Functional dissection of circuitry in a neural integrator

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  • An Erratum to this article was published on 01 June 2007


In neural integrators, transient inputs are accumulated into persistent firing rates that are a neural correlate of short-term memory. Integrators often contain two opposing cell populations that increase and decrease sustained firing as a stored parameter value rises. A leading hypothesis for the mechanism of persistence is positive feedback through mutual inhibition between these opposing populations. We tested predictions of this hypothesis in the goldfish oculomotor velocity-to-position integrator by measuring the eye position and firing rates of one population, while pharmacologically silencing the opposing one. In complementary experiments, we measured responses in a partially silenced single population. Contrary to predictions, induced drifts in neural firing were limited to half of the oculomotor range. We built network models with synaptic-input thresholds to demonstrate a new hypothesis suggested by these data: mutual inhibition between the populations does not provide positive feedback in support of integration, but rather coordinates persistent activity intrinsic to each population.

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Figure 1: Traditional model of feedback for opposing populations.
Figure 2: Eye position after inactivation of one population.
Figure 3: Firing rates in the right population after inactivation of the left.
Figure 4: Analysis of rate drift after complete left inactivation.
Figure 5: Firing rates in the right population after inactivation of caudal neurons.
Figure 6: Analysis of rate drift after caudal inactivation.
Figure 7: Models with activation thresholds can explain the asymmetric effects of unilateral inactivations.
Figure 8: Loss of coordination after loss of mutual inhibition.

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  • 02 May 2007

    panels c and d


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    *NOTE: In the version of this article initially published, the labels for the x-axes in figure 8, panels c and d are incorrect. The correct labels should be “Rate, left”. This error has been corrected in the HTML and PDF versions of the article.


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We thank H.S. Seung, C. Brody and J. Raymond for helpful discussions and critique. The experimental phase of this work was supported by Bell Laboratories. E.A. holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. M.S.G. holds a Brachmann–Hoffman Fellowship from Wellesley College. All authors received support from the US National Institutes of Health.

Author information

D.W.T. supervised the experimental component of the project. E.A., R.B. and D.W.T. conceived the experiments. E.A. and D.W.T. developed the instrumentation. E.A. collected and analyzed the data with assistance by B.M. M.S.G. supervised the theoretical component of the project. E.A., I.O., R.B., M.S.G. and D.W.T. provide data interpretation and coordination between experiments and modeling. I.O. and M.S.G. developed the mathematical models and performed the simulations. E.A., M.S.G. and D.W.T. wrote the paper.

Correspondence to Emre Aksay or Mark S Goldman.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Model tuning curves defined by experimentally measured rate versus position relationships. (PDF 200 kb)

Supplementary Fig. 2

Method for functional dissection of a circuit. (PDF 1071 kb)

Supplementary Table 1

Change in position drift for each complete left inactivation. (PDF 123 kb)

Supplementary Table 2

Change in rate drift for each complete left inactivation. (PDF 92 kb)

Supplementary Table 3

Change in rate drift for each caudal right inactivation. (PDF 89 kb)

Supplementary Table 4

Values of η for the model simulations. (PDF 119 kb)

Supplementary Methods (PDF 122 kb)

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