Journal: Journal of Neurophysiology

Article Title: Efficient reinforcement learning of a reservoir network model of parametric working memory achieved with a cluster population winner-take-all readout mechanism

doi: 10.1152/jn.00378.2015

Figure Lengend Snippet: A: schematic diagram of the model. The 4 layers in the model are the input layer (IL), the reservoir network layer (RNL), the cluster population readout layer (CPL), and the decision making output layer (DML). See materials and methods for details. B: schematic diagram of the delayed discrimination task. Two vibration stimuli are presented. Each stimulus lasts for 0.5 s. There is a 3-s delay between the 2 stimuli. The task is to report the frequency of which stimulus is larger. C: performance of the model. Each number indicates average % of correct trials for pairs of stimuli (f1, f2) in all simulation rounds during the test phase. The vertical column is the performance when f1 is fixed at 0.6 Hz, and the horizontal column is the performance when f2 is fixed at 0.6 Hz. D: similar to C, but the difference between f1 and f2 is fixed at ±0.3 Hz. E: performance of the model when f1 (gray curve) or f2 (black curve) is fixed at 0.6 and the other stimulus frequency changes. Error bars show SE.

Article Snippet: By introducing feedback connections from the CPL and the DML to the RNL, we may train the RNL and obtain results that match the experimental data more closely ( Sussillo and Abbott 2009 ).

Techniques:

Journal: Journal of Neurophysiology

Article Title: Efficient reinforcement learning of a reservoir network model of parametric working memory achieved with a cluster population winner-take-all readout mechanism

doi: 10.1152/jn.00378.2015

Figure Lengend Snippet: CPL network characteristics. A: performance of the model depends on the number of clusters and the number of neurons within each cluster of the CPL. Colors indicate the performance. Dotted lines are the iso-number lines indicating the model's performance when the total numbers of neurons are 10,000, 20,000, and 30,000. Each data point is the average from 10 runs. B: model performance critically depends on the strength of the competition. Each data point is 10 rounds of simulation runs, each of which includes 200 training and 200 testing trials. C: Pearson correlation coefficients between activities of all the neurons of the RNL and the CPL at the end of f2 presentation. D: mean correlation coefficients of the CPL neurons for the same stimulus condition trials and for the different stimulus condition trials. Inset histogram shows the distribution of the difference between the correlation coefficients between 2 conditions.

Article Snippet: By introducing feedback connections from the CPL and the DML to the RNL, we may train the RNL and obtain results that match the experimental data more closely ( Sussillo and Abbott 2009 ).

Techniques: