Skip to main content
  • Poster presentation
  • Open access
  • Published:

Extending the tempotron with hierarchical dendrites allows faster learning

Certain functional classes of neurons seem to be able to differentiate between input patterns with high temporal precision. As input patterns to neurons can consist of up to thousands of inputs, the ability to identify target patterns amongst statistically similar background patterns is impressive and has been suggested to occur via modification to synaptic weights. Yet how does learning that occurs locally at the level of synapses converge, without global coordination? While this question is important for understanding how synaptic learning gives rise to dendritic computation, it is impractical to test experimentally. Insight from abstract neuronal models, such as the multilayer perceptron networks [1], provide a potential glimpse of the difficulty of how to ensure that global convergence during learning when weight changes are local. In this work, we report that by extending the tempotron model [2], we are able to demonstrate that learning can, indeed, learn locally but also converge globally. By arranging dendritic units in a hierarchical manner which feeds into a master dendrite and soma, learning occurs over two timescales: locally on each dendritic branch, using a simple incremental plasticity rule; and at a slower timescale on the main branch, where information is integrated across branches. We observe that the inclusion of dendrites reduces the learning time required by allowing dendrites to subsample the entire input space. In comparison to one single dendrite receiving n inputs, the inclusion of m dendrites means that each dendrite is now subsampling n/m inputs, which not results in faster learning epochs before convergence but also improves the overall robustness against noise. Thus, the move from a single tempotron to a set of hierarchically configured tempotrons, representing dendrites, imbues the unit with recognition of pattern fragments (with a pattern capacity > m!), faster convergence during learning and increased noise tolerance (both of which scale with m). The inclusion of dendrites also allows for them to signal in sequences with varying relative temporal offsets, granting the neuron the opportunity to differentiate between multiple positive patterns to identify not only which pattern was observed but also when. Furthermore, we have also demonstrated that tempotrons can be extended to work for non-episodic patterns i.e. ongoing and without reset, and can also perform well when number of distractor patterns greatly outnumber positive patterns. Conceptually, our model reconciles the tempotron learning rule with work on dendritic computation which argues for dendrites as computation units [3]. It also provides a potential explanation to phenomena observed experimentally, such as neurons in visual cortex whose dendrites had other preferred orientations that were different to those of the soma [4].

References

  1. Rumelhart DE, Hinton GE, Williams RJ: Learning representations by back-propagating errors. Nature. 1986, 323: 533-536.

    Article  Google Scholar 

  2. Gütig R, Sompolinsky H: The tempotron: a neuron that learns spike timing-based decisions. Nature Neuroscience. 2006, 9 (3): 420-428.

    Article  PubMed  Google Scholar 

  3. Brannon & Mel: Arithmetic of subthreshold synaptic summation in a model CA1 pyramidal cell. Neuron. 2003, 37 (6): 977-987.

    Article  Google Scholar 

  4. Jia H, Rochefort NL, Chen X, Konnerth A: Dendritic organization of sensory input to cortical neurons in vivo. Nature. 2010, 464: 1307-1312.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

SJ is supported by EU FP7 Marie Curie fellowship (PIEF-GA-2013-628086). RC is supported by EU FP7 Marie Curie Initial Training Network 289146 'NETT'.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sarah J Jarvis.

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jarvis, S.J., Caze, R. & Clopath, C. Extending the tempotron with hierarchical dendrites allows faster learning. BMC Neurosci 16 (Suppl 1), P37 (2015). https://0-doi-org.brum.beds.ac.uk/10.1186/1471-2202-16-S1-P37

Download citation

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/1471-2202-16-S1-P37

Keywords