due to the use of neuron tuning curves. We also describe potential
ways in which humans might be generating the priors
needed for this inference. This provides an initial step towards
better understanding how Bayesian computations may be implemented
in a biologically plausible neural network.},
year = {2017},
}