To determine whether an input of a certain amplitude can be “detected” given a specific set of spike trains we use the log likelihood ratio (LLR).

Log likelihood ratios (relative to a homogeneous Poisson model) increase monotonically with the increasing fraction of observed inputs (Fig.

In general, if we have two models H1 and Hz with Poisson observations the log likelihood ratio is given by where the two models have conditional intensities defined by 11 and 12 (log base 2 is used LLR (H 1,H2) * log2 when reporting bits).

Importantly, the log likelihood ratio quantifies the relative accuracy of the two models.

For instance, when H2 is a homogeneous Poisson model that only describes the mean firing rate, the log likelihood ratio quantifies how much more accurately spikes are predicted by the model H1 over just predicting the mean.

D) Detectability of synaptic connections from spike trains: Dependence of the log likelihood ratio between Models M1 and M2 on the input amplitude.

F) Dependence of the log likelihood ratios of models M1 and M2 relative to a homogeneous Poisson process on the length of data used for analysis.

They have less impact on the postsynaptic firing, and thus are less accurate in predicting output spikes compared to excitatory inputs of the same magnitude (the log likelihood ratios comparing Model 2 with coupling to Model 1 with spike-history alone are 58i2% smaller for inhibitory inputs).

The activity of neurons in LIP was correlated to the log likelihood that the targets are baited [5].

To investigate the influence of log likelihood on the activity of the memory units, we computed log likelihood ratio (logLR) quintiles as follows.

To determine how the activity of memory units depended on the log likelihood that the targets were baited we first compared their average activity after observing a complete sequence of the lower and upper quintile, and reordered the quintiles so they were increasing for each unit.

For each model we sought parameters which maximized the log likelihood of (minimized the negative log likelihood ) of the observed consumption choices of each participant.

Fixed effects model comparison was performed at the group level by summation of log likelihoods across participants.

Model comparison used the Bayesian Information Criterion (BIC) [58] , Where and L is the maximized group level log likelihood , k is the number of free parameters in the model and n the number of independent observations.