Timezone: »
The olfactory system faces a difficult inference problem: it has to determine what odors are present based on the distributed activation of its receptor neurons. Here we derive neural implementations of two approximate inference algorithms that could be used by the brain. One is a variational algorithm (which builds on the work of Beck. et al., 2012), the other is based on sampling. Importantly, we use a more realistic prior distribution over odors than has been used in the past: we use a "spike and slab'' prior, for which most odors have zero concentration. After mapping the two algorithms onto neural dynamics, we find that both can infer correct odors in less than 100 ms, although it takes ~500 ms to eliminate false positives. Thus, at the behavioral level, the two algorithms make very similar predictions. However, they make different assumptions about connectivity and neural computations, and make different predictions about neural activity. Thus, they should be distinguishable experimentally. If so, that would provide insight into the mechanisms employed by the olfactory system, and, because the two algorithms use very different coding strategies, that would also provide insight into how networks represent probabilities.
Author Information
Agnieszka Grabska-Barwinska (Google DeepMind)
Jeff Beck (Gatsby Unit, UCL)
Alexandre Pouget (University of Geneva)
Peter E Latham (Gatsby Unit, UCL)
Related Events (a corresponding poster, oral, or spotlight)
-
2013 Poster: Demixing odors - fast inference in olfaction »
Sat. Dec 7th 03:00 -- 07:59 AM Room Harrah's Special Events Center, 2nd Floor
More from the Same Authors
-
2021 Poster: Powerpropagation: A sparsity inducing weight reparameterisation »
Jonathan Schwarz · Siddhant Jayakumar · Razvan Pascanu · Peter E Latham · Yee Teh -
2021 Poster: Towards Biologically Plausible Convolutional Networks »
Roman Pogodin · Yash Mehta · Timothy Lillicrap · Peter E Latham -
2020 Poster: Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks »
Roman Pogodin · Peter E Latham -
2020 Poster: A Local Temporal Difference Code for Distributional Reinforcement Learning »
Pablo Tano · Peter Dayan · Alexandre Pouget -
2020 Poster: Dynamic allocation of limited memory resources in reinforcement learning »
Nisheet Patel · Luigi Acerbi · Alexandre Pouget -
2014 Poster: Optimal decision-making with time-varying evidence reliability »
Jan Drugowitsch · Ruben Moreno-Bote · Alexandre Pouget -
2014 Spotlight: Optimal decision-making with time-varying evidence reliability »
Jan Drugowitsch · Ruben Moreno-Bote · Alexandre Pouget -
2014 Poster: Optimal prior-dependent neural population codes under shared input noise »
Agnieszka Grabska-Barwinska · Jonathan W Pillow -
2011 Poster: How biased are maximum entropy models? »
Jakob H Macke · Iain Murray · Peter E Latham -
2007 Oral: Neural characterization in partially observed populations of spiking neurons »
Jonathan W Pillow · Peter E Latham -
2007 Poster: Neural characterization in partially observed populations of spiking neurons »
Jonathan W Pillow · Peter E Latham