Stimulus onset quenches variability.

<p>(a) and (b) Sample spike trains from two representative neurons aligned to the stimulus presentation (shaded area). (c) and (d) The population average of the Fano factor (FF) decreases with stimulus onset. The FF is only computed for units that do not receive direct sensory input. These results mimic Fig 5 of [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004640#pcbi.1004640.ref010" target="_blank">10</a>]. FFs, mean rates and variances are for causal moving windows of 5 time steps. (c) was computed for the presentation of stimulus “AXXX_ _ _ …” during the test phase after being presented with a probability of 0.1 during self-organization. (d) In turn, “BXXX_ _ _ …” had a probability of 0.9 in the same experiment. Error bars represent SEM over 20 independent realizations.</p

Spontaneous activity predicts evoked activity and decisions.

<p>The network self-organizes during repeated presentations of the sequences “AXXX_ _ _ …” (33%) and “BXXX_ _ _ …” (67%) with blank intervals in between. In the test phase, an ambiguous mix of cue “A” and cue “B” is presented (“A/BXXX_ _ _ …”, shaded area) and the network decides at the first “_” with a linear classifier if either A or B was the start of the sequence. (a) Trial-to-trial variability of the evoked activity patterns during the test phase is well predicted from activity prior to stimulus onset. The figure shows the correlation between the variable evoked activity patterns at different time steps after stimulus onset and a linear prediction of these based on the stimulus and either the spontaneous activity state prior to stimulus onset (blue) or trial-shuffled spontaneous activity (baseline, grey). Similar to Fig 4 in [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004640#pcbi.1004640.ref008" target="_blank">8</a>], the decay of the correlation is exponential-like. The correlation drops further as new stimuli are presented (hatched area). Due to variable inter-trial-intervals, the hatched area covers the entire area were stimulation is possible. (b) The decisions of the network can be predicted from spontaneous activity before stimulus onset. The plot shows the accuracy of predicted network decisions (“A” vs. “B”) at the green dashed line from activity surrounding the decision. Separate classifiers were trained for each of the 11 ambiguity classes (e.g. 20%A) and time step surrounding the decision. The grey line corresponds to predictions from trial-shuffled activity. Predictions in (b) are averaged over all priors of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004640#pcbi.1004640.g007" target="_blank">Fig 7c</a>. Error bars represent SEM over 20 independent realizations.</p

Different priors are learnt by the network.

<p>During self-organization, the sequences “ABCD” and “EFGH” were randomly interleaved with frequencies of 67% and 33%, respectively. This is reflected in the relative occurrence of (a) each letter and (b) each word in the spontaneous activity. For different priors during self-organization, this results in the frequencies in (c) for each letter and in (d) for each word. Both show overlearning effects in that their frequencies are biased in favour of the word that was shown more often. The reversing trend and high variance for the extreme priors (0.1 and 0.9) can be accounted for by pathological network dynamics for some simulations with these priors. The letter frequency is the observed frequency in the evoked activity while the word frequency was normalized over the total number of observed words (“ABCD”, “EFGH”, “DCBA”, and “HGFE”) to yield better comparison over different realizations. Error bars represent SEM over 20 independent realizations.</p

Prior and ambiguous information is combined in network decisions.

<p>(a) As in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004640#pcbi.1004640.g006" target="_blank">Fig 6</a>, the network self-organizes during repeated presentations of the sequences “AXXX_ _ _ …” (33%) and “BXXX_ _ _ …” (67%) with blank intervals in between. In the test phase, an ambiguous mix of cue “A” and cue “B” is presented (“A/BXXX_ _ _ …”) and the network decides at the first “_” with a linear classifier if either A or B was the start of the sequence. The fraction of decisions for “A” or “B” at the first blank state “_” approximates the integration of cue likelihood and prior stimulus probability as expected from the probabilistic model (grey dashed line) for the given prior (<i>p</i>(A) = 0.33). The probabilistic model was fitted by grid search over its two parameters to the parameters that had the smallest accumulated error over all priors. (b) For the “Only IP” condition, STDP was deactivated during the self-organization phase and a new probabilistic model was fitted. (c, d) The intersections of the decisions for different priors for our simulations (blue line) and the probabilistic model (grey dashed). The performance was evaluated for the same simulation as Figs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004640#pcbi.1004640.g005" target="_blank">5</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004640#pcbi.1004640.g006" target="_blank">6</a>. Error bars represent SEM over 20 independent realizations.</p

The network learns a predictive model mimicking [31].

<p>Similar to [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004640#pcbi.1004640.ref031" target="_blank">31</a>], we stimulated the network either with the sequence “ABCD” (experiment) or with all permutations of this sequence (control). After a period of self-organization, the network was tested with the sequences “ABCD” and “DCBA”. (a) Mean firing rates (“sequence magnitude”) for both test sequences. (b) For the experimental condition, the network was also tested with the sequences “ABCD”, “A_CD”, and “E_CD”. Error bars represent SEM over 20 independent realizations. ⋆ indicates <i>p</i> < 0.05 for a two-sided t-test assuming independent samples and identical variances.</p

Spontaneous and evoked activity align through self-organization.

<p>The network was stimulated with the words “ABCD” (67%) and “EFGH” (33%). (a) The spontaneous activity follows the spatiotemporal trajectories of the evoked states in the PCA projection. (b) In the multidimensional scaling projection, the evoked activity (red) follows the spontaneous outline (black) and avoids the shuffled spontaneous states (blue) (cp. Fig 6c of [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004640#pcbi.1004640.ref013" target="_blank">13</a>]). (c) The evoked states are closer to the spontaneous states than to the shuffled spontaneous states: As in Fig 6 of [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004640#pcbi.1004640.ref013" target="_blank">13</a>], the distance from evoked states to the closest spontaneous states (D_spont) is smaller than the distance to the closest shuffled spontaneous state (D_shuff). The red dashed line shows equality. (d) Spontaneous activity becomes more similar to evoked activity during learning: After self-organizing to “ABCD” and “EFGH” with identical probabilities, spontaneous activity was compared to the evoked activity from the imprinted sequences (natural) or the two control sequences “EDCBA” and “FGH” (control) with Kullback-Leibler divergence. New networks were generated for each training time and condition. Error bars represent SEM over 50 independent realizations. ⋆ indicates <i>p</i> < 0.05 for a t-test assuming independent samples and identical variances.</p

Example of a texture profile analysis (TPA) curve.

<p>This kind of curve was obtained for a cereal food bolus collected after mastication. Hardness is taken as the maximal force reached during the first compression. Adhesiveness is the area under the negative curve after the first compression, representing the work done to pull the food bolus apart in tension. Cohesiveness is the ratio of the area under the second compression curve to the area under the first compression. Springiness is the duration of the contact between the piston tool and the bolus during the second compression divided by the duration of the contact during the first compression.</p

Labelling of the different boluses collected along the masticatory sequence.

<p>Labelling of the different boluses collected along the masticatory sequence.</p

Sequential analysis of bolus characteristics.

<p>Changes observed during the progress of masticatory sequence in mechanical hardness (A), adhesiveness (B), cohesiveness (C) and springiness (D) calculated on data obtained from TPA performed at 65% deformation, associated changes in proportions of subjects perceiving hardness (E) stickiness (F) and dryness (G) as being dominant in the bolus, and bolus median particle size (H). Bars represent slopes calculated between values from two consecutives boluses.</p

Bolus characteristics analysis.

<p>Median particle size (<i>d</i>₅₀<i>)</i> [A], hardness (in N), adhesiveness (in N.s), springiness and cohesiveness measured at 65% [B] or 20% [C] of deformation of the bolus are presented for each bolus collected at several time points between the beginning and end of the complete masticatory sequence (B1c to Bsw). Dominance rate for hardness, stickiness and dryness perceptions were calculated from 50 observations and are shown at the same time points as physical measurements [D]. Springiness and cohesiveness are dimensionless. Significant differences between two consecutive boluses are shown with lower-case letters for granulometric and rheological data (<i>P</i><0.05), and with * for sensory data (<i>P</i><0.15). Although a very small significance was observed for springiness measured at 65% deformation, no significant difference was noted with SNK test between consecutive boluses. Results for physical measurements are means ± SEM (<i>N</i> = 20). Points obtained for a given variable are joined up to improve readability.</p