Perceptual learning and decision making: an integrated neuro-physiological, behavioral and computational approach mario pannunzi Barcelona, 26 XI 2009.

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Perceptual learning and decision making: an integrated neuro-physiological, behavioral and computational approach mario pannunzi Barcelona, 26 XI 2009

the experiment Sigala and Logothetis, Nature 2002

learning scenario inspired by the Sigala-Logothetis experiment diagnostic feature: eye distance non-diagnostic feature: mouth height D1O1O2D2 classification task in which stimuli are defined by combinations of ‘diagnostic’ and ‘non-diagnostic’ features to be learnt: association between diagnostic feature and ‘motor response’, irrespective of non-diagnostic feature D1 ® Left, D2 ® Right metaphor:

the model a WTA with two layers: feature’s layer (IT?) category’s layer (PFC?) stimulus protocol

learning mechanism D1 ® Left D2 ® Right Bailey et al., Nature review 2000, Reynolds et al., Neural Network 2002

learning mechanism D1 ® Left D2 ® Right Fusi et al., Neural Comp. 2000

Our model is suitable to reproduce experimental results in a minimal model and can be verified by an experiment similar to the Sigala-Logothetis ones. The existence of plasticity of top-down synapses pose various questions, between them we try to answer to these ones: does the structure in the feedback synapse’s matrix due to plasticity increase the percentage of correct answers? and when the stimuli were ``corrupted''? does this structure accelerate the dynamics of the network, in order to have faster answers? does the plasticity in the feedback synapses accelerate the learning process? main objectives

results I: synaptic structure L D1D2 R O1O2 L D1D2 R O1O2 R P = fraction of potentiated synapses R 0 = 0.5 = fraction of potentiated synapses at the beginning J = R P - R 0 J

results: comparison with experimental data starting trials intermediate trials ending trials time

verify the model correct answer wrong answer

adding some non- diagnostic features Left (C1) Right (C2) (D1) (D2)(O1b)(O2b) (O1b)(O2b)(O1c)(O2c) (O1d)(O2d)(O1e)(O2e) one diagnostic feature and various non- diagnostic features

performances does the plasticity in the feedback synapses increase the percentage of correct answers? and when the stimuli were ``corrupted''?

time decision does the plasticity in the feedback synapses accelerate the dynamics of the network, in order to have faster answers? 4 non-diagnostic features 8 non-diagnostic features 16 non-diagnostic features

learning dynamics does the plasticity in the feedback synapses accelerate the learning process? and in this case, how does it work?

thanks for your attention.