A Illustration of the effects of diffusion (top) and additional drift (bottom) on the temporal evolution of distributions of initial positions p(start) towards distributions of final positions p(end) over 6.5s of delay activity. The bump is always represented by its center position φ. Two peaks in the distribution of initial positions φ(0) and their corresponding final positions φ(6.5) are highlighted by colors (purple, red), together with example trajectories of the center positions. Top: Diffusion symmetrically widens the initial distribution. Bottom: Strong drift towards one single fixed point of bump centers (φ = 0) makes the origin of trajectories indistinguishable. B Normalized mutual information (MI, see text for details) of distribu...
Progress has been made in understanding how temporal network features affect the percentage of node...
<p>(A) Snapshot of the simulation with periodically reversing agents (<i>η</i> = 0.24) at 180 min of...
Temporal contact networks are studied to understand dynamic spreading phenomena such as communicable...
All networks have the same instantiation of random connectivity (p = 0.5), similar to Fig 1B1. A Cen...
A Center positions of 20 repeated simulations of the reference network (U = 1, τx = 150ms) for 10 di...
A Expected magnitude of drift fields as a function of the sparsity parameter p of recurrent excitato...
Networks with continuous set of attractors are considered to be a paradigmatic model for parametric ...
A While a bump (“Bump”) is centered at an initial angle φ0 (chosen to be 0), additional external inp...
We discuss various network mechanisms capable of making spatial working memory more robust to noise ...
A Excitatory (E) neurons (red circles) are distributed on a ring with coordinates in [−π, π]. Excita...
<p>This graph illustrates how the position of the cue affects the position of the bump after retriev...
<p>The panels summarise the results of simulations conducted as for Fig. 3 except for two factors. F...
<p><b>A–D.</b> Diffusion coefficient α varying from 0.1–1000, drift coefficient β is constant at 10....
<p>Each curve shows the evolution of effectual connectivity as a function of time for different pa...
Working memory is imprecise, and these imprecisions can be explained by the combined influences of r...
Progress has been made in understanding how temporal network features affect the percentage of node...
<p>(A) Snapshot of the simulation with periodically reversing agents (<i>η</i> = 0.24) at 180 min of...
Temporal contact networks are studied to understand dynamic spreading phenomena such as communicable...
All networks have the same instantiation of random connectivity (p = 0.5), similar to Fig 1B1. A Cen...
A Center positions of 20 repeated simulations of the reference network (U = 1, τx = 150ms) for 10 di...
A Expected magnitude of drift fields as a function of the sparsity parameter p of recurrent excitato...
Networks with continuous set of attractors are considered to be a paradigmatic model for parametric ...
A While a bump (“Bump”) is centered at an initial angle φ0 (chosen to be 0), additional external inp...
We discuss various network mechanisms capable of making spatial working memory more robust to noise ...
A Excitatory (E) neurons (red circles) are distributed on a ring with coordinates in [−π, π]. Excita...
<p>This graph illustrates how the position of the cue affects the position of the bump after retriev...
<p>The panels summarise the results of simulations conducted as for Fig. 3 except for two factors. F...
<p><b>A–D.</b> Diffusion coefficient α varying from 0.1–1000, drift coefficient β is constant at 10....
<p>Each curve shows the evolution of effectual connectivity as a function of time for different pa...
Working memory is imprecise, and these imprecisions can be explained by the combined influences of r...
Progress has been made in understanding how temporal network features affect the percentage of node...
<p>(A) Snapshot of the simulation with periodically reversing agents (<i>η</i> = 0.24) at 180 min of...
Temporal contact networks are studied to understand dynamic spreading phenomena such as communicable...