At the start of every time step , feedforward connections propagate information from the sensory layer to the association layer through modifiable connections vij.

Moreover, an action a at time step (tl) may have caused a change in the sensory stimulus.

In the model, the new stimulus causes feedforward processing on the next time step t, which results in another set of Q-values.

Where qk(t) aims to represent the value of action k at time step t, i.e.

The RPE is high if the sum of the reward r(t) and discounted qa/(t) deviates strongly from the prediction qa(t-1) on the previous time step .

We will first establish the equivalence of online gradient descent defined in Equation (19) and the AuGMEnT learning rule for the synaptic weights wflt) from the regular units onto the time step t-1 should change as: leaving the other weights k7éa unchanged.

It follows activity of association unit j on the previous time step .

Every trial started with an empty screen, shown for one time step .

This was followed by a memory delay of two time steps during which only the fixation point was Visible.

the time average of k A, j is obtained at every time step by spatially integrating the value of k A, j weighted by the pdf of the unbound kinesins.

By using this method, the amount of computation reduces significantly because the time step can be determined by the dynamics of bound kinesins not by the fluctuating motion of the unbound kinesin.

Instead of calculating the rapidly changing value of k A, j over time, the time average of the transition rate is used to avoid intensive computations With excessively short time steps for capturing the changes.

The dynamics of the cargo, the chemical reactions and the unbinding are considered for bound kinesins, and rebinding to the MT are considered for unbound kinesins at every time step .

If two or more kinesins are attached on the cargo, the probability distribution of rebinding for every unbound kinesin is also calculated at every time step .

5 (g)) are larger than ral at a certain time step , the rebinding occurs.

If the challenger is unsuccessful it will remain displaced for a time step .

Note that following Equations 1 and 3, c is 1 minus the proportion of the population that each food rail can support on one time step .

[5] the position of the IT is set at a distance of 1 from the initial nutritional state and individuals can eat a maximum ((p) of \/§ / 500 per time step .

First, we recorded for each individual the mean of the angular difference between the food rail for the food consumed at each time step and the ideal food rail that would guide them to the IT (fl, see Models, Equation 5).

At every time step individuals perform the following four processes (name of process given in parentheses) in the given order: (1) those without a food source select a food at random and fight for it if necessary (‘Choose Food’), (2) calculate their appetite and eat (‘Eat’), (3) recalculate their fitness based on their nutritional state (‘Calcu-late Fitness’) and (4) may spontaneously leave a food source with a probability that is function of nutritional latitude, K (‘Leave’).

Finally, after a 500 time steps a new generation is spawned from the previous generation, and the parental generation dies (‘Reproduce’).

Note that the amount an individual can eat in one time step has a maximum value of (p.

However, when deciding how to allocate reward over several time steps , the number of possible allocation plans grows exponentially as outcomes further into the future are considered, generating decision-problems of considerable complexity [16].

Given dynamic inconsistency in the discounting function, a naive agent would be expected to change their plans at each time step .

To simulate nai've behavior, the form of the discount function was made dependent on the absolute timing of the outcomes as well as their delay, such that the decision-maker at each time step believed that future decision-makers would apply the same preferences as those currently held for those time-periods.

As a result, at each time step , and each state of capital, all possible consumption levels have equal value.

Given dynamic inconsistency in the discounting function, a naive agent would be eXpected to change their plans at each time step .

In a simulation run of the model, every five time steps bats i and j emit an echolocation call with the aXis of emission corresponding to the bats’ headings.

(i) Every five time steps an individual would send an echolocation call to determine the heading of the other bat.

(ii) The other way a bat may change its behavioural state is by moving from the interacting to the independent state at each time step .

To reproduce this feature in the model an individual in the interaction state may choose, at each 20 ms time step , to enter an independent state with a small probability of 0.05.

Previous theory on the emergence of novel pathogenic strains [21] showed that if mutated strains arise at rate [,4 per time step , the overall emergence probability P is given for yM the maximum immune size for which it is possible for immune escape to arise, and H(y) the emergence probability of an escape mutation were it to appear.

The time step was set to be very low: Ar = 0.00005.

This is because the tau-leaping algorithm is accurate only if the eXpected number of events per time step is small [37]; since the growth rates of the pathogen strains and the lymphocytes are both large, a small time step is needed to make the simulation valid.

That is, the birth rate per time step for each pathogen is Poisson-distributed with mean (R,- - x,- - AT), and death rate with mean (x,- - y - AT) for i = 1, 2.

One time step consists of I of these elemental update steps.

Generate a large number (in this study, at least 100 and usually more than 400) of equilibrium data sets from the non-neutral model With parameters YT, by simulating the model for a sufficient number of time steps .

The number of time steps was chosen to be at least ten times the number of timesteps such that the species richness and Shannon diversity index in the local community appeared to have reached their equilibrium values; this number depends on the model parameters (e.g.

fewer time steps are needed When m is close to 1);

In the free-choice condition, two equally rewarded targets are presented in both hemif1elds 5O time-steps after the beginning of the trial followed by a free-choice cue (i.e., red and green cues simultaneously presented on the center of the screen) 50 time steps later.

In the cued condition, a green or a red cue is presented 50 time steps after the onset of the targets, indicating Which effector should be used to acquire either of the targets.

7 depicts such a scenario with 3 targets, in which the cue is presented 50 time steps after the trial starts, followed by the target onset 50 time steps later.