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Figure 9. Physical model of odor diffusion in behavioral arena. ; (A) Configuration of behavioral arena on which the PDE model is based. The arena consists of a square shaped transparent plastic box with a side length of 120 mm and a height of 12 mm. The lid is inverted on a surface of agarose. The odor source consists of a solution of isoamyl acetate mixed with paraffin oil. A droplet of 3 μl of odor is placed inside a transparent reinforcement ring of a radius rring. This volume fills the ring evenly and, upon inversion of the lid on an agarose slab, the droplet remains suspended due to surface tension. The droplet shape is modeled as a spherical cap. The flat face of the droplet is in contact with the top plastic lid. The volume vdrop of the droplet is related to the radius of the flat face rring and the droplet height hdrop according to the formula of a sphere. The agarose layer at the bottom of the chamber is modeled as a two-dimensional sheet with an independent diffusion constant. The top flat face of the droplet that contacts the plastic cap is treated as a no-flux boundary, and flux continuity is imposed on the spherical interface with air. The remaining boundaries, air-agarose and air-plastic, are modeled as Robin boundary conditions to accommodate the possibility of adsorption-desorption at these boundaries. The establishment of the odor gradient in the arena is modeled by two simultaneous diffusion processes, both of which are described by partial differential equation (PDE): ∂x(r⃑, t)/∂t = D ∇2x(r⃑, t) where x(r⃑, t) denotes the odor concentration at position r⃑ and time t. The diffusion constant D depends on whether the medium is air or the odor droplet. We used a flux continuity condition at the droplet-air boundary. For additional details about the model, see ‘Materials and methods’. (B) As described in Louis et al. (2008), infrared spectroscopy was used to estimate the absorbance and thereby the average concentration along sections of the arena (IR beam depicted in panel A). The time course of the cumulated concentration was determined for 7 sections at a distance from the source ranging from 0 to 45 mm (only first 6 are shown in the graph). Each concentration profile results from an average over 2 to 4 independent measurements. The absorbance was measured for a source concentration of 1.0 M. As discussed in ‘Materials and methods’, the parameters of the model are estimated by optimizing the fit of the model with the average concentration profiles along the 7 sections of the arena. The parameters of the model are reported in Table 5. The PDE model leads to a good fit of the temporal profiles of the average concentrations after an initial transient phase of 30 s. (C) Assessment of variability in the concentration estimates at a fixed position of the center of the 1.0 M odor source (7.5 mm). Mean concentration obtained from four independent infrared measurements. Error bars denote standard deviation. The time course of the simulated concentration is shown as a dashed line. (D) Assessment of variability in the concentration estimates at a fixed position of the center of the 1.0 M odor source (15 mm). Mean concentration obtained from three independent infrared measurements. Error bars denote standard deviation. The time course of the simulated concentration is shown as a dashed line.

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Figure 2. Decision accuracy and reaction time in two speed-accuracy regimes are explained by bounded evidence accumulation. ; (A) Data from two monkeys. Black symbols and lines show the high accuracy regime. Red symbols and lines show the high speed regime. The top graph shows the proportion of correct choices plotted as a function of motion strength. Decision accuracy improved with stronger motion in both regimes. The bottom graph shows mean RT (±SEM) plotted as a function of motion strength. RTs decreased for stronger motion in both regimes. In the regime where the monkeys responded more slowly, their performance improved relative to the other regime. N = 3534 and 3307 trials for the high speed and high accuracy regimes, respectively, for monkey D and 4838 and 4733 trials for the same regimes for monkey E. (B and C) Bounded evidence-accumulation models. Noisy evidence is accumulated until it reaches a terminating threshold or bound, which establishes the time and sign of the decision. (B) Traditionally, this is represented as a single accumulation—or drift-diffusion process—between an upper and lower bound. (C) The model can be described equivalently as a race between two anticorrelated drift-diffusion processes, such that negative evidence for one process is positive evidence for the other. The decision is determined by the first accumulation to reach the bound. The traces in (A) show fits to the data using the bounded accumulation model.

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Sensory-memory interactions via modular structure explain errors in visual working memory

• Authors : Yang, Jun; Zhang, Hanqi; Lim, Sukbin

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Sensory-memory interactions via modular structure explain errors in visual working memory

• Authors : Yang, Jun; Zhang, Hanqi; Lim, Sukbin

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Mapping out Min protein patterns in fully confined fluidic chambers

• Authors : Caspi, Yaron; Dekker, Cees;

• Source: eLife ; volume 5 ; ISSN 2050-084X

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