upgrade deps

Project.toml

@@ -17,7 +17,6 @@ JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-NNlibCUDA = "a00861dc-f156-4864-bf3c-e6376f28a68d"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
@@ -29,7 +28,7 @@ Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 [compat]
 ArgParse = "1"
 BatchedBLAS = "0.2"
-CUDA = "3.4, 4"
+CUDA = "4, 5"
 Cairo = "1"
 CodecZlib = "0.7"
 Compose = "0.9"
@@ -38,8 +37,7 @@ Fontconfig = "0.4"
 Gadfly = "1"
 JLD2 = "0.4"
 NLsolve = "4"
-NNlib = "0.7.28, 0.8"
-NNlibCUDA = "0.2"
+NNlib = "0.9"
 StatsBase = "0.33, 0.34"
 SymmetricFormats = "0.1"
 UnPack = "1"

src/TrainSpikingNet.jl

@@ -1,7 +1,7 @@
 module TrainSpikingNet
 
 using LinearAlgebra, LinearAlgebra.BLAS, Random, JLD2, StatsBase, Statistics, SymmetricFormats, UnPack, CodecZlib, Printf
-using CUDA, NNlib, NNlibCUDA, BatchedBLAS
+using CUDA, NNlib, BatchedBLAS
 
 using NLsolve
 

src/plotfn.jl

@@ -61,7 +61,9 @@ function plot(test_file; ineurons_to_plot = 1:16)
  utotal1 = utotal[1,itask][:,ci]))
  utotal_ci = hcat((x[:,ci] for x in utotal[:,itask] if !ismissing(x))...)
  df[!,:utotal_ave] = dropdims(median(utotal_ci, dims=2), dims=2)
- df[!,:utotal_disp] = dropdims(mapslices(mad, utotal_ci, dims=2), dims=2)
+ df[!,:utotal_disp] = dropdims(mapslices(x->mad(x, normalize=true),
+ utotal_ci, dims=2),
+ dims=2)
  transform!(df, [:utotal_ave, :utotal_disp] => ByRow((mu,sigma)->mu+sigma) => :utotal_upper)
  transform!(df, [:utotal_ave, :utotal_disp] => ByRow((mu,sigma)->mu-sigma) => :utotal_lower)
  push!(ps, Gadfly.plot(df, x=:t, y=Col.value(:utarg, :utotal_ave, :utotal1),