Try this code:

ex_nn<-explain(model_nn,
               data = df[, -1],
               y = df[, 1],
               predict_function = function(x, y) compute(x, y)$net.result)

Is this okay?

Oh you are fast...! Your proposition is not far from a solution that i was trying since a few hours.

But the plot part seems to not work (the neural network model is flat as the variably "Y" is missing in ex_nn):

set.seed(13)
N <- 250
X1 <- runif(N)
X2 <- runif(N)
X3 <- runif(N)
X4 <- runif(N)
X5 <- runif(N)

f <- function(x1, x2, x3, x4, x5) {
((x1-0.5)2)^2-0.5 + sin(x210) + x3^6 + (x4-0.5)2 + abs(2x5-1)
}
y <- f(X1, X2, X3, X4, X5)

library(randomForest)
library(DALEX)
library(e1071)
library(rms)
library(neuralnet)

df <- data.frame(y, X1, X2, X3, X4, X5)

model_rf<-randomForest(y~., df)
model_svm<-svm(y~., df)
model_lm<-lm(y~., df)
model_nn<-neuralnet(y~X1+X2+X3+X4+X5,df,hidden=1)

dd <- datadist(df)
options(datadist="dd")
model_rms <- ols(y ~ rcs(X1) + rcs(X2) + rcs(X3) + rcs(X4) + rcs(X5), df)

ex_rf<-explain(model_rf)
ex_svm<-explain(model_svm)
ex_lm<-explain(model_lm)
ex_nn<-explain(model_nn,data = df[, -1],y = df[, 1],predict_function = function(x, y) compute(x, y)$net.result)
ex_rms<-explain(model_rms, label = "rms", data = df[, -1], y = df$y)
ex_tr<-explain(model_lm, data = df[,-1],
predict_function = function(m, x) f(x[,1], x[,2], x[,3], x[,4], x[,5]),
label = "True Model")

library(ggplot2)
plot(single_variable(ex_rf, "X1"),
single_variable(ex_svm, "X1"),
single_variable(ex_lm, "X1"),
single_variable(ex_nn, "X1"),
single_variable(ex_rms, "X1"),
single_variable(ex_tr, "X1")) +
ggtitle("Responses for X1. Truth: y ~ (2*x1 - 1)^2")

Problem solved... my last post was fucking stupid! Everything works great!

So, what was the problem?

The code you give me made the trick. I was just biased by the result! Stupid human brain!