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Remembering to decide: discrimination of temporally separated stimuli (selecting the best apple) Paul Miller Brandeis University

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Parametric Working Memory and Sequential Discrimination Experiments by group of R. Romo et al., UNAM Nature 399:470 (1999), Cereb. Cort. 13:1196 (2003)

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Choose f1 > f2 f2f1

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or f2 > f1 f1f2

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Rastergram: f1(Hz) 10 14 18 22 26 30 34 basedelay Trial-averaged firing rate Firing rate (Hz) 0 30 Time (sec)0.5 3.5 (from Miller et al. Cerebral Cortex 2003) Tuning curve of memory activity Firing rate (Hz) Stimulus, f1 (Hz) 5 18 1034 Romo et al. Nature 1999

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A continuous attractor acts as an integrator Time Input Memory activity

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... but integration yields magnitude x time Time Input Memory activity

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Problem: How can a network compare an incoming stimulus with an earlier one in memory? Especially as discrimination ≡ subtraction whereas integration ≡ addition Sequential Discrimination Integral feedback control: memory neurons (M) inhibit their inputs (D). Solution: - + ∫ r D dt Input

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rDrD rMrM I D =Input -W MD r M t t t t cue1delaycue2 cue1 delay cue2 cue 1 low cue1delaycue2

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Input rDrD rMrM I D =Input -W MD r M t t t t cue1delaycue2 cue1 delay cue2 cue 1 low cue1delaycue2

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Input rDrD rMrM I D =Input -W MD r M t t t t cue1delaycue2 cue1 delay cue2 cue 2 higher cue1delaycue2

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Input rDrD rMrM I D =Input -W MD r M t t t t cue1delaycue2 cue1 delay cue2 cue 2 lower Threshold not reached cue1delaycue2

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Input rDrD rMrM I D =Input -W MD r M t t t t cue1delaycue2 cue1 delay cue2 cue 1 high cue1delaycue2

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Input rDrD rMrM I D =Input -W MD r M t t t t cue1delaycue2 cue1 delay cue2 cue 2 lower Threshold not reached cue1delaycue2

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Input rDrD rMrM I D =Input -W MD r M t t t t cue1delaycue2 cue1 delay cue2 cue1delaycue2 cue 2 higher

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A continuous attractor for memory

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Feedback too high Gradient is proportional to difference between current needed to produce a firing rate and the feedback current generated by that firing rate.

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Feedback too high Gradient is proportional to difference between current needed to produce a firing rate and the feedback current generated by that firing rate.

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Feedback too high Gradient is proportional to difference between current needed to produce a firing rate and the feedback current generated by that firing rate.

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Feedback too low Gradient is proportional to difference between current needed to produce a firing rate and the feedback current generated by that firing rate.

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Feedback too low Gradient is proportional to difference between current needed to produce a firing rate and the feedback current generated by that firing rate.

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Feedback too low Gradient is proportional to difference between current needed to produce a firing rate and the feedback current generated by that firing rate.

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Continuous or discrete memory? Note psychophysics: for most continuous quantities, we can only remember (even recognize?) them in discrete categories Except when quantity is encoded across different neurons (eg vision, pitch)

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Simulation results Look at Discriminating neuron Memory = Discrete Integrator

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Activity of model discriminating neuron. basedelaycomparison

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basedelaycomparison Activity of model discriminating neuron.

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Trial-averaged firing rate through time of model discriminating neuron for different pairs of stimuli f1 = 34Hz f1 = 10Hz f2>f1 f2

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Base tuning Comparison tuning Delay tuning f2>f1 f2

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Trial-averaged firing rate through time from experimental data of Romo (prefrontal cortex) Base, f1 Delay Comparison, f2 Time (sec) 0 0.5 3.5 4 0 35 Firing rate (Hz) f2>f1 f2

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PFC cell from Romo's data: Initial tuning +ve to f1 : final tuning to +f2-f1 Base, f1 Delay Comparison, f2 Time (sec) 0 0.5 3.5 4 0 60 Firing rate (Hz) f2>f1 f2

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PFC cell from Romo's data Initial tuning -ve to f1 : final tuning to +f1-f2 Base, f1 Delay Comparison, f2 Time (sec) 0 0.5 3.5 4 0 35 Firing rate (Hz) f2

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Decision-making as a competition between pools

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f1=22Hz Probability of choosing f2>f1 from simulations

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f1=14Hz f1=22Hz Probability of choosing f2>f1 from simulations

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f1=14Hz f1=22Hz f1=30Hz Probability of choosing f2>f1 from simulations Miller, in preparation

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Probability of choosing f2>f1 from experiment f1 = 20Hzf1 = 30Hz f2

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Probability of choosing f2>f1 from experiment = fix f2 (20Hz), vary f1 = fix f1 (20Hz), vary f2

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Probability of choosing f2>f1 from experiment Hernandez et al, 1997 = fix f2 (20Hz), vary f1 = fix f1 (20Hz), vary f2 = fix f2 (30Hz), vary f1 = fix f1 (30Hz), vary f2

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fixed f1=22Hzfixed f1=30Hz Probability of choosing f2>f1 from simulations

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fixed f1=22Hzfixed f1=30Hz Probability of choosing f2>f1 from simulations fixed f2=22Hzfixed f2=30Hz

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Input rDrD rMrM I D =Input -W MD r M t t t t cue1delaycue2 cue1 delay cue2 cue 1:low Is magnitude dissociated from duration of input?

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Input rDrD rMrM I D =Input -W MD r M t t t t cue1delaycue2 cue1 delay cue2 cue 1:longer Is magnitude dissociated from duration of input?

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Duration of initial stimulus:= 0.5s Is magnitude dissociated from duration of input? Simulation results

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Duration of initial stimulus:= 0.5s = 0.25s Is magnitude dissociated from duration of input? Simulation results

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Duration of initial stimulus:= 0.5s = 0.25s = 0.75s + Is magnitude dissociated from duration of input? Simulation results

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From Luna et al., Nat Neurosci 2005 Is magnitude dissociated from duration of input? Experimental results

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