Cited By
View all- Kreutzer ESenn WPetrovici M(2022)Natural-gradient learning for spiking neuronseLife10.7554/eLife.6652611Online publication date: 25-Apr-2022
Conductance-based dendrites perform reliability-weighted opinion pooling
Article No.: 11, Pages 1 - 3
Abstract
Cue integration, the combination of different sources of information to reduce uncertainty, is a fundamental computational principle of brain function. Starting from a normative model we show that the dynamics of multi-compartment neurons with conductance-based dendrites naturally implement the required probabilistic computations. The associated error-driven plasticity rule allows neurons to learn the relative reliability of different pathways from data samples, approximating Bayes-optimal observers in multisensory integration tasks. Additionally, the model provides a functional interpretation of neural recordings from multisensory integration experiments and makes specific predictions for membrane potential and conductance dynamics of individual neurons.
References
[1]
Matteo Carandini and David J Heeger. 2012. Normalization as a canonical neural computation. Nature Reviews Neuroscience 13, 1 (2012), 51.
[2]
Mark M Churchland, M Yu Byron, John P Cunningham, Leo P Sugrue, Marlene R Cohen, Greg S Corrado, William T Newsome, Andrew M Clark, Paymon Hosseini, Benjamin B Scott, et al. 2010. Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nature neuroscience 13, 3 (2010), 369.
[3]
Sylvain Crochet, James FA Poulet, Yves Kremer, and Carl CH Petersen. 2011. Synaptic mechanisms underlying sparse coding of active touch. Neuron 69, 6 (2011), 1160--1175.
[4]
Marc O Ernst and Martin S Banks. 2002. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 6870 (2002), 429.
[5]
Christopher R Fetsch, Amanda H Turner, Gregory C DeAngelis, and Dora E Angelaki. 2009. Dynamic reweighting of visual and vestibular cues during self-motion perception. Journal of Neuroscience 29, 49 (2009), 15601--15612.
[6]
Andreas VM Herz, Alexander Mathis, and Martin Stemmler. 2017. Periodic population codes: From a single circular variable to higher dimensions, multiple nested scales, and conceptual spaces. Curr. Opin. Neurobiol. 46 (2017), 99--108.
[7]
Geoffrey E Hinton. 2002. Training products of experts by minimizing contrastive divergence. Neural computation 14, 8 (2002), 1771--1800.
[8]
Jeffry S Isaacson and Massimo Scanziani. 2011. How inhibition shapes cortical activity. Neuron 72, 2 (2011), 231--243.
[9]
David C Knill and Alexandre Pouget. 2004. The Bayesian brain: the role of uncertainty in neural coding and computation. TRENDS in Neurosciences 27, 12 (2004), 712--719.
[10]
MAlex Meredith and Barry E Stein. 1983. Interactions among converging sensory inputs in the superior colliculus. Science 221, 4608 (1983), 389--391.
[11]
Michael L Morgan, Gregory C DeAngelis, and Dora E Angelaki. 2008. Multisensory integration in macaque visual cortex depends on cue reliability. Neuron 59, 4 (2008), 662--673.
[12]
Nader Nikbakht, Azadeh Tafreshiha, Davide Zoccolan, and Mathew E Diamond. 2018. Supralinear and supramodal integration of visual and tactile signals in rats: psychophysics and neuronal mechanisms. Neuron 97, 3 (2018), 626--639.
[13]
Tomokazu Ohshiro, Dora E Angelaki, and Gregory C DeAngelis. 2011. A normalization model of multisensory integration. Nat. Neurosci. 14, 6 (2011), 775.
[14]
Thomas J Perrault Jr, J William Vaughan, Barry E Stein, and Mark T Wallace. 2005. Superior colliculus neurons use distinct operational modes in the integration of multisensory stimuli. Journal of neurophysiology 93, 5 (2005), 2575--2586.
[15]
Rajesh PN Rao and Dana H Ballard. 1999. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature neuroscience 2, 1 (1999).
[16]
Shankar Sachidhanandam, Varun Sreenivasan, Alexandros Kyriakatos, Yves Kremer, and Carl CH Petersen. 2013. Membrane potential correlates of sensory perception in mouse barrel cortex. Nat. Neurosci. 16, 11 (2013), 1671--1677.
[17]
Joao Sacramento, Rui Ponte Costa, Yoshua Bengio, and Walter Senn. 2018. Dendritic cortical microcircuits approximate the backpropagation algorithm. In Advances in Neural Information Processing Systems. 8721--8732.
[18]
Benjamin Scellier and Yoshua Bengio. 2017. Equilibrium propagation: Bridging the gap between energy-based models and backpropagation. Front. Comput. Neurosci. 11 (2017), 24.
[19]
Johannes Schemmel, Daniel Briiderle, Andreas Griibl, Matthias Hock, Karlheinz Meier, and Sebastian Millner. 2010. A wafer-scale neuromorphic hardware system for large-scale neural modeling. In Proceedings of 2010 IEEE International Symposium on Circuits and Systems. IEEE, 1947--1950.
[20]
Robert Urbanczik and Walter Senn. 2014. Learning by the dendritic prediction of somatic spiking. Neuron 81, 3(2014), 521--528.
[21]
Bernard Widrow and Marcian E Hoff. 1960. Adaptive switching circuits. Technical Report. Stanford Univ Ca Stanford Electronics Labs.
Index Terms
- Conductance-based dendrites perform reliability-weighted opinion pooling
Recommendations
A calcium-influx-dependent plasticity model exhibiting multiple STDP curves
AbstractHebbian plasticity means that if the firing of two neurons is correlated, then their connection is strengthened. Conversely, uncorrelated firing causes a decrease in synaptic strength. Spike-timing-dependent plasticity (STDP) represents one ...
Computational simulation of dentate gyrus granule cell-The role of metaplasticity
After several decades of study, the dynamics of synaptic plasticity in neurons still remains somewhat a mystery. By conducting a series of simulations on a simulated version of an in-vivo experiment on the rat dentate gyrus granule cell, using the ...
2013 Special Issue: Dendritic calcium signaling in cerebellar Purkinje cell
The Purkinje cells in the cerebellum are unique neurons that generate local and global Ca^2^+ signals in response to two types of excitatory inputs, parallel fiber and climbing fiber, respectively. The spatiotemporal distribution and interaction of ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
March 2020
131 pages
Copyright © 2020 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
In-Cooperation
- INTEL: Intel Corporation
- IBM: IBM
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 18 June 2020
Check for updates
Author Tags
Qualifiers
- Extended-abstract
- Research
- Refereed limited
Conference
NICE '20
Acceptance Rates
Overall Acceptance Rate 25 of 40 submissions, 63%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 85Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 28 Jan 2025
Other Metrics
Citations
Cited By
View all- Kreutzer ESenn WPetrovici M(2022)Natural-gradient learning for spiking neuronseLife10.7554/eLife.6652611Online publication date: 25-Apr-2022
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in