The effect of “robust” integrator dynamics on decision-making performance
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POSTER PRESENTATION Open Access The effect of “robust” integrator dynamics on decision-making performance Nicholas Cain 1* , Andrea Barreiro 1 , Mike Shadlen 2 , Eric Shea-Brown 1 From Nineteenth Annual Computational Neuroscience Meeting: CNS*2010 San Antonio, TX, USA. 24-30 July 2010 The drift-diffusion model of decision-making provides a framework to explain performance statistics of two- alternative perceptual decision-making tasks. In addi- tion, the cortical activity of rhesus monkeys performing these tasks has been observed to stochastically fluctu- ate until a threshold level of activity is reached, imply- ing a neuronal implementation of this decision-making paradigm [1]. The observed ramping of activity is hypothesized to represent the accumulation of sensory evidence over timescales much longer than those of the underlying cortical hardware. One proposed mechanism to imple- ment this short-term cortical memory is by a self-excita- tory recurrent neural integrator [2]. However, it has been observed that naïve excitatory recurrent circuits require extremely fine-tuning to accurately integrate an input signal [3]. We investigated the consequences of implementing a short-term evidence accumulation circuit via a recurrent network designed to be robust to mistuning in the glo- bal self-excitation parameter [4], [5]. Following the approach of previous work, we derived a firing rate model for the dynamics of the integrator parameterized by biophysically relevant quantities. In particular, a sin- gle piecewise-defined ordinary differential equation models the average firing rate activity of a number of bistable sub-populations exhibiting hysteresis. A major consequence of this robust integration scheme is the exclusion of instantaneous sensory evi- dence that does not sufficiently favor one alternative over the other. In particular, if stimulus is below a cer- tain evidence-exclusion threshold, average integrator activity does not change. This evidence exclusion mechanism reveals a potential performance tradeoff between gaining robustness to mistuning on one hand, but ignoring potentially relevant information on the other. To our surprise, we find that a surprisingly large frac- tion of sensory evidence can be ignored before decision- making performance is compromised. To explain this observation, we derived equations that describe a hierar- chy of sequential hypothesis tests that vary in their evidence-exclusion threshold. These models yield quan- titative predictions of how decision-making performance is diminished as model robustness is increased. We conclude that for a fixed task difficulty and a con- stant decision threshold, a substantial fraction of the sti- mulus can be ignored before error rates or reaction times increase, if decision boundaries are modified appropriately. This observation implies that an evi- dence-accumulating neural integrator circuit robust to feedback mistuning might be able to underlie perceptual decision-making, without a strong compromise in performance. Acknowledgements The authors thank Adrienne Fairhall, Michael Shadlen, and their respective lab groups, for numerous helpful discussions. The authors also acknowledge funding support from the Northwest Center for Neural Engineering, and a Burroughs-Wellcome Fund Career Award at the Scientific Interface (E.S.-B.). Author details 1 Department of Applied Mathematics, University of Washington, Seattle, WA, 98195, USA. 2 Department of Physiology and Biophysics, University of Washington, Seattle, WA, 98195, USA. Published: 20 July 2010 References 1. Roitman J, Shadlen M: Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J Neurosci 2002, 22:9475-9489. 2. Wang XJ: Decision Making in Recurrent Neuronal Circuits. Neuron 2008, 60:215-234. * Correspondence: [email protected] 1 Department of Applied Mathematics, University of Washington, Seattle, WA, 98195, USA Cain et al. BMC Neuroscience 2010, 11(Suppl 1):P97 http://www.biomedcentral.com/1471-2202/11/S1/P97 © 2010 Cain et al; licensee BioMed Central Ltd.
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Transcript of The effect of “robust” integrator dynamics on decision-making performance
POSTER PRESENTATION Open Access
The effect of “robust” integrator dynamics ondecision-making performanceNicholas Cain1*, Andrea Barreiro1, Mike Shadlen2, Eric Shea-Brown1
From Nineteenth Annual Computational Neuroscience Meeting: CNS*2010San Antonio, TX, USA. 24-30 July 2010
The drift-diffusion model of decision-making providesa framework to explain performance statistics of two-alternative perceptual decision-making tasks. In addi-tion, the cortical activity of rhesus monkeys performingthese tasks has been observed to stochastically fluctu-ate until a threshold level of activity is reached, imply-ing a neuronal implementation of this decision-makingparadigm [1].The observed ramping of activity is hypothesized to
represent the accumulation of sensory evidence overtimescales much longer than those of the underlyingcortical hardware. One proposed mechanism to imple-ment this short-term cortical memory is by a self-excita-tory recurrent neural integrator [2]. However, it hasbeen observed that naïve excitatory recurrent circuitsrequire extremely fine-tuning to accurately integrate aninput signal [3].We investigated the consequences of implementing a
short-term evidence accumulation circuit via a recurrentnetwork designed to be robust to mistuning in the glo-bal self-excitation parameter [4], [5]. Following theapproach of previous work, we derived a firing ratemodel for the dynamics of the integrator parameterizedby biophysically relevant quantities. In particular, a sin-gle piecewise-defined ordinary differential equationmodels the average firing rate activity of a number ofbistable sub-populations exhibiting hysteresis.
A major consequence of this robust integrationscheme is the exclusion of instantaneous sensory evi-dence that does not sufficiently favor one alternativeover the other. In particular, if stimulus is below a cer-tain evidence-exclusion threshold, average integratoractivity does not change. This evidence exclusionmechanism reveals a potential performance tradeoff
between gaining robustness to mistuning on one hand,but ignoring potentially relevant information on theother.To our surprise, we find that a surprisingly large frac-
tion of sensory evidence can be ignored before decision-making performance is compromised. To explain thisobservation, we derived equations that describe a hierar-chy of sequential hypothesis tests that vary in theirevidence-exclusion threshold. These models yield quan-titative predictions of how decision-making performanceis diminished as model robustness is increased.We conclude that for a fixed task difficulty and a con-
stant decision threshold, a substantial fraction of the sti-mulus can be ignored before error rates or reactiontimes increase, if decision boundaries are modifiedappropriately. This observation implies that an evi-dence-accumulating neural integrator circuit robust tofeedback mistuning might be able to underlie perceptualdecision-making, without a strong compromise inperformance.
AcknowledgementsThe authors thank Adrienne Fairhall, Michael Shadlen, and their respectivelab groups, for numerous helpful discussions. The authors also acknowledgefunding support from the Northwest Center for Neural Engineering, and aBurroughs-Wellcome Fund Career Award at the Scientific Interface (E.S.-B.).
Author details1Department of Applied Mathematics, University of Washington, Seattle, WA,98195, USA. 2Department of Physiology and Biophysics, University ofWashington, Seattle, WA, 98195, USA.
Published: 20 July 2010
References1. Roitman J, Shadlen M: Response of neurons in the lateral intraparietal
area during a combined visual discrimination reaction time task. JNeurosci 2002, 22:9475-9489.
2. Wang XJ: Decision Making in Recurrent Neuronal Circuits. Neuron 2008,60:215-234.* Correspondence: [email protected]
1Department of Applied Mathematics, University of Washington, Seattle, WA,98195, USA
Cain et al. BMC Neuroscience 2010, 11(Suppl 1):P97http://www.biomedcentral.com/1471-2202/11/S1/P97
© 2010 Cain et al; licensee BioMed Central Ltd.
3. Seung S, Lee D, Reis B, Tank D: Stability of the memory of eye position ina recurrent network of conductance-based model neurons. Neuron 2000,26:259-271.
4. Koulakov A, Raghavachari S, Kepecs A, Lisman J: Model for a robust neuralintegrator. Nat Neurosci 2002, 5(8):775-782.
5. Goldman M: Robust Persistent Neural Activity in a Model Integrator withMultiple Hysteretic Dendrites per Neuron. Cereb Cortex 2003,13(11):1185-1195.
doi:10.1186/1471-2202-11-S1-P97Cite this article as: Cain et al.: The effect of “robust” integratordynamics on decision-making performance. BMC Neuroscience 201011(Suppl 1):P97.
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