Development of relative error of LCM period for different integrators.

<p>The initial period in time has already a small relative error. This error disappears completely over subsequent periods.</p

Activity of the read-out neuron for three input frequencies.

<p>Three mutually inhibitory neurons with STD were driven by three frequencies of 13.33 Hz (75 ms), 1.29 Hz (36 ms) and 11.11 Hz (90 ms). The emergent fundamental frequency is 1.11 Hz (900 ms).</p

The adaptation of the neural activity by the STD model is independent from the membrane conductance model.

<p><b>A</b> Activity of two neurons and showing the LCM at the fundamental frequency of 11.9 Hz (84 ms) for a network constructed using the Hodgkin-Huxley conductance model. <b>B</b> Activity of neurons and showing the fundamental frequency of 1.85 Hz, using the Fitzhugh-Nagumo model. <b>C</b> The network of HH neurons and synaptic transmission as in panel lbfa, but without adaptation. The neurons function as simple coincidence detectors and do not converge to the LCM. Due to the difference in periodicity of the two neurons they will eventually go completely out of phase. <b>D</b> Phase diagram of the relative change in membrane voltage for the HH network versus the relative change in timing induced by deterministic LTD adaptation. Shown are the stable phase differences for neurons (blue) and (green) for at least 20 periods. The phase difference was calculated by determining the timing differences of spiking events for individual periods between synapses connecting and of the adapted network and the non-adapted network. The corresponding difference in amplitude of the membrane activity for each event provides the relative change.</p

Frequency response model with Short Term Depression.

<p>The network neural activity is selected from existing frequencies in the compound input using characteristic activity of the onset neurons. <b>A</b> Scheme of a plausible microcircuit in the ventral cochlear nucleus. <b>B</b> Burst activity of the receptor neuron with compound input QFM (black). <b>C</b> Chopper neuron activity resulting in an 1800 ms emergent frequency as part of its unique pattern due to the characteristic frequency of . <b>D</b> Chopper neuron activity with 1800 ms activity pattern and unique activity pattern due to .</p

Phase and delay independence of the simple STD model.

<p>Two separate simulations of two mutually inhibitory neurons with STD where one simulation has zero phase difference (blue) in input and one simulation has 65 ms phase difference (green) in input. The emergent frequency, as shown by the read-out neuron , remains the same throughout any phase or delay difference.</p

Short Term Plasticity in cortical layer 5 microcircuitry.

<p><b>A</b> Short term facilitation of the pyramidal-Martinotti synapse, shown is the activity of the Martinotti cell when the pyramidal cell is stimulated at 20 Hz, 40 Hz and 70 Hz. <b>B</b> Two pyramidal and one Martinotti neuron with STF, the Martinotti neuron is stimulated at 70 Hz with adaptive post-synaptic IPSPs at neurons (black) and (blue). <b>C</b> Complete microcircuit with STF, except the to synapse with STD. is stimulated at 40 Hz which results in a single AP in that terminates the stimulation burst in and causes a large IPSP in .</p

Competition between neurite branches in a complex morphology.

<p>(A) Example morphology of a reconstructed pyramidal neuron with apical and basal dendrites. (B) In the control case, starting from the reconstructed morphology, the neuron was allowed to grow out for 10 hours in the model. The simulation was then repeated with the same initial conditions, but with increased polymerization rate for one of the growth cones. The dendritic morphology obtained in this last simulation is represented by a dendrogram, colored according to the tubulin concentration in the branches. The gray vertical lines at the terminal segments indicate the starting morphology, and the black vertical lines show the neurite length after 10 hours in the control case. The black dot marks the growth cone with increased polymerization rate. (C) The competition between branches increases with increasing path distance to the soma. The graph shows the total retraction of all neurites, divided by the growth of the modified growth cone, as a function of path length between the modified growth cone and the soma.</p

The Continuous Temporal Expectancy Task results in large variation in reaction times.

<p>(A) Illustration of the CTET paradigm (adapted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196907#pone.0196907.ref044" target="_blank">44</a>]) with stimuli presented for either 600 ms (900 ms experiment 2) if they were standard stimuli or for 1200 ms (1600 ms experiment 2) if they were targets. (B) Example sequence of reaction times exhibiting large variation to the 100 target images shown.</p

Neurite outgrowth of a developing neuron in tissue culture.

<p>(A) Still shots of a time-lapse movie of a developing cerebellar neuron in tissue culture, revealing neurites that are growing out and retracting. The arrows point to the neurites' growth cones; color of arrows corresponds to colors used in panels B and C. Figure taken from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086741#pone.0086741-DaFontouraCosta1" target="_blank">[3]</a>. (B) The red and green neurites are forced to grow out as in the experiment (dashed black lines), whereas the blue neurite is fully controlled by the tubulin dynamics of the model. The parameters of the model (diffusion constant, active transport rate, tubulin decay and tubulin soma concentration) were optimized so as to make the blue neurite grow as closely as possible to the experimental data. (C) Using the optimized parameter set from B, the green neurite is now fully governed by the model, whereas the red and blue neurites are forced to grow according to data recorded in the experiment. The errors in B and C are the square root of the summed squared deviation of the free growth cone from the experimentally measured location at each point in time.</p

More mind wandering episodes are associated with increased LRTC of response-time series.

<p>The Correlation between DFA the of the subjective rating of attention, or mind wandering episodes shows that variability increases with more mind wandering (R² = .43, <i>p</i> = .01).</p