Volume 11 Supplement 1

Nineteenth Annual Computational Neuroscience Meeting: CNS*2010

Open Access

Learning mechanisms for DA-modulated spiking networks in the basal ganglia

BMC Neuroscience201011(Suppl 1):P106

DOI: 10.1186/1471-2202-11-S1-P106

Published: 20 July 2010

How the basal ganglia act to gate cortically planned actions is a topic of current discussion. Interesting work by Gurney et al [13] suggests an interaction between the STN and GPe as a central element of inhibition for action gating, with pathological oscillations occurring if striatal input changes due to dopamine depletion in Parkinson's disease.

But what exactly changes in the signals that the striatum projects to the rest of the basal ganglia? How could the altered dopamine signal and its effect on striatal learning influence the observed functions of the basal ganglia in Parkinsonian and in healthy patients?

While rate-based learning models of the basal ganglia have been suggested [5], a spiking network that reproduces basal ganglia anatomy and autonomously learns a set of possible action sequences that can then be reinforced through dopamine feedback has yet to be demonstrated.

On the way to constructing such a network, we present some effects of spike timing dependent plasticity, synaptic delay, group inhibition, noisy & localised projections and dopamine modulation on feed-forward and associative spiking networks within the basal ganglia and cortex.

Authors’ Affiliations

Institute for Signal Processing, University of Lübeck


  1. Gurney K, Prescott TJ, Redgrave P: A computational model of action selection in the basal ganglia. I. A new functional anatomy. Biol. Cybern. 2001, 84: 401-410. 10.1007/PL00007984.View ArticlePubMedGoogle Scholar
  2. Humphries MD, Stewart RD, Gurney KN: A physiologically plausible model of action selection and oscillatory activity in the basal ganglia. J Neurosci. 2006, 26: 12921-42. 10.1523/JNEUROSCI.3486-06.2006.View ArticlePubMedGoogle Scholar
  3. Bogacz R, Gurney K: The basal ganglia and cortex implement optimal decision making between alternative actions. Neural Comput. 2007, 19: 442-477. 10.1162/neco.2007.19.2.442.View ArticlePubMedGoogle Scholar
  4. Cisek P: Cortical mechanisms of action selection: the affordance competition hypothesis. Philos Trans R Soc Lond B Biol Sci. 2007, 362: 1585-1599. 10.1098/rstb.2007.2054.PubMed CentralView ArticlePubMedGoogle Scholar
  5. Frank MJ: Dynamic dopamine modulation in the basal ganglia: a neurocomputational account of cognitive deficits in medicated and nonmedicated Parkinsonism. J Cogn Neurosci. 2005, 17: 51-72. 10.1162/0898929052880093.View ArticlePubMedGoogle Scholar
  6. Schultz W: Multiple reward signals in the brain. Nature Reviews Neuroscience. 2000, 1: 199-207. 10.1038/35044563.View ArticlePubMedGoogle Scholar
  7. Farries MA, Fairhall AL: Reinforcement learning with modulated spike timing dependent synaptic plasticity. Journal of neurophysiology. 2007, 98: 3648-65. 10.1152/jn.00364.2007.View ArticlePubMedGoogle Scholar
  8. Masquelier T, Guyonneau R, Thorpe SJ: Competitive STDP-based spike pattern learning. Neural Computation. 2009, 21: 1259-76. 10.1162/neco.2008.06-08-804.View ArticlePubMedGoogle Scholar
  9. Potjans W, Morrison A, Diesmann M: A spiking neural network model of an actor-critic learning agent. Neural computation. 2009, 21: 301-39. 10.1162/neco.2008.08-07-593.View ArticlePubMedGoogle Scholar
  10. Singh SP, Barto AG, Chenanez N: Intrinsically motivated reinforcement learning. Advances in Neural Information Processing Systems. 2005, 17: 1281-1288.Google Scholar


© Vogt and Hofmann; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd.