From 94bebbb2f5c139548c4dcc01ecd9c4110f3f032f Mon Sep 17 00:00:00 2001
From: anandpathak31 <anandpathak31@gmail.com>
Date: Wed, 10 Jan 2024 12:59:05 -0500
Subject: [PATCH 01/10] debugging RF

---
 examples/image_example.csv         | 2050 ++++++++++++++--------------
 examples/image_example_old.csv     | 1025 ++++++++++++++
 src/blox/connections.jl            |   11 +-
 src/blox/discrete.jl               |    2 +-
 src/blox/reinforcement_learning.jl |    2 +-
 5 files changed, 2061 insertions(+), 1029 deletions(-)
 create mode 100644 examples/image_example_old.csv

diff --git a/examples/image_example.csv b/examples/image_example.csv
index 21d7afd6..0d90b0fc 100644
--- a/examples/image_example.csv
+++ b/examples/image_example.csv
@@ -1,1025 +1,1027 @@
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diff --git a/examples/image_example_old.csv b/examples/image_example_old.csv
new file mode 100644
index 00000000..21d7afd6
--- /dev/null
+++ b/examples/image_example_old.csv
@@ -0,0 +1,1025 @@
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diff --git a/src/blox/connections.jl b/src/blox/connections.jl
index a2756f3d..fc2065e8 100644
--- a/src/blox/connections.jl
+++ b/src/blox/connections.jl
@@ -452,8 +452,10 @@ function (bc::BloxConnector)(
     neurons_in = get_inh_neurons(str_in)
 
     t_event = get_event_time(kwargs, nameof(str_out), nameof(str_in))
-    cb_matr = t_event => [sys_matr_in.H ~ IfElse.ifelse(sys_matr_out.H*sys_matr_out.jcn > sys_matr_in.H*sys_matr_in.jcn, 0, 1)]
-    cb_strios = t_event => [sys_strios_in.H ~ IfElse.ifelse(sys_matr_out.H*sys_matr_out.jcn > sys_matr_in.H*sys_matr_in.jcn, 0, 1)]
+    #it should actually be H*jcn (= ρ) but since H should always be reset to 1 at the beginning of each trial (which we are not currently) untill t_event, it makes sense to not keep H here
+    # for a more general version where H can change values anytime, we need to reset H to 1 at the begining
+    cb_matr = t_event => [sys_matr_in.H ~ IfElse.ifelse(sys_matr_out.jcn > sys_matr_in.jcn, 0, 1)]
+    cb_strios = t_event => [sys_strios_in.H ~ IfElse.ifelse(sys_matr_out.jcn > sys_matr_in.jcn, 0, 1)]
     push!(bc.events, cb_matr)
     push!(bc.events, cb_strios)
 
@@ -461,7 +463,7 @@ function (bc::BloxConnector)(
         sys_neuron = get_namespaced_sys(neuron)
         # Large negative current added to shut down the Striatum spiking neurons.
         # Value is hardcoded for now, as it's more of a hack, not user option. 
-        cb_neuron = t_event => [sys_neuron.I_bg ~ IfElse.ifelse(sys_matr_out.H*sys_matr_out.jcn > sys_matr_in.H*sys_matr_in.jcn, -2, 0)]
+        cb_neuron = t_event => [sys_neuron.I_bg ~ IfElse.ifelse(sys_matr_out.jcn > sys_matr_in.jcn, -2, 0)]
         push!(bc.events, cb_neuron)
     end
 end
@@ -507,12 +509,15 @@ end
 sample_poisson(λ) = rand(Poisson(λ))
 @register_symbolic sample_poisson(λ)
 
+
 """
     Non-symbolic, time-block-based way of `@register_symbolic sample_poisson(λ)`. 
 """
 function sample_affect!(integ, u, p, ctx)
     R = minimum([integ.p[p[1]]/(integ.p[p[2]] + eps()), integ.p[p[1]]])
     v = rand(Poisson(R))
+    @show R
+    @show v
     integ.p[p[3]] = v
 end
 
diff --git a/src/blox/discrete.jl b/src/blox/discrete.jl
index 5b4290c8..b3cb471d 100644
--- a/src/blox/discrete.jl
+++ b/src/blox/discrete.jl
@@ -71,7 +71,7 @@ struct SNc <: AbstractModulator
     end
 end
 
-(b::SNc)(R_DA, feedback) = b.N_time_blocks * b.κ_DA + R_DA - b.κ_DA + feedback * b.DA_reward
+(b::SNc)(R_DA) = R_DA #b.N_time_blocks * b.κ_DA + R_DA - b.κ_DA + feedback * b.DA_reward
 
 function get_modulator_state(s::SNc)
     sys = get_namespaced_sys(s)
diff --git a/src/blox/reinforcement_learning.jl b/src/blox/reinforcement_learning.jl
index 466866bf..bdd195f4 100644
--- a/src/blox/reinforcement_learning.jl
+++ b/src/blox/reinforcement_learning.jl
@@ -55,7 +55,7 @@ end
 dlogistic(x) = logistic(x) * (1 - logistic(x)) 
 
 function (hmp::HebbianModulationPlasticity)(val_pre, val_post, val_modulator, w, feedback)
-    DA = hmp.modulator(val_modulator, feedback)
+    DA = hmp.modulator(val_modulator)
     DA_baseline = hmp.modulator.κ_DA * hmp.modulator.N_time_blocks
     ϵ = feedback - (hmp.modulator.κ_DA - DA)
     

From 742a044420948472f03550d8b49888ae2d1bdafe Mon Sep 17 00:00:00 2001
From: anandpathak31 <anandpathak31@gmail.com>
Date: Mon, 15 Jan 2024 06:05:32 -0500
Subject: [PATCH 02/10] greedy policy dispatch

---
 Project.toml                       |   2 +
 notebook.jl                        | 481 +++++++++++++++++++++++++++++
 src/blox/connections.jl            |  33 +-
 src/blox/discrete.jl               |   4 +-
 src/blox/reinforcement_learning.jl |  24 +-
 5 files changed, 531 insertions(+), 13 deletions(-)
 create mode 100644 notebook.jl

diff --git a/Project.toml b/Project.toml
index 87166e71..d21709cd 100644
--- a/Project.toml
+++ b/Project.toml
@@ -40,6 +40,8 @@ OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Peaks = "18e31ff7-3703-566c-8e60-38913d67486b"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
diff --git a/notebook.jl b/notebook.jl
new file mode 100644
index 00000000..c4876e13
--- /dev/null
+++ b/notebook.jl
@@ -0,0 +1,481 @@
+### A Pluto.jl notebook ###
+# v0.19.36
+
+using Markdown
+using InteractiveUtils
+
+# ╔═╡ 7d500a22-c5ed-48d1-b009-4d4a0f03fc2a
+using Pkg
+
+# ╔═╡ 98f74502-74e6-11ee-34ff-7b16a35b2860
+Pkg.activate(".")
+
+# ╔═╡ 038809e1-7512-4f5d-8fbf-feef2cdce117
+Pkg.add("Plots")
+
+# ╔═╡ b4e8ba6e-e91a-474c-9314-8dd75fa2e82e
+Pkg.add("DifferentialEquations")
+
+# ╔═╡ b6a6209f-97f3-4844-928a-948f3d53b4d7
+using Statistics
+
+# ╔═╡ 1f184999-9dd8-4958-8158-157b683b6f5c
+using CSV
+
+# ╔═╡ 536b8983-8548-4144-be00-4d516fe7b75f
+using DataFrames
+
+# ╔═╡ e6a453f3-e2a8-44ec-9be2-1f6d9293c911
+using Neuroblox
+
+# ╔═╡ e8deef43-7ee9-484f-a8a8-2df6e7006e22
+using Plots
+
+# ╔═╡ ee0c04b9-57c9-44bc-b092-218019a545ec
+using DifferentialEquations
+
+# ╔═╡ d37ba3dd-78df-4e18-ac76-3864a2e9216e
+using MetaGraphs
+
+# ╔═╡ 8abc4a4b-4acf-4c25-a10c-33278329c9f4
+begin
+
+   #t_trial = 1600 # ms
+   time_block_dur = 90 # ms
+    N_trials = 500
+	
+	global_ns = :g 
+
+		fn = "examples/image_example.csv"
+	
+    @named LC = NextGenerationEIBlox(;namespace=global_ns, Cₑ=2*26,Cᵢ=1*26, Δₑ=0.5, Δᵢ=0.5, η_0ₑ=10.0, η_0ᵢ=0.0, v_synₑₑ=10.0, v_synₑᵢ=-10.0, v_synᵢₑ=10.0, v_synᵢᵢ=-10.0, alpha_invₑₑ=10.0/26, alpha_invₑᵢ=0.8/26, alpha_invᵢₑ=10.0/26, alpha_invᵢᵢ=0.8/26, kₑₑ=0.0*26, kₑᵢ=0.6*26, kᵢₑ=0.6*26, kᵢᵢ=0*26) #1
+
+	
+	@named ITN = NextGenerationEIBlox(;namespace=global_ns, Cₑ=2*36,Cᵢ=1*36, Δₑ=0.5, Δᵢ=0.5, η_0ₑ=10.0, η_0ᵢ=0.0, v_synₑₑ=10.0, v_synₑᵢ=-10.0, v_synᵢₑ=10.0, v_synᵢᵢ=-10.0, alpha_invₑₑ=10.0/36, alpha_invₑᵢ=0.8/36, alpha_invᵢₑ=10.0/36, alpha_invᵢᵢ=0.8/36, kₑₑ=0.0*36, kₑᵢ=0.6*36, kᵢₑ=0.6*36, kᵢᵢ=0*36) #2
+
+	@named VC = CorticalBlox(N_wta=45, N_exci=5,  density=0.01, weight=1,I_bg_ar=0;namespace=global_ns) #3
+	
+    @named PFC = CorticalBlox(N_wta=20, N_exci=5, density=0.01, weight=1,I_bg_ar=0;namespace=global_ns) #4
+
+	
+
+	@named STR1 = Striatum(N_inhib=25;namespace=global_ns) #5
+	@named STR2 = Striatum(N_inhib=25;namespace=global_ns) #6
+	@named tan_nrn = HHNeuronExciBlox(;namespace=global_ns) #7
+	@named gpi1 = GPi(N_inhib=25;namespace=global_ns) #8
+	@named gpi2 = GPi(N_inhib=25;namespace=global_ns) #9
+	@named gpe1 = GPe(N_inhib=25;namespace=global_ns) #10
+	@named gpe2 = GPe(N_inhib=25;namespace=global_ns) #11
+	@named STN1 = STN(N_exci=25,I_bg=3*ones(25);namespace=global_ns) #12
+    @named STN2 = STN(N_exci=25,I_bg=3*ones(25);namespace=global_ns) #13
+	@named Thal1 = Thalamus(N_exci=25;namespace=global_ns) #14
+	@named Thal2 = Thalamus(N_exci=25;namespace=global_ns) #15
+
+    fn = joinpath(@__DIR__, "examples/image_example.csv")
+    data = CSV.read(fn, DataFrame)
+	
+    @named stim = ImageStimulus(data[1:N_trials,:]; namespace=global_ns, t_stimulus=600, t_pause=1000) #16
+    @named tan_pop = TAN(;namespace=global_ns) #17
+
+	
+	@named AS = GreedyPolicy(namespace=global_ns, t_decision=180.5) #18
+    @named SNcb = SNc(namespace=global_ns) #19
+	
+	
+    assembly = [LC, ITN, VC, PFC, STR1, STR2, tan_nrn, gpi1, gpi2, gpe1, gpe2, STN1, STN2, Thal1, Thal2, stim, tan_pop, AS, SNcb]
+
+
+	hebbian_mod = HebbianModulationPlasticity(K=0.016, decay=0.01, α=2.5, θₘ=1, modulator=SNcb, t_pre=1600-eps(), t_post=1600-eps(), t_mod=180)
+	
+    hebbian_cort = HebbianPlasticity(K=5e-5, W_lim=5, t_pre=1600-eps(), t_post=1600-eps())
+	
+	hebbian_thal_cort = HebbianPlasticity(K=1e-5, W_lim=6, t_pre=1600-eps(), t_post=1600-eps())
+
+
+	# assembly = [PFC, STR1, STR2]
+	
+    g = MetaDiGraph()
+    add_blox!.(Ref(g), assembly)
+	
+	
+    add_edge!(g,1,3, Dict(:weight => 44)) #LC->VC
+	add_edge!(g,1,4, Dict(:weight => 44)) #LC->pfc
+	add_edge!(g,2,7, Dict(:weight => 100)) #ITN->tan
+	add_edge!(g,3,4, Dict(:weight => 2.5, :density => 0.08, :learning_rule => hebbian_cort)) #VC->pfc
+	add_edge!(g,4,5, Dict(:weight => 0.075, :density => 0.04, :learning_rule => hebbian_mod)) #pfc->str1
+	add_edge!(g,4,6, Dict(:weight => 0.075, :density => 0.04, :learning_rule => hebbian_mod)) #pfc->str2
+	add_edge!(g,7,5, Dict(:weight => 0.17)) #tan->str1
+	add_edge!(g,7,6, Dict(:weight => 0.17)) #tan->str2
+	add_edge!(g,5,8, Dict(:weight => 4, :density => 0.04)) #str1->gpi1
+	add_edge!(g,6,9, Dict(:weight => 4, :density => 0.04)) #str2->gpi2
+	add_edge!(g,8,14, Dict(:weight => 0.16, :density => 0.04)) #gpi1->thal1
+	add_edge!(g,9,15, Dict(:weight => 0.16, :density => 0.04)) #gpi2->thal2
+    add_edge!(g,14,4, Dict(:weight => 0.2, :density => 0.32, :learning_rule => hebbian_thal_cort, :sta => true)) #thal1->pfc
+	add_edge!(g,15,4, Dict(:weight => 0.2, :density => 0.32, :learning_rule => hebbian_thal_cort, :sta => true)) #thal2->pfc
+	add_edge!(g,5,10, Dict(:weight => 4, :density => 0.04)) #str1->gpe1
+	add_edge!(g,6,11, Dict(:weight => 4, :density => 0.04)) #str2->gpe2
+	add_edge!(g,10,8, Dict(:weight => 0.2, :density => 0.04)) #gpe1->gpi1
+	add_edge!(g,11,9, Dict(:weight => 0.2, :density => 0.04)) #gpe2->gpi2
+	add_edge!(g,10,12, Dict(:weight => 3.5, :density => 0.04)) #gpe1->stn1
+	add_edge!(g,11,13, Dict(:weight => 3.5, :density => 0.04)) #gpe2->stn2
+	add_edge!(g,12,8, Dict(:weight => 0.1, :density => 0.04)) #stn1->gpi1
+	add_edge!(g,13,9, Dict(:weight => 0.1, :density => 0.04)) #stn2->gpi2
+	add_edge!(g,16,3, :weight, 14) #stim->VC
+	add_edge!(g,17,5, Dict(:weight => 1, :t_event => 180.0)) #TAN pop -> str1
+	add_edge!(g,17,6, Dict(:weight => 1, :t_event => 180.0)) #TAN pop -> str2
+	add_edge!(g,5,17, Dict(:weight => 1)) #str1 -> TAN pop 
+	add_edge!(g,6,17, Dict(:weight => 1)) #str2 -> TAN pop 
+	add_edge!(g,5,6, Dict(:weight => 1, :t_event => 181.0)) #str1 -> str2
+	add_edge!(g,6,5, Dict(:weight => 1, :t_event => 181.0)) #str2 -> str1
+	add_edge!(g,5,18)# str1->AS
+	add_edge!(g,6,18)# str2->AS
+	add_edge!(g, 5, 19, Dict(:weight => 1)) # str1->Snc
+    add_edge!(g,6, 19, Dict(:weight => 1))  # str2->Snc
+	
+	
+end
+
+# ╔═╡ 48d15d01-7d94-4a2f-bf32-5f0c548b24f6
+size(data[1,1:225])
+
+# ╔═╡ 3650464e-90f5-4fd4-abf4-7f0a24e29d41
+stim
+
+# ╔═╡ 5ddd418f-23d2-4151-82c0-e8c9fae7e4f2
+  agent = Agent(g; name=:ag, t_block = 90);
+
+# ╔═╡ b14520ac-ac20-48e4-ac2a-91448fa398b8
+#neuron_net = system_from_graph(g; name=global_ns,t_affect=90.0);
+
+# ╔═╡ 77cbc8f9-5352-447f-b3bd-b81e0f343882
+#prob = ODEProblem(structural_simplify(neuron_net), [], (0.0, 2000), []);
+
+# ╔═╡ 7576c26d-8617-4c66-ab5e-db0bd8bb9e17
+    prob=agent.problem
+
+# ╔═╡ 58c3e086-8626-4e1d-9a95-7f192ab66666
+
+
+# ╔═╡ 3db27342-2b9c-4ce3-82d6-cd18e6870dd1
+getsys=agent.odesystem;
+
+# ╔═╡ d80b3bf7-24d8-4395-8903-de974e6445f9
+begin
+
+	st=states(getsys)
+	
+    vlist=Int64[]
+	
+	
+	for ii = 1:length(st)
+		if contains(string(st[ii]), "V(t)")
+			push!(vlist,ii)
+		end
+	end
+end
+
+# ╔═╡ 8049c9d0-328b-4275-8f31-207de4b8f729
+size(vlist)
+
+# ╔═╡ 96ca0f9a-fab8-4f9b-b4d2-4dd80dbd8973
+sn=(hebbian_mod.modulator)
+
+# ╔═╡ eb6e33a9-3704-46dc-8d51-76c0e6eab779
+inh1=collect(0:6:268) .+1
+
+# ╔═╡ 9c276a3f-256c-4cf7-b916-826370d9fffe
+inh2=collect(271:6:390) .+1
+
+# ╔═╡ d8445c07-5c91-4170-bd48-8ddbd8de144c
+exc1 = setdiff(collect(1:1:270),inh1)
+
+# ╔═╡ 7aab15ca-a27d-436f-b25e-b736d7ad2c3c
+exc2 = setdiff(collect(272:1:391),inh2)
+
+# ╔═╡ 49689d91-6efd-4192-b169-5c3b90267d32
+str1_ar=collect(392+1:1:392+25)
+
+# ╔═╡ 1e3641bf-497c-426e-b692-b917f4a10ee6
+str2_ar=collect(392+26:1:392+50)
+
+# ╔═╡ ad12f058-25bb-4b50-8f35-12544e099242
+begin
+	thal1_ar=collect(392+50+151:1:392+50+175)
+	thal2_ar=collect(392+50+176:1:392+50+200)
+end
+
+# ╔═╡ 71ae1e6a-9d52-4481-97e5-a0b911dac68d
+size(exc1)
+
+# ╔═╡ c122ffe5-9385-4bf6-83b5-aed97eb74e39
+
+
+# ╔═╡ 8a774634-557c-4482-9581-623cfd417565
+perf=zeros(N_trials)
+
+# ╔═╡ 391c420f-e006-4ad1-8787-6f62b306f33c
+act=zeros(N_trials)
+
+# ╔═╡ 709f2677-8627-4069-9827-618b906be7fa
+zero([0.9,-1])
+
+# ╔═╡ 78c45058-d2ee-4c1c-92f9-5d43e0647ecf
+eps()
+
+# ╔═╡ 0b036e57-120e-4d2f-902e-1352d573bb25
+perf
+
+# ╔═╡ a973a05c-e686-48c5-8242-688e6475624b
+sum(perf)
+
+# ╔═╡ 432effe7-77c2-4163-99f5-1ba95a3052e8
+begin
+
+	 env = ClassificationEnvironment(stim; name=:env, namespace=global_ns)
+    #run_experiment!(agent, env; alg=Vern7(), reltol=1e-9,abstol=1e-9)
+end
+
+# ╔═╡ c5bcb85c-0abb-4353-9202-02dcf23125f2
+ stim_params2 = Neuroblox.get_trial_stimulus(env)
+
+# ╔═╡ b2b22100-94f5-4d25-93e3-13f387c840f4
+   # prob2=remake(prob;tspan=(0,1600))
+	prob2 = remake(prob; p = merge(stim_params2),tspan=(0,1600))
+
+# ╔═╡ 0eff1351-d443-4d72-b100-6c591a7c5fe9
+    sol = solve(prob2,Vern7(),saveat=0.01)
+
+# ╔═╡ 10cfc03b-525e-4666-be1c-cc5074c40933
+    sol.retcode
+
+# ╔═╡ d039f7a0-e15f-4ada-bd0b-3ffac1dd9b96
+    ss=convert(Array,sol)
+
+# ╔═╡ 7ca9413b-f672-4177-8289-20b9ceaebc01
+aa=findall(x-> x<-59, ss[:,1])
+
+# ╔═╡ 49bd611f-e7f6-4a23-b2b2-13a3edc8bb32
+size(aa)
+
+# ╔═╡ 8f9abbff-9755-4da3-ba84-62e4ac7afaa0
+aa[end]
+
+# ╔═╡ 6b52f398-7af9-4ce8-bdc4-4943afa09587
+ss[:,1]
+
+# ╔═╡ 970dcbaa-2740-4690-933e-7db0d99ebffd
+v=ss[vlist[exc2[4]],:]
+
+# ╔═╡ cb8ea960-c0de-4796-8a5f-4f3f357ae774
+plot(sol.t,v,xlims=(0,1600))
+
+# ╔═╡ 1f853f06-48ca-4592-8f82-49bdc6e23c68
+begin
+	VV=zeros(length(vlist),length(sol.t))
+	V=zeros(length(vlist),length(sol.t))
+
+	for ii = 1:length(vlist)
+		VV[ii,:] .= ss[vlist[ii],:] .+ 200*(ii-1)
+		V[ii,:] .= ss[vlist[ii],:] 
+	end
+end
+
+# ╔═╡ 269c6778-45e5-4e9c-86d6-d01b39bb5d5b
+begin
+plot(sol.t,[VV[exc1[1:100],:]'],legend=false,yticks=[],color = "blue",size = (1000,700))
+plot!(sol.t,[VV[inh1[1:20],:]'],legend=false,yticks=[],color = "red")
+#plot!(sol.t,VV[[end],:]',legend=false,yticks=[],color = "green")	
+#plot!(soll2.t,[VV[inhib_mod_ar[1],:]],legend=false,yticks=[],color = "green")
+	
+end
+
+# ╔═╡ 3be065f1-b177-40d6-858b-b43805cf48d3
+begin
+plot(sol.t,[VV[exc2,:]'],legend=false,yticks=[],color = "blue",size = (1000,700))
+plot!(sol.t,[VV[inh2,:]'],legend=false,yticks=[],color = "red")
+#plot!(sol.t,VV[[end],:]',legend=false,yticks=[],color = "green")	
+#plot!(soll2.t,[VV[inhib_mod_ar[1],:]],legend=false,yticks=[],color = "green")
+	
+end
+
+# ╔═╡ 8f5bf952-01b1-45d8-9584-5ca31b004d65
+begin
+plot(sol.t,[VV[str1_ar,:]'],legend=false,yticks=[],color = "green",size = (1000,700));
+plot!(sol.t,[VV[str2_ar,:]'],legend=false,yticks=[],color = "blue",size = (1000,700))	
+end
+
+# ╔═╡ 327dfad8-ef55-4b43-927b-17448aeb7313
+begin
+plot(sol.t,[VV[thal1_ar,:]'],legend=false,yticks=[],color = "green",size = (1000,700));
+plot!(sol.t,[VV[thal2_ar,:]'],legend=false,yticks=[],color = "blue",size = (1000,700))	
+end
+
+# ╔═╡ fc3610bc-8f62-4f23-b740-527eb2a94e79
+plot(sol.t,ss[vlist[exc2[4]]+8,:])
+
+# ╔═╡ 7f6120ca-61bc-4161-b827-26560bc5f28c
+plot(sol.t,mean(V[exc1,:],dims=1)')
+
+# ╔═╡ fe451072-9911-41d6-b179-aad13f38dce0
+    action = agent.action_selection(agent.odesystem,prob2)
+
+# ╔═╡ 14e2806e-5cdd-4b0b-b8c7-8c6d2c7e349c
+begin 
+
+	#function run_experiment_test!(agent::Agent, env::ClassificationEnvironment, t_warmup=200.0; kwargs...)
+	t_warmup=800
+    #N_trials = env.N_trials
+    t_trial = env.t_trial
+    tspan = (0, t_trial)
+
+    sys = Neuroblox.get_sys(agent)
+    prob3 = remake(prob; p = merge(stim_params2),tspan=(0,1600))
+
+	if t_warmup > 0
+        prob3 = remake(prob3; tspan=(0,t_warmup))
+        #if haskey(kwargs, :alg)
+        #    sol = solve(prob, kwargs[:alg]; kwargs...)
+        #else
+		
+            sol2 = solve(prob3, Vern7())
+		
+        #end
+        u0 = sol2[1:end,end] # last value of state vector
+        prob3 = remake(prob3; p = merge(stim_params2),tspan=tspan, u0=u0)
+    else
+        prob3 = remake(prob3; p = merge(stim_params2),tspan=tspan)
+        u0 = []
+    end
+
+    action_selection = agent.action_selection
+    learning_rules = agent.learning_rules
+    
+    defs = ModelingToolkit.get_defaults(sys)
+    weights = Dict{Num, Float64}()
+    for w in keys(learning_rules)
+        weights[w] = defs[w]
+    end
+
+end
+
+
+# ╔═╡ dedebc43-46f3-47d1-aaeb-da74b7b5c588
+size(u0)
+
+# ╔═╡ 6aabdb11-ff58-4434-bdff-5c2d7e80ed68
+weights
+
+# ╔═╡ b7e84b20-0b80-478c-bc88-2883f80bcbb4
+begin
+learning=1
+	
+	if learning==1
+		
+    for ii = 1:N_trials
+        prob4 = agent.problem
+        stim_params = Neuroblox.get_trial_stimulus(env)
+        prob4 = remake(prob4; p = merge(weights, stim_params), u0 = u0,tspan=(0,1600))
+
+        #if haskey(kwargs, :alg)
+          #  sol = solve(prob, kwargs[:alg]; kwargs...)
+        #else
+            sol2 = solve(prob4, Vern7())
+        #end
+		    agent.problem = prob4
+            @info env.current_trial
+        #u0 = sol[1:end,end] # next run should continue where the last one ended
+
+        if isnothing(action_selection)
+            feedback = 1
+        else
+           # action = action_selection(sol2)
+			action = action_selection(agent)
+            feedback = env(action)
+			@info action
+	        @info feedback
+			perf[ii]=feedback
+			act[ii] = action
+        end
+     
+        for (w, rule) in learning_rules
+            w_val = weights[w]
+            Δw = Neuroblox.weight_gradient(rule, sol2, w_val, feedback)
+            weights[w] += Δw
+        end
+        Neuroblox.increment_trial!(env)
+		
+    end
+	
+
+    
+end
+	
+end
+
+# ╔═╡ 650ed1b7-1643-4e74-8a61-f1fbd70ba229
+env.current_trial
+
+# ╔═╡ Cell order:
+# ╠═7d500a22-c5ed-48d1-b009-4d4a0f03fc2a
+# ╠═98f74502-74e6-11ee-34ff-7b16a35b2860
+# ╠═b6a6209f-97f3-4844-928a-948f3d53b4d7
+# ╠═1f184999-9dd8-4958-8158-157b683b6f5c
+# ╠═536b8983-8548-4144-be00-4d516fe7b75f
+# ╠═e6a453f3-e2a8-44ec-9be2-1f6d9293c911
+# ╠═038809e1-7512-4f5d-8fbf-feef2cdce117
+# ╠═e8deef43-7ee9-484f-a8a8-2df6e7006e22
+# ╠═b4e8ba6e-e91a-474c-9314-8dd75fa2e82e
+# ╠═ee0c04b9-57c9-44bc-b092-218019a545ec
+# ╠═d37ba3dd-78df-4e18-ac76-3864a2e9216e
+# ╠═8abc4a4b-4acf-4c25-a10c-33278329c9f4
+# ╠═48d15d01-7d94-4a2f-bf32-5f0c548b24f6
+# ╠═3650464e-90f5-4fd4-abf4-7f0a24e29d41
+# ╠═5ddd418f-23d2-4151-82c0-e8c9fae7e4f2
+# ╠═b14520ac-ac20-48e4-ac2a-91448fa398b8
+# ╠═77cbc8f9-5352-447f-b3bd-b81e0f343882
+# ╠═7576c26d-8617-4c66-ab5e-db0bd8bb9e17
+# ╠═58c3e086-8626-4e1d-9a95-7f192ab66666
+# ╠═3db27342-2b9c-4ce3-82d6-cd18e6870dd1
+# ╠═d80b3bf7-24d8-4395-8903-de974e6445f9
+# ╠═8049c9d0-328b-4275-8f31-207de4b8f729
+# ╠═b2b22100-94f5-4d25-93e3-13f387c840f4
+# ╠═0eff1351-d443-4d72-b100-6c591a7c5fe9
+# ╠═fe451072-9911-41d6-b179-aad13f38dce0
+# ╠═96ca0f9a-fab8-4f9b-b4d2-4dd80dbd8973
+# ╠═10cfc03b-525e-4666-be1c-cc5074c40933
+# ╠═d039f7a0-e15f-4ada-bd0b-3ffac1dd9b96
+# ╠═7ca9413b-f672-4177-8289-20b9ceaebc01
+# ╠═49bd611f-e7f6-4a23-b2b2-13a3edc8bb32
+# ╠═6b52f398-7af9-4ce8-bdc4-4943afa09587
+# ╠═970dcbaa-2740-4690-933e-7db0d99ebffd
+# ╠═cb8ea960-c0de-4796-8a5f-4f3f357ae774
+# ╠═eb6e33a9-3704-46dc-8d51-76c0e6eab779
+# ╠═9c276a3f-256c-4cf7-b916-826370d9fffe
+# ╠═d8445c07-5c91-4170-bd48-8ddbd8de144c
+# ╠═7aab15ca-a27d-436f-b25e-b736d7ad2c3c
+# ╠═49689d91-6efd-4192-b169-5c3b90267d32
+# ╠═1e3641bf-497c-426e-b692-b917f4a10ee6
+# ╠═ad12f058-25bb-4b50-8f35-12544e099242
+# ╠═71ae1e6a-9d52-4481-97e5-a0b911dac68d
+# ╠═1f853f06-48ca-4592-8f82-49bdc6e23c68
+# ╠═269c6778-45e5-4e9c-86d6-d01b39bb5d5b
+# ╠═3be065f1-b177-40d6-858b-b43805cf48d3
+# ╠═8f5bf952-01b1-45d8-9584-5ca31b004d65
+# ╠═327dfad8-ef55-4b43-927b-17448aeb7313
+# ╠═c122ffe5-9385-4bf6-83b5-aed97eb74e39
+# ╠═fc3610bc-8f62-4f23-b740-527eb2a94e79
+# ╠═8f9abbff-9755-4da3-ba84-62e4ac7afaa0
+# ╠═7f6120ca-61bc-4161-b827-26560bc5f28c
+# ╠═dedebc43-46f3-47d1-aaeb-da74b7b5c588
+# ╠═14e2806e-5cdd-4b0b-b8c7-8c6d2c7e349c
+# ╠═8a774634-557c-4482-9581-623cfd417565
+# ╠═391c420f-e006-4ad1-8787-6f62b306f33c
+# ╠═c5bcb85c-0abb-4353-9202-02dcf23125f2
+# ╠═6aabdb11-ff58-4434-bdff-5c2d7e80ed68
+# ╠═709f2677-8627-4069-9827-618b906be7fa
+# ╠═78c45058-d2ee-4c1c-92f9-5d43e0647ecf
+# ╠═b7e84b20-0b80-478c-bc88-2883f80bcbb4
+# ╠═650ed1b7-1643-4e74-8a61-f1fbd70ba229
+# ╠═0b036e57-120e-4d2f-902e-1352d573bb25
+# ╠═a973a05c-e686-48c5-8242-688e6475624b
+# ╠═432effe7-77c2-4163-99f5-1ba95a3052e8
diff --git a/src/blox/connections.jl b/src/blox/connections.jl
index fc2065e8..aa57cea4 100644
--- a/src/blox/connections.jl
+++ b/src/blox/connections.jl
@@ -42,13 +42,13 @@ function get_callbacks(g, bc; t_block=missing)
         end
         if !isempty(eqs_params) && !isempty(eqs)
             cbs_spikes = (t_block + eps(float(t_block))) => eqs
-            cbs_params = t_block => eqs_params
+            cbs_params = (t_block - eps(float(t_block))) => eqs_params
             return vcat(cbs_params, cbs_spikes, bc.events)
         elseif isempty(eqs_params) && !isempty(eqs)
             cbs_spikes = (t_block + eps(float(t_block))) => eqs
             return vcat(cbs_spikes, bc.events)
         elseif !isempty(eqs_params) && isempty(eqs)
-            cbs_params = t_block => eqs_params
+            cbs_params = (t_block - eps(float(t_block))) => eqs_params
             return vcat(cbs_params, bc.events)
         else
             return bc.events
@@ -452,19 +452,27 @@ function (bc::BloxConnector)(
     neurons_in = get_inh_neurons(str_in)
 
     t_event = get_event_time(kwargs, nameof(str_out), nameof(str_in))
-    #it should actually be H*jcn (= ρ) but since H should always be reset to 1 at the beginning of each trial (which we are not currently) untill t_event, it makes sense to not keep H here
-    # for a more general version where H can change values anytime, we need to reset H to 1 at the begining
-    cb_matr = t_event => [sys_matr_in.H ~ IfElse.ifelse(sys_matr_out.jcn > sys_matr_in.jcn, 0, 1)]
-    cb_strios = t_event => [sys_strios_in.H ~ IfElse.ifelse(sys_matr_out.jcn > sys_matr_in.jcn, 0, 1)]
+    cb_matr = [t_event] => [sys_matr_in.H ~ IfElse.ifelse(sys_matr_out.H*sys_matr_out.jcn > sys_matr_in.H*sys_matr_in.jcn, 0, 1)]
+    cb_strios = [t_event] => [sys_strios_in.H ~ IfElse.ifelse(sys_matr_out.H*sys_matr_out.jcn > sys_matr_in.H*sys_matr_in.jcn, 0, 1)]
+    
+    #H should be reset to 1 at the beginning of each trial
+    cb_matr_init = [0.1] => [sys_matr_in.H ~ 1]
+    cb_strios_init = [0.1] => [sys_strios_in.H ~ 1]
+
     push!(bc.events, cb_matr)
     push!(bc.events, cb_strios)
+    push!(bc.events, cb_matr_init)
+    push!(bc.events, cb_strios_init)
 
     for neuron in neurons_in
         sys_neuron = get_namespaced_sys(neuron)
         # Large negative current added to shut down the Striatum spiking neurons.
         # Value is hardcoded for now, as it's more of a hack, not user option. 
-        cb_neuron = t_event => [sys_neuron.I_bg ~ IfElse.ifelse(sys_matr_out.jcn > sys_matr_in.jcn, -2, 0)]
+        cb_neuron = [t_event] => [sys_neuron.I_bg ~ IfElse.ifelse(sys_matr_out.H*sys_matr_out.jcn > sys_matr_in.H*sys_matr_in.jcn, -2, 0)]
+        # lateral inhibition current I_bg should be set to 0 at the beginning of each trial
+        cb_neuron_init = [0.1] => [sys_neuron.I_bg ~ 0]
         push!(bc.events, cb_neuron)
+        push!(bc.events, cb_neuron_init)
     end
 end
 
@@ -493,6 +501,7 @@ function (bc::BloxConnector)(
     end
 
     eq = sys_in.jcn ~ w*sys_out.H*sys_out.jcn
+  
 
     accumulate_equation!(bc, eq)
 end
@@ -515,10 +524,12 @@ sample_poisson(λ) = rand(Poisson(λ))
 """
 function sample_affect!(integ, u, p, ctx)
     R = minimum([integ.p[p[1]]/(integ.p[p[2]] + eps()), integ.p[p[1]]])
+   # R = integ.p[p[1]]
     v = rand(Poisson(R))
-    @show R
-    @show v
+    @info R
+    @info v
     integ.p[p[3]] = v
+    #integ.p[p[2]] = v
 end
 
 function (bc::BloxConnector)(
@@ -538,6 +549,7 @@ function (bc::BloxConnector)(
 
     t_event = get_event_time(kwargs, nameof(discr_out), nameof(discr_in))
     cb = t_event => (sample_affect!, [], [sys_out.κ, sys_out.jcn, sys_out.spikes_window], nothing)
+   # cb = t_event => (sample_affect!, [], [sys_out.R, sys_out.spikes_window], nothing)
     push!(bc.events, cb)
 
     eq = sys_in.jcn ~ w*sys_out.spikes_window
@@ -603,5 +615,6 @@ function connect_action_selection!(as::AbstractActionSelection, matr1::Matrisome
     sys1 = get_namespaced_sys(matr1)
     sys2 = get_namespaced_sys(matr2)
 
-    as.competitor_states = [sys1.ρ, sys2.ρ]
+   # as.competitor_states = [sys1.ρ, sys2.ρ]
+    as.competitor_params = [sys1.H, sys2.H]
 end
\ No newline at end of file
diff --git a/src/blox/discrete.jl b/src/blox/discrete.jl
index b3cb471d..4c1de5ad 100644
--- a/src/blox/discrete.jl
+++ b/src/blox/discrete.jl
@@ -44,7 +44,9 @@ struct TAN <: AbstractDiscrete
         @variables t 
         sts = @variables R(t)=κ 
         ps = @parameters κ=κ spikes_window=0.0 jcn=0.0 [input=true]
-        eqs = Equation[]
+        eqs = [
+                R ~ minimum([κ, κ/(λ*jcn + eps())])
+              ]
         sys = ODESystem(eqs, t, sts, ps; name)
 
         new(sys, namespace)
diff --git a/src/blox/reinforcement_learning.jl b/src/blox/reinforcement_learning.jl
index bdd195f4..51351b4c 100644
--- a/src/blox/reinforcement_learning.jl
+++ b/src/blox/reinforcement_learning.jl
@@ -136,16 +136,34 @@ mutable struct GreedyPolicy <: AbstractActionSelection
     const name
     const namespace
     competitor_states 
+    competitor_params
     const t_decision
 
-    function GreedyPolicy(; name, t_decision, namespace=nothing, competitor_states=nothing)
+    function GreedyPolicy(; name, t_decision, namespace=nothing, competitor_states=nothing, competitor_params=nothing)
         sts = isnothing(competitor_states) ? Num[] : competitor_states
-        new(name, namespace, sts, t_decision)
+        ps = isnothing(competitor_states) ? Num[] : competitor_params
+        new(name, namespace, sts, ps, t_decision)
     end
 end
 
 function (p::GreedyPolicy)(sol::SciMLBase.AbstractSciMLSolution)
     comp_vals = sol(p.t_decision; idxs=p.competitor_states)
+    @info comp_vals
+    return argmax(comp_vals)
+end
+
+function (p::GreedyPolicy)(sys::ODESystem, prob::ODEProblem)
+    ps = parameters(sys)
+    params = prob.p
+    map_idxs = Int.(ModelingToolkit.varmap_to_vars([ps[i] => i for i in eachindex(ps)], ps))
+    comp_params = p.competitor_params
+    idxs_cp = Int64[]
+    for i in eachindex(comp_params)
+        idxs = findall(x -> x==comp_params[i], ps)
+        push!(idxs_cp,idxs)
+    end
+    comp_vals = params[map_idxs[idxs_cp]]
+    @info comp_vals
     return argmax(comp_vals)
 end
  
@@ -175,6 +193,8 @@ mutable struct Agent
     end
 end
 
+
+
 function run_experiment!(agent::Agent, env::ClassificationEnvironment, t_warmup=200.0; kwargs...)
     N_trials = env.N_trials
     t_trial = env.t_trial

From cd31327c7f40960436982dd3d5c9d9e4ba3fba73 Mon Sep 17 00:00:00 2001
From: anandpathak31 <anandpathak31@gmail.com>
Date: Tue, 16 Jan 2024 00:11:27 -0500
Subject: [PATCH 03/10] adding SNc callback

---
 notebook.jl                        | 38 ++++++++++++++++++++----------
 src/blox/connections.jl            |  4 ++--
 src/blox/discrete.jl               | 17 ++++++++-----
 src/blox/reinforcement_learning.jl |  4 ++--
 4 files changed, 40 insertions(+), 23 deletions(-)

diff --git a/notebook.jl b/notebook.jl
index c4876e13..a96e4480 100644
--- a/notebook.jl
+++ b/notebook.jl
@@ -85,7 +85,7 @@ begin
     assembly = [LC, ITN, VC, PFC, STR1, STR2, tan_nrn, gpi1, gpi2, gpe1, gpe2, STN1, STN2, Thal1, Thal2, stim, tan_pop, AS, SNcb]
 
 
-	hebbian_mod = HebbianModulationPlasticity(K=0.016, decay=0.01, α=2.5, θₘ=1, modulator=SNcb, t_pre=1600-eps(), t_post=1600-eps(), t_mod=180)
+	hebbian_mod = HebbianModulationPlasticity(K=0.016, decay=0.01, α=2.5, θₘ=1, modulator=SNcb, t_pre=1600-eps(), t_post=1600-eps(), t_mod=90)
 	
     hebbian_cort = HebbianPlasticity(K=5e-5, W_lim=5, t_pre=1600-eps(), t_post=1600-eps())
 	
@@ -101,7 +101,7 @@ begin
     add_edge!(g,1,3, Dict(:weight => 44)) #LC->VC
 	add_edge!(g,1,4, Dict(:weight => 44)) #LC->pfc
 	add_edge!(g,2,7, Dict(:weight => 100)) #ITN->tan
-	add_edge!(g,3,4, Dict(:weight => 2.5, :density => 0.08, :learning_rule => hebbian_cort)) #VC->pfc
+	add_edge!(g,3,4, Dict(:weight => 1, :density => 0.08, :learning_rule => hebbian_cort)) #VC->pfc
 	add_edge!(g,4,5, Dict(:weight => 0.075, :density => 0.04, :learning_rule => hebbian_mod)) #pfc->str1
 	add_edge!(g,4,6, Dict(:weight => 0.075, :density => 0.04, :learning_rule => hebbian_mod)) #pfc->str2
 	add_edge!(g,7,5, Dict(:weight => 0.17)) #tan->str1
@@ -121,8 +121,8 @@ begin
 	add_edge!(g,12,8, Dict(:weight => 0.1, :density => 0.04)) #stn1->gpi1
 	add_edge!(g,13,9, Dict(:weight => 0.1, :density => 0.04)) #stn2->gpi2
 	add_edge!(g,16,3, :weight, 14) #stim->VC
-	add_edge!(g,17,5, Dict(:weight => 1, :t_event => 180.0)) #TAN pop -> str1
-	add_edge!(g,17,6, Dict(:weight => 1, :t_event => 180.0)) #TAN pop -> str2
+	add_edge!(g,17,5, Dict(:weight => 1, :t_event => 90.0)) #TAN pop -> str1
+	add_edge!(g,17,6, Dict(:weight => 1, :t_event => 90.0)) #TAN pop -> str2
 	add_edge!(g,5,17, Dict(:weight => 1)) #str1 -> TAN pop 
 	add_edge!(g,6,17, Dict(:weight => 1)) #str2 -> TAN pop 
 	add_edge!(g,5,6, Dict(:weight => 1, :t_event => 181.0)) #str1 -> str2
@@ -177,8 +177,11 @@ end
 # ╔═╡ 8049c9d0-328b-4275-8f31-207de4b8f729
 size(vlist)
 
-# ╔═╡ 96ca0f9a-fab8-4f9b-b4d2-4dd80dbd8973
-sn=(hebbian_mod.modulator)
+# ╔═╡ 19951361-eae0-4483-aca0-a371d6f005cc
+AS.competitor_states[1]
+
+# ╔═╡ 677f1a53-d6a2-4d15-9dba-514918254d20
+stt=Neuroblox.get_modulator_state(hebbian_mod.modulator)
 
 # ╔═╡ eb6e33a9-3704-46dc-8d51-76c0e6eab779
 inh1=collect(0:6:268) .+1
@@ -245,6 +248,15 @@ end
 # ╔═╡ 0eff1351-d443-4d72-b100-6c591a7c5fe9
     sol = solve(prob2,Vern7(),saveat=0.01)
 
+# ╔═╡ fe451072-9911-41d6-b179-aad13f38dce0
+    action = agent.action_selection(sol)
+
+# ╔═╡ ae499def-0886-4105-8a87-3f955badb6ff
+plot(sol(1:1000;idxs=AS.competitor_states[1]))
+
+# ╔═╡ a252da3e-8554-4120-9419-c90c07be5ad2
+sol(1:10;idxs=AS.competitor_states[2])
+
 # ╔═╡ 10cfc03b-525e-4666-be1c-cc5074c40933
     sol.retcode
 
@@ -316,9 +328,6 @@ plot(sol.t,ss[vlist[exc2[4]]+8,:])
 # ╔═╡ 7f6120ca-61bc-4161-b827-26560bc5f28c
 plot(sol.t,mean(V[exc1,:],dims=1)')
 
-# ╔═╡ fe451072-9911-41d6-b179-aad13f38dce0
-    action = agent.action_selection(agent.odesystem,prob2)
-
 # ╔═╡ 14e2806e-5cdd-4b0b-b8c7-8c6d2c7e349c
 begin 
 
@@ -367,7 +376,7 @@ weights
 
 # ╔═╡ b7e84b20-0b80-478c-bc88-2883f80bcbb4
 begin
-learning=1
+learning=0
 	
 	if learning==1
 		
@@ -388,8 +397,8 @@ learning=1
         if isnothing(action_selection)
             feedback = 1
         else
-           # action = action_selection(sol2)
-			action = action_selection(agent)
+            action = action_selection(sol2)
+			#action = action_selection(agent)
             feedback = env(action)
 			@info action
 	        @info feedback
@@ -441,7 +450,10 @@ env.current_trial
 # ╠═b2b22100-94f5-4d25-93e3-13f387c840f4
 # ╠═0eff1351-d443-4d72-b100-6c591a7c5fe9
 # ╠═fe451072-9911-41d6-b179-aad13f38dce0
-# ╠═96ca0f9a-fab8-4f9b-b4d2-4dd80dbd8973
+# ╠═19951361-eae0-4483-aca0-a371d6f005cc
+# ╠═677f1a53-d6a2-4d15-9dba-514918254d20
+# ╠═ae499def-0886-4105-8a87-3f955badb6ff
+# ╠═a252da3e-8554-4120-9419-c90c07be5ad2
 # ╠═10cfc03b-525e-4666-be1c-cc5074c40933
 # ╠═d039f7a0-e15f-4ada-bd0b-3ffac1dd9b96
 # ╠═7ca9413b-f672-4177-8289-20b9ceaebc01
diff --git a/src/blox/connections.jl b/src/blox/connections.jl
index aa57cea4..1432388a 100644
--- a/src/blox/connections.jl
+++ b/src/blox/connections.jl
@@ -615,6 +615,6 @@ function connect_action_selection!(as::AbstractActionSelection, matr1::Matrisome
     sys1 = get_namespaced_sys(matr1)
     sys2 = get_namespaced_sys(matr2)
 
-   # as.competitor_states = [sys1.ρ, sys2.ρ]
-    as.competitor_params = [sys1.H, sys2.H]
+    as.competitor_states = [sys1.ρ, sys2.ρ]
+    #as.competitor_params = [sys1.H, sys2.H]
 end
\ No newline at end of file
diff --git a/src/blox/discrete.jl b/src/blox/discrete.jl
index 4c1de5ad..59536775 100644
--- a/src/blox/discrete.jl
+++ b/src/blox/discrete.jl
@@ -58,18 +58,23 @@ struct SNc <: AbstractModulator
     N_time_blocks
     κ_DA
     DA_reward
+    t_event
     namespace
 
-    function SNc(; name, namespace=nothing, κ_DA=1, N_time_blocks=5, DA_reward=10, λ_DA=0.33)
+    function SNc(; name, namespace=nothing, κ_DA=1, N_time_blocks=5, DA_reward=10, λ_DA=0.33, t_event=90.0)
         @variables t 
-        sts = @variables R(t)=κ_DA 
-        ps = @parameters κ=κ_DA λ_DA=λ_DA jcn=0.0 [input=true]
+        sts = @variables R(t)=κ_DA R_(t)=κ_DA
+        ps = @parameters κ=κ_DA λ_DA=λ_DA jcn=0.0 [input=true] jcn_=0.0 t_event=t_event
         eqs = [
                 R ~ minimum([κ_DA, κ_DA/(λ_DA*jcn + eps())])
+                R_ ~ minimum([κ_DA, κ_DA/(λ_DA*jcn_ + eps())])
               ]
-        sys = ODESystem(eqs, t, sts, ps; name)
 
-        new(sys, N_time_blocks, κ_DA, DA_reward, namespace)
+        R_cb = [[t_event+1] => [jcn_ ~ jcn]]     
+
+        sys = ODESystem(eqs, t, sts, ps; name = name, discrete_events = R_cb)
+
+        new(sys, N_time_blocks, κ_DA, DA_reward, t_event, namespace)
     end
 end
 
@@ -77,5 +82,5 @@ end
 
 function get_modulator_state(s::SNc)
     sys = get_namespaced_sys(s)
-    return sys.R
+    return sys.R_
 end
\ No newline at end of file
diff --git a/src/blox/reinforcement_learning.jl b/src/blox/reinforcement_learning.jl
index 51351b4c..70d0fe39 100644
--- a/src/blox/reinforcement_learning.jl
+++ b/src/blox/reinforcement_learning.jl
@@ -151,7 +151,7 @@ function (p::GreedyPolicy)(sol::SciMLBase.AbstractSciMLSolution)
     @info comp_vals
     return argmax(comp_vals)
 end
-
+"""
 function (p::GreedyPolicy)(sys::ODESystem, prob::ODEProblem)
     ps = parameters(sys)
     params = prob.p
@@ -166,7 +166,7 @@ function (p::GreedyPolicy)(sys::ODESystem, prob::ODEProblem)
     @info comp_vals
     return argmax(comp_vals)
 end
- 
+"""
 mutable struct Agent 
     odesystem
     problem

From 0210e408183e78ffbaa03e8fc03294720b6e5958 Mon Sep 17 00:00:00 2001
From: Haris Orgn <organtzh@gmail.com>
Date: Tue, 16 Jan 2024 18:29:35 +0200
Subject: [PATCH 04/10] add function to get weight values between two bloxs

---
 src/Neuroblox.jl           |  1 +
 src/blox/blox_utilities.jl | 16 ++++++++++++++++
 2 files changed, 17 insertions(+)

diff --git a/src/Neuroblox.jl b/src/Neuroblox.jl
index 2bd4cd2c..e85cd03e 100644
--- a/src/Neuroblox.jl
+++ b/src/Neuroblox.jl
@@ -181,5 +181,6 @@ export create_adjacency_edges!
 export get_namespaced_sys, nameof
 export run_experiment!, run_trial!
 export addnontunableparams, get_hemodynamic_observers
+export get_weights
 
 end
\ No newline at end of file
diff --git a/src/blox/blox_utilities.jl b/src/blox/blox_utilities.jl
index d345a92c..02095996 100644
--- a/src/blox/blox_utilities.jl
+++ b/src/blox/blox_utilities.jl
@@ -235,6 +235,22 @@ function get_event_time(kwargs, name_blox1, name_blox2)
     end
 end
 
+function get_weights(agent::Agent, blox_out, blox_in)
+    ps = parameters(agent.odesystem)
+    pv = agent.problem.p
+    map_idxs = Int.(ModelingToolkit.varmap_to_vars([ps[i] => i for i in eachindex(ps)], ps))
+
+    name_out = String(namespaced_nameof(blox_out))
+    name_in = String(namespaced_nameof(blox_in))
+
+    idxs_weight = findall(ps) do p
+        n = String(Symbol(p))
+        occursin("w_", n) && occursin(name_out, n) && occursin(name_in, n)
+    end
+    
+    return pv[map_idxs[idxs_weight]]
+end
+
 function find_spikes(x::AbstractVector{T}; minprom=zero(T), maxprom=nothing, minheight=zero(T), maxheight=nothing) where {T}
     spikes, _ = argmaxima(x)
     peakproms!(spikes, x; minprom, maxheight)

From 871bc504752b574543ce894c6b8843670ffbc042 Mon Sep 17 00:00:00 2001
From: anandpathak31 <anandpathak31@gmail.com>
Date: Wed, 17 Jan 2024 12:48:56 -0500
Subject: [PATCH 05/10] lower cap for connection weights set to 0

---
 notebook.jl                        | 144 ++++++++++++++++++++---------
 src/blox/connections.jl            |   2 +-
 src/blox/discrete.jl               |   7 +-
 src/blox/reinforcement_learning.jl |   2 +-
 4 files changed, 105 insertions(+), 50 deletions(-)

diff --git a/notebook.jl b/notebook.jl
index a96e4480..afa9b276 100644
--- a/notebook.jl
+++ b/notebook.jl
@@ -101,7 +101,7 @@ begin
     add_edge!(g,1,3, Dict(:weight => 44)) #LC->VC
 	add_edge!(g,1,4, Dict(:weight => 44)) #LC->pfc
 	add_edge!(g,2,7, Dict(:weight => 100)) #ITN->tan
-	add_edge!(g,3,4, Dict(:weight => 1, :density => 0.08, :learning_rule => hebbian_cort)) #VC->pfc
+	add_edge!(g,3,4, Dict(:weight => 2, :density => 0.08, :learning_rule => hebbian_cort)) #VC->pfc
 	add_edge!(g,4,5, Dict(:weight => 0.075, :density => 0.04, :learning_rule => hebbian_mod)) #pfc->str1
 	add_edge!(g,4,6, Dict(:weight => 0.075, :density => 0.04, :learning_rule => hebbian_mod)) #pfc->str2
 	add_edge!(g,7,5, Dict(:weight => 0.17)) #tan->str1
@@ -135,6 +135,18 @@ begin
 	
 end
 
+# ╔═╡ fd33687b-1277-4689-acff-ef0f6c93b036
+
+
+# ╔═╡ 07e91c2c-3a1b-49d8-855b-9568e9697bb1
+S = transpose(Matrix(data[!, Not(:category)]))
+
+# ╔═╡ 7e997ed5-20d1-424f-827c-f0bb2432149a
+SS=S[:,1:1000]
+
+# ╔═╡ 83d4d150-a21c-45eb-8fd1-fb8668062738
+Gray.(SS[:,1:100])
+
 # ╔═╡ 48d15d01-7d94-4a2f-bf32-5f0c548b24f6
 size(data[1,1:225])
 
@@ -142,7 +154,7 @@ size(data[1,1:225])
 stim
 
 # ╔═╡ 5ddd418f-23d2-4151-82c0-e8c9fae7e4f2
-  agent = Agent(g; name=:ag, t_block = 90);
+  #agent = Agent(g; name=:ag, t_block = 90);
 
 # ╔═╡ b14520ac-ac20-48e4-ac2a-91448fa398b8
 #neuron_net = system_from_graph(g; name=global_ns,t_affect=90.0);
@@ -225,12 +237,40 @@ zero([0.9,-1])
 # ╔═╡ 78c45058-d2ee-4c1c-92f9-5d43e0647ecf
 eps()
 
+# ╔═╡ 5705a3d8-d5c2-4c52-82b9-91ad05a43733
+#eqs=equations(agent.odesystem);
+
+# ╔═╡ 07bb7cce-498e-4ed6-b3e3-8fd31eb913fb
+#bc=Neuroblox.connector_from_graph(g)
+
+# ╔═╡ f1d77711-8db9-4c1d-9d5d-2ab172ef5f99
+#eqs_params=Neuroblox.get_equations_with_parameter_lhs(bc)
+
+# ╔═╡ 29b37f0c-cd6d-467b-961b-f2ded9cc3831
+#eqs_params[1] 
+
 # ╔═╡ 0b036e57-120e-4d2f-902e-1352d573bb25
 perf
 
+# ╔═╡ 9703f279-a311-4aa2-85e4-75d8898b21b6
+Gray.(act/2)
+
 # ╔═╡ a973a05c-e686-48c5-8242-688e6475624b
+Gray.(perf)
+
+# ╔═╡ 974d9926-4543-46cf-9e2f-38826be8f3ee
 sum(perf)
 
+# ╔═╡ 35891714-fafc-4465-a2e5-bcd7aaf1a59c
+begin
+	perf_smth=zeros(length(perf)-19)
+	for ii=20:length(perf)
+		perf_smth[ii-19] = mean(perf[ii-19:ii])
+		
+	end
+	plot(perf_smth,ylims=(0,1))
+end
+
 # ╔═╡ 432effe7-77c2-4163-99f5-1ba95a3052e8
 begin
 
@@ -245,14 +285,54 @@ end
    # prob2=remake(prob;tspan=(0,1600))
 	prob2 = remake(prob; p = merge(stim_params2),tspan=(0,1600))
 
+# ╔═╡ 14e2806e-5cdd-4b0b-b8c7-8c6d2c7e349c
+begin 
+
+	#function run_experiment_test!(agent::Agent, env::ClassificationEnvironment, t_warmup=200.0; kwargs...)
+	t_warmup=800
+    #N_trials = env.N_trials
+    t_trial = env.t_trial
+    tspan = (0, t_trial)
+
+    sys = Neuroblox.get_sys(agent)
+    prob3 = remake(prob; p = merge(stim_params2),tspan=(0,1600))
+
+	if t_warmup > 0
+        prob3 = remake(prob3; tspan=(0,t_warmup))
+        #if haskey(kwargs, :alg)
+        #    sol = solve(prob, kwargs[:alg]; kwargs...)
+        #else
+		
+            sol2 = solve(prob3, Vern7())
+		
+        #end
+        u0 = sol2[1:end,end] # last value of state vector
+        prob3 = remake(prob3; p = merge(stim_params2),tspan=tspan, u0=u0)
+    else
+        prob3 = remake(prob3; p = merge(stim_params2),tspan=tspan)
+        u0 = []
+    end
+
+    action_selection = agent.action_selection
+    learning_rules = agent.learning_rules
+    
+    defs = ModelingToolkit.get_defaults(sys)
+    weights = Dict{Num, Float64}()
+    for w in keys(learning_rules)
+        weights[w] = defs[w]
+    end
+
+end
+
+
 # ╔═╡ 0eff1351-d443-4d72-b100-6c591a7c5fe9
-    sol = solve(prob2,Vern7(),saveat=0.01)
+    sol = solve(prob3,Vern7(),saveat=0.01)
 
 # ╔═╡ fe451072-9911-41d6-b179-aad13f38dce0
     action = agent.action_selection(sol)
 
 # ╔═╡ ae499def-0886-4105-8a87-3f955badb6ff
-plot(sol(1:1000;idxs=AS.competitor_states[1]))
+sol(0;idxs=stt)
 
 # ╔═╡ a252da3e-8554-4120-9419-c90c07be5ad2
 sol(1:10;idxs=AS.competitor_states[2])
@@ -328,46 +408,6 @@ plot(sol.t,ss[vlist[exc2[4]]+8,:])
 # ╔═╡ 7f6120ca-61bc-4161-b827-26560bc5f28c
 plot(sol.t,mean(V[exc1,:],dims=1)')
 
-# ╔═╡ 14e2806e-5cdd-4b0b-b8c7-8c6d2c7e349c
-begin 
-
-	#function run_experiment_test!(agent::Agent, env::ClassificationEnvironment, t_warmup=200.0; kwargs...)
-	t_warmup=800
-    #N_trials = env.N_trials
-    t_trial = env.t_trial
-    tspan = (0, t_trial)
-
-    sys = Neuroblox.get_sys(agent)
-    prob3 = remake(prob; p = merge(stim_params2),tspan=(0,1600))
-
-	if t_warmup > 0
-        prob3 = remake(prob3; tspan=(0,t_warmup))
-        #if haskey(kwargs, :alg)
-        #    sol = solve(prob, kwargs[:alg]; kwargs...)
-        #else
-		
-            sol2 = solve(prob3, Vern7())
-		
-        #end
-        u0 = sol2[1:end,end] # last value of state vector
-        prob3 = remake(prob3; p = merge(stim_params2),tspan=tspan, u0=u0)
-    else
-        prob3 = remake(prob3; p = merge(stim_params2),tspan=tspan)
-        u0 = []
-    end
-
-    action_selection = agent.action_selection
-    learning_rules = agent.learning_rules
-    
-    defs = ModelingToolkit.get_defaults(sys)
-    weights = Dict{Num, Float64}()
-    for w in keys(learning_rules)
-        weights[w] = defs[w]
-    end
-
-end
-
-
 # ╔═╡ dedebc43-46f3-47d1-aaeb-da74b7b5c588
 size(u0)
 
@@ -401,6 +441,7 @@ learning=0
 			#action = action_selection(agent)
             feedback = env(action)
 			@info action
+			@info env.category[env.current_trial]
 	        @info feedback
 			perf[ii]=feedback
 			act[ii] = action
@@ -424,6 +465,9 @@ end
 # ╔═╡ 650ed1b7-1643-4e74-8a61-f1fbd70ba229
 env.current_trial
 
+# ╔═╡ c75142d1-e9b9-46e4-afcf-bc6c4e0c7325
+env.category
+
 # ╔═╡ Cell order:
 # ╠═7d500a22-c5ed-48d1-b009-4d4a0f03fc2a
 # ╠═98f74502-74e6-11ee-34ff-7b16a35b2860
@@ -437,6 +481,10 @@ env.current_trial
 # ╠═ee0c04b9-57c9-44bc-b092-218019a545ec
 # ╠═d37ba3dd-78df-4e18-ac76-3864a2e9216e
 # ╠═8abc4a4b-4acf-4c25-a10c-33278329c9f4
+# ╠═fd33687b-1277-4689-acff-ef0f6c93b036
+# ╠═07e91c2c-3a1b-49d8-855b-9568e9697bb1
+# ╠═7e997ed5-20d1-424f-827c-f0bb2432149a
+# ╠═83d4d150-a21c-45eb-8fd1-fb8668062738
 # ╠═48d15d01-7d94-4a2f-bf32-5f0c548b24f6
 # ╠═3650464e-90f5-4fd4-abf4-7f0a24e29d41
 # ╠═5ddd418f-23d2-4151-82c0-e8c9fae7e4f2
@@ -487,7 +535,15 @@ env.current_trial
 # ╠═709f2677-8627-4069-9827-618b906be7fa
 # ╠═78c45058-d2ee-4c1c-92f9-5d43e0647ecf
 # ╠═b7e84b20-0b80-478c-bc88-2883f80bcbb4
+# ╠═5705a3d8-d5c2-4c52-82b9-91ad05a43733
+# ╠═07bb7cce-498e-4ed6-b3e3-8fd31eb913fb
+# ╠═f1d77711-8db9-4c1d-9d5d-2ab172ef5f99
+# ╠═29b37f0c-cd6d-467b-961b-f2ded9cc3831
 # ╠═650ed1b7-1643-4e74-8a61-f1fbd70ba229
 # ╠═0b036e57-120e-4d2f-902e-1352d573bb25
+# ╠═9703f279-a311-4aa2-85e4-75d8898b21b6
 # ╠═a973a05c-e686-48c5-8242-688e6475624b
+# ╠═c75142d1-e9b9-46e4-afcf-bc6c4e0c7325
+# ╠═974d9926-4543-46cf-9e2f-38826be8f3ee
+# ╠═35891714-fafc-4465-a2e5-bcd7aaf1a59c
 # ╠═432effe7-77c2-4163-99f5-1ba95a3052e8
diff --git a/src/blox/connections.jl b/src/blox/connections.jl
index 1432388a..42dc3922 100644
--- a/src/blox/connections.jl
+++ b/src/blox/connections.jl
@@ -548,7 +548,7 @@ function (bc::BloxConnector)(
     end
 
     t_event = get_event_time(kwargs, nameof(discr_out), nameof(discr_in))
-    cb = t_event => (sample_affect!, [], [sys_out.κ, sys_out.jcn, sys_out.spikes_window], nothing)
+    cb = [t_event+eps(t_event)] => (sample_affect!, [], [sys_out.κ, sys_out.jcn, sys_out.spikes_window], nothing)
    # cb = t_event => (sample_affect!, [], [sys_out.R, sys_out.spikes_window], nothing)
     push!(bc.events, cb)
 
diff --git a/src/blox/discrete.jl b/src/blox/discrete.jl
index 59536775..3438fb64 100644
--- a/src/blox/discrete.jl
+++ b/src/blox/discrete.jl
@@ -58,23 +58,22 @@ struct SNc <: AbstractModulator
     N_time_blocks
     κ_DA
     DA_reward
-    t_event
     namespace
 
     function SNc(; name, namespace=nothing, κ_DA=1, N_time_blocks=5, DA_reward=10, λ_DA=0.33, t_event=90.0)
         @variables t 
         sts = @variables R(t)=κ_DA R_(t)=κ_DA
-        ps = @parameters κ=κ_DA λ_DA=λ_DA jcn=0.0 [input=true] jcn_=0.0 t_event=t_event
+        ps = @parameters κ=κ_DA λ_DA=λ_DA jcn=0.0 [input=true] jcn_=0.0 
         eqs = [
                 R ~ minimum([κ_DA, κ_DA/(λ_DA*jcn + eps())])
                 R_ ~ minimum([κ_DA, κ_DA/(λ_DA*jcn_ + eps())])
               ]
 
-        R_cb = [[t_event+1] => [jcn_ ~ jcn]]     
+        R_cb = [[t_event+3*eps(t_event)] => [jcn_ ~ jcn]]     
 
         sys = ODESystem(eqs, t, sts, ps; name = name, discrete_events = R_cb)
 
-        new(sys, N_time_blocks, κ_DA, DA_reward, t_event, namespace)
+        new(sys, N_time_blocks, κ_DA, DA_reward, namespace)
     end
 end
 
diff --git a/src/blox/reinforcement_learning.jl b/src/blox/reinforcement_learning.jl
index 70d0fe39..e44de5fc 100644
--- a/src/blox/reinforcement_learning.jl
+++ b/src/blox/reinforcement_learning.jl
@@ -60,7 +60,7 @@ function (hmp::HebbianModulationPlasticity)(val_pre, val_post, val_modulator, w,
     ϵ = feedback - (hmp.modulator.κ_DA - DA)
     
    # Δw = hmp.K * val_post * val_pre * DA * (DA - DA_baseline) * dlogistic(DA) - hmp.decay * w
-    Δw = hmp.K * val_post * val_pre * ϵ * (ϵ + hmp.θₘ) * dlogistic(hmp.α * (ϵ + hmp.θₘ)) - hmp.decay * w
+    Δw = maximum([hmp.K * val_post * val_pre * ϵ * (ϵ + hmp.θₘ) * dlogistic(hmp.α * (ϵ + hmp.θₘ)) - hmp.decay * w, -w])
 
     return Δw
 end

From 7e9ebc2ea5fb7e46ee29a75be288b5d3bb6597f6 Mon Sep 17 00:00:00 2001
From: anandpathak31 <anandpathak31@gmail.com>
Date: Sun, 21 Jan 2024 22:52:22 -0500
Subject: [PATCH 06/10] recording matrisome activity at t event

---
 notebook.jl             | 114 ++++++++++++++++++++--------------------
 src/blox/connections.jl |   2 +-
 src/blox/discrete.jl    |  26 +++++----
 3 files changed, 75 insertions(+), 67 deletions(-)

diff --git a/notebook.jl b/notebook.jl
index afa9b276..9e3f3029 100644
--- a/notebook.jl
+++ b/notebook.jl
@@ -75,14 +75,14 @@ begin
     data = CSV.read(fn, DataFrame)
 	
     @named stim = ImageStimulus(data[1:N_trials,:]; namespace=global_ns, t_stimulus=600, t_pause=1000) #16
-    @named tan_pop = TAN(;namespace=global_ns) #17
-
+    @named tan_pop1 = TAN(;namespace=global_ns) #17
+    @named tan_pop2 = TAN(;namespace=global_ns) #18
 	
-	@named AS = GreedyPolicy(namespace=global_ns, t_decision=180.5) #18
-    @named SNcb = SNc(namespace=global_ns) #19
+	@named AS = GreedyPolicy(namespace=global_ns, t_decision=180.5) #19 
+    @named SNcb = SNc(namespace=global_ns) #20 
 	
 	
-    assembly = [LC, ITN, VC, PFC, STR1, STR2, tan_nrn, gpi1, gpi2, gpe1, gpe2, STN1, STN2, Thal1, Thal2, stim, tan_pop, AS, SNcb]
+    assembly = [LC, ITN, VC, PFC, STR1, STR2, tan_nrn, gpi1, gpi2, gpe1, gpe2, STN1, STN2, Thal1, Thal2, stim, tan_pop1, tan_pop2, AS, SNcb]
 
 
 	hebbian_mod = HebbianModulationPlasticity(K=0.016, decay=0.01, α=2.5, θₘ=1, modulator=SNcb, t_pre=1600-eps(), t_post=1600-eps(), t_mod=90)
@@ -121,16 +121,18 @@ begin
 	add_edge!(g,12,8, Dict(:weight => 0.1, :density => 0.04)) #stn1->gpi1
 	add_edge!(g,13,9, Dict(:weight => 0.1, :density => 0.04)) #stn2->gpi2
 	add_edge!(g,16,3, :weight, 14) #stim->VC
-	add_edge!(g,17,5, Dict(:weight => 1, :t_event => 90.0)) #TAN pop -> str1
-	add_edge!(g,17,6, Dict(:weight => 1, :t_event => 90.0)) #TAN pop -> str2
-	add_edge!(g,5,17, Dict(:weight => 1)) #str1 -> TAN pop 
-	add_edge!(g,6,17, Dict(:weight => 1)) #str2 -> TAN pop 
+	add_edge!(g,17,5, Dict(:weight => 1, :t_event => 90.0)) #TAN pop1 -> str1
+	add_edge!(g,18,6, Dict(:weight => 1, :t_event => 90.0)) #TAN pop2 -> str2
+	add_edge!(g,5,17, Dict(:weight => 1)) #str1 -> TAN pop1 
+	add_edge!(g,6,17, Dict(:weight => 1)) #str2 -> TAN pop1
+	add_edge!(g,5,18, Dict(:weight => 1)) #str1 -> TAN pop2 
+	add_edge!(g,6,18, Dict(:weight => 1)) #str2 -> TAN pop2
 	add_edge!(g,5,6, Dict(:weight => 1, :t_event => 181.0)) #str1 -> str2
 	add_edge!(g,6,5, Dict(:weight => 1, :t_event => 181.0)) #str2 -> str1
-	add_edge!(g,5,18)# str1->AS
-	add_edge!(g,6,18)# str2->AS
-	add_edge!(g, 5, 19, Dict(:weight => 1)) # str1->Snc
-    add_edge!(g,6, 19, Dict(:weight => 1))  # str2->Snc
+	add_edge!(g,5,19)# str1->AS
+	add_edge!(g,6,19)# str2->AS
+	add_edge!(g, 5, 20, Dict(:weight => 1)) # str1->Snc
+    add_edge!(g,6, 20, Dict(:weight => 1))  # str2->Snc
 	
 	
 end
@@ -154,7 +156,7 @@ size(data[1,1:225])
 stim
 
 # ╔═╡ 5ddd418f-23d2-4151-82c0-e8c9fae7e4f2
-  #agent = Agent(g; name=:ag, t_block = 90);
+  agent = Agent(g; name=:ag, t_block = 90);
 
 # ╔═╡ b14520ac-ac20-48e4-ac2a-91448fa398b8
 #neuron_net = system_from_graph(g; name=global_ns,t_affect=90.0);
@@ -285,48 +287,8 @@ end
    # prob2=remake(prob;tspan=(0,1600))
 	prob2 = remake(prob; p = merge(stim_params2),tspan=(0,1600))
 
-# ╔═╡ 14e2806e-5cdd-4b0b-b8c7-8c6d2c7e349c
-begin 
-
-	#function run_experiment_test!(agent::Agent, env::ClassificationEnvironment, t_warmup=200.0; kwargs...)
-	t_warmup=800
-    #N_trials = env.N_trials
-    t_trial = env.t_trial
-    tspan = (0, t_trial)
-
-    sys = Neuroblox.get_sys(agent)
-    prob3 = remake(prob; p = merge(stim_params2),tspan=(0,1600))
-
-	if t_warmup > 0
-        prob3 = remake(prob3; tspan=(0,t_warmup))
-        #if haskey(kwargs, :alg)
-        #    sol = solve(prob, kwargs[:alg]; kwargs...)
-        #else
-		
-            sol2 = solve(prob3, Vern7())
-		
-        #end
-        u0 = sol2[1:end,end] # last value of state vector
-        prob3 = remake(prob3; p = merge(stim_params2),tspan=tspan, u0=u0)
-    else
-        prob3 = remake(prob3; p = merge(stim_params2),tspan=tspan)
-        u0 = []
-    end
-
-    action_selection = agent.action_selection
-    learning_rules = agent.learning_rules
-    
-    defs = ModelingToolkit.get_defaults(sys)
-    weights = Dict{Num, Float64}()
-    for w in keys(learning_rules)
-        weights[w] = defs[w]
-    end
-
-end
-
-
 # ╔═╡ 0eff1351-d443-4d72-b100-6c591a7c5fe9
-    sol = solve(prob3,Vern7(),saveat=0.01)
+    sol = solve(prob2,Vern7(),saveat=0.01)
 
 # ╔═╡ fe451072-9911-41d6-b179-aad13f38dce0
     action = agent.action_selection(sol)
@@ -408,6 +370,46 @@ plot(sol.t,ss[vlist[exc2[4]]+8,:])
 # ╔═╡ 7f6120ca-61bc-4161-b827-26560bc5f28c
 plot(sol.t,mean(V[exc1,:],dims=1)')
 
+# ╔═╡ 14e2806e-5cdd-4b0b-b8c7-8c6d2c7e349c
+begin 
+
+	#function run_experiment_test!(agent::Agent, env::ClassificationEnvironment, t_warmup=200.0; kwargs...)
+	t_warmup=800
+    #N_trials = env.N_trials
+    t_trial = env.t_trial
+    tspan = (0, t_trial)
+
+    sys = Neuroblox.get_sys(agent)
+    prob3 = remake(prob; p = merge(stim_params2),tspan=(0,1600))
+
+	if t_warmup > 0
+        prob3 = remake(prob3; tspan=(0,t_warmup))
+        #if haskey(kwargs, :alg)
+        #    sol = solve(prob, kwargs[:alg]; kwargs...)
+        #else
+		
+            sol2 = solve(prob3, Vern7())
+		
+        #end
+        u0 = sol2[1:end,end] # last value of state vector
+        prob3 = remake(prob3; p = merge(stim_params2),tspan=tspan, u0=u0)
+    else
+        prob3 = remake(prob3; p = merge(stim_params2),tspan=tspan)
+        u0 = []
+    end
+
+    action_selection = agent.action_selection
+    learning_rules = agent.learning_rules
+    
+    defs = ModelingToolkit.get_defaults(sys)
+    weights = Dict{Num, Float64}()
+    for w in keys(learning_rules)
+        weights[w] = defs[w]
+    end
+
+end
+
+
 # ╔═╡ dedebc43-46f3-47d1-aaeb-da74b7b5c588
 size(u0)
 
@@ -420,7 +422,7 @@ learning=0
 	
 	if learning==1
 		
-    for ii = 1:N_trials
+    for ii = 326:N_trials
         prob4 = agent.problem
         stim_params = Neuroblox.get_trial_stimulus(env)
         prob4 = remake(prob4; p = merge(weights, stim_params), u0 = u0,tspan=(0,1600))
diff --git a/src/blox/connections.jl b/src/blox/connections.jl
index 42dc3922..1b36d4d9 100644
--- a/src/blox/connections.jl
+++ b/src/blox/connections.jl
@@ -615,6 +615,6 @@ function connect_action_selection!(as::AbstractActionSelection, matr1::Matrisome
     sys1 = get_namespaced_sys(matr1)
     sys2 = get_namespaced_sys(matr2)
 
-    as.competitor_states = [sys1.ρ, sys2.ρ]
+    as.competitor_states = [sys1.ρ_, sys2.ρ_] #HACK : accessing values of rho at a specific time after the simulation
     #as.competitor_params = [sys1.H, sys2.H]
 end
\ No newline at end of file
diff --git a/src/blox/discrete.jl b/src/blox/discrete.jl
index 3438fb64..a271d988 100644
--- a/src/blox/discrete.jl
+++ b/src/blox/discrete.jl
@@ -6,14 +6,19 @@ struct Matrisome <: AbstractDiscrete
     odesystem
     namespace
 
-    function Matrisome(; name, namespace=nothing)
+    function Matrisome(; name, namespace=nothing, t_event) #HACK : this t_event has to be informed from the t_event in Action Selection block
         @variables t 
-        sts = @variables ρ(t)=0.0 
-        ps = @parameters H=1 jcn=0.0 [input=true]
+        sts = @variables ρ(t)=0.0 ρ_(t)
+        ps = @parameters H=1 jcn=0.0 [input=true] jcn_=0.0 H_=1 #HACK : jcn_ and H_ store the value of jcn and H at time t_event that can be accessed after the simulation
         eqs = [
-            ρ ~ H*jcn
+            ρ ~ H*jcn,
+            ρ_ ~ H_*jcn_
         ]
-        sys = ODESystem(eqs, t, sts, ps; name)
+        cb_eqs = [ jcn_ ~ jcn,
+                    H_ ~ H
+                 ]
+        Rho_cb = [[t_event+3*eps(t_event)] => cb_eqs]   
+        sys = ODESystem(eqs, t, sts, ps; name = name, discrete_events = Rho_cb)
 
         new(sys, namespace)
     end
@@ -28,8 +33,9 @@ struct Striosome <: AbstractDiscrete
         sts = @variables ρ(t)=0.0 
         ps = @parameters H=1 jcn=0.0 [input=true]
         eqs = [
-            ρ ~ H*jcn
-        ]
+                ρ ~ H*jcn,
+              ]
+       
         sys = ODESystem(eqs, t, sts, ps; name)
 
         new(sys, namespace)
@@ -62,10 +68,10 @@ struct SNc <: AbstractModulator
 
     function SNc(; name, namespace=nothing, κ_DA=1, N_time_blocks=5, DA_reward=10, λ_DA=0.33, t_event=90.0)
         @variables t 
-        sts = @variables R(t)=κ_DA R_(t)=κ_DA
-        ps = @parameters κ=κ_DA λ_DA=λ_DA jcn=0.0 [input=true] jcn_=0.0 
+        sts = @variables R(t)=κ_DA R_(t)=κ_DA 
+        ps = @parameters κ=κ_DA λ_DA=λ_DA jcn=0.0 [input=true] jcn_=0.0 #HACK: jcn_ stores the value of jcn at time t_event that can be accessed after the simulation
         eqs = [
-                R ~ minimum([κ_DA, κ_DA/(λ_DA*jcn + eps())])
+                R ~ minimum([κ_DA, κ_DA/(λ_DA*jcn + eps())]),
                 R_ ~ minimum([κ_DA, κ_DA/(λ_DA*jcn_ + eps())])
               ]
 

From 9039b3da800a12abd50cb8a8dc39d5408bb4b5a0 Mon Sep 17 00:00:00 2001
From: anandpathak31 <anandpathak31@gmail.com>
Date: Mon, 22 Jan 2024 00:57:18 -0500
Subject: [PATCH 07/10] t_event for matrisome

---
 notebook.jl          | 4 ++--
 src/blox/discrete.jl | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/notebook.jl b/notebook.jl
index 9e3f3029..fefb7722 100644
--- a/notebook.jl
+++ b/notebook.jl
@@ -418,11 +418,11 @@ weights
 
 # ╔═╡ b7e84b20-0b80-478c-bc88-2883f80bcbb4
 begin
-learning=0
+learning=1
 	
 	if learning==1
 		
-    for ii = 326:N_trials
+    for ii = 18:N_trials
         prob4 = agent.problem
         stim_params = Neuroblox.get_trial_stimulus(env)
         prob4 = remake(prob4; p = merge(weights, stim_params), u0 = u0,tspan=(0,1600))
diff --git a/src/blox/discrete.jl b/src/blox/discrete.jl
index a271d988..66d941ef 100644
--- a/src/blox/discrete.jl
+++ b/src/blox/discrete.jl
@@ -6,7 +6,7 @@ struct Matrisome <: AbstractDiscrete
     odesystem
     namespace
 
-    function Matrisome(; name, namespace=nothing, t_event) #HACK : this t_event has to be informed from the t_event in Action Selection block
+    function Matrisome(; name, namespace=nothing, t_event=180.0) #HACK : this t_event has to be informed from the t_event in Action Selection block
         @variables t 
         sts = @variables ρ(t)=0.0 ρ_(t)
         ps = @parameters H=1 jcn=0.0 [input=true] jcn_=0.0 H_=1 #HACK : jcn_ and H_ store the value of jcn and H at time t_event that can be accessed after the simulation

From c7f491822b34fa31da102296c63d9ea4006a262e Mon Sep 17 00:00:00 2001
From: Haris Orgn <organtzh@gmail.com>
Date: Tue, 23 Jan 2024 19:00:00 +0200
Subject: [PATCH 08/10] `deepcopy` learnign rule to avoid overwriting states

---
 src/blox/connections.jl | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/src/blox/connections.jl b/src/blox/connections.jl
index 1b36d4d9..6a1ddad7 100644
--- a/src/blox/connections.jl
+++ b/src/blox/connections.jl
@@ -132,7 +132,7 @@ function (bc::BloxConnector)(
     push!(bc.weights, w)    
 
     if haskey(kwargs, :learning_rule)
-        lr = kwargs[:learning_rule]
+        lr = deepcopy(kwargs[:learning_rule])
         maybe_set_state_pre!(lr, sys_out.spikes_cumulative)
         maybe_set_state_post!(lr,sys_in.spikes_cumulative)
         bc.learning_rules[w] = lr
@@ -190,7 +190,7 @@ function (bc::BloxConnector)(
     push!(bc.weights, w)
 
     if haskey(kwargs, :learning_rule)
-        lr = kwargs[:learning_rule]
+        lr = deepcopy(kwargs[:learning_rule])
         bc.learning_rules[w] = lr
     end
 
@@ -373,7 +373,7 @@ function (bc::BloxConnector)(
     kwargs = (kwargs..., weight=wt_ar)
 
     if haskey(kwargs, :learning_rule)
-        lr = kwargs[:learning_rule]
+        lr = deepcopy(kwargs[:learning_rule])
         sys_matr = get_namespaced_sys(get_matrisome(str))
         maybe_set_state_post!(lr, sys_matr.H)
         kwargs = (kwargs..., learning_rule=lr)
@@ -431,7 +431,7 @@ function (bc::BloxConnector)(
     push!(bc.weights, w)
 
     if haskey(kwargs, :learning_rule)
-        lr = kwargs[:learning_rule]
+        lr = deepcopy(kwargs[:learning_rule])
         maybe_set_state_pre!(lr, sys_out.spikes_cumulative)
         maybe_set_state_post!(lr, sys_in.H)
         bc.learning_rules[w] = lr
@@ -497,7 +497,7 @@ function (bc::BloxConnector)(
     push!(bc.weights, w)
 
     if haskey(kwargs, :learning_rule)
-        bc.learning_rules[w] = kwargs[:learning_rule]
+        bc.learning_rules[w] = deepcopy(kwargs[:learning_rule])
     end
 
     eq = sys_in.jcn ~ w*sys_out.H*sys_out.jcn
@@ -526,8 +526,10 @@ function sample_affect!(integ, u, p, ctx)
     R = minimum([integ.p[p[1]]/(integ.p[p[2]] + eps()), integ.p[p[1]]])
    # R = integ.p[p[1]]
     v = rand(Poisson(R))
-    @info R
-    @info v
+    @show p
+    @show integ.p[p[1]]
+    @show integ.p[p[2]]
+    @show integ.p[p[3]]
     integ.p[p[3]] = v
     #integ.p[p[2]] = v
 end
@@ -544,7 +546,7 @@ function (bc::BloxConnector)(
     push!(bc.weights, w)
 
     if haskey(kwargs, :learning_rule)
-        bc.learning_rules[w] = kwargs[:learning_rule]
+        bc.learning_rules[w] = deepcopy(kwargs[:learning_rule])
     end
 
     t_event = get_event_time(kwargs, nameof(discr_out), nameof(discr_in))

From 1b158ab9f7c1d42c2b38a57a2b1b494e2f2e9e0c Mon Sep 17 00:00:00 2001
From: Haris Orgn <organtzh@gmail.com>
Date: Tue, 23 Jan 2024 19:06:11 +0200
Subject: [PATCH 09/10] make `get_weights` directional

---
 src/blox/blox_utilities.jl | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/blox/blox_utilities.jl b/src/blox/blox_utilities.jl
index 02095996..46079768 100644
--- a/src/blox/blox_utilities.jl
+++ b/src/blox/blox_utilities.jl
@@ -245,7 +245,8 @@ function get_weights(agent::Agent, blox_out, blox_in)
 
     idxs_weight = findall(ps) do p
         n = String(Symbol(p))
-        occursin("w_", n) && occursin(name_out, n) && occursin(name_in, n)
+        r = Regex("w.*$(name_out).*$(name_in)")
+        occursin(r, n)
     end
     
     return pv[map_idxs[idxs_weight]]

From 9ed70391016084b57ed82402af7b7703dc08dd46 Mon Sep 17 00:00:00 2001
From: anandpathak31 <anandpathak31@gmail.com>
Date: Thu, 25 Jan 2024 00:41:28 -0500
Subject: [PATCH 10/10] conflict resolve

---
 notebook.jl      | 95 +++++++++++++++++++++++++++++++++++++++++++-----
 src/Neuroblox.jl |  1 +
 2 files changed, 87 insertions(+), 9 deletions(-)

diff --git a/notebook.jl b/notebook.jl
index fefb7722..98e2db65 100644
--- a/notebook.jl
+++ b/notebook.jl
@@ -64,10 +64,10 @@ begin
 	@named tan_nrn = HHNeuronExciBlox(;namespace=global_ns) #7
 	@named gpi1 = GPi(N_inhib=25;namespace=global_ns) #8
 	@named gpi2 = GPi(N_inhib=25;namespace=global_ns) #9
-	@named gpe1 = GPe(N_inhib=25;namespace=global_ns) #10
-	@named gpe2 = GPe(N_inhib=25;namespace=global_ns) #11
-	@named STN1 = STN(N_exci=25,I_bg=3*ones(25);namespace=global_ns) #12
-    @named STN2 = STN(N_exci=25,I_bg=3*ones(25);namespace=global_ns) #13
+	@named gpe1 = GPe(N_inhib=15;namespace=global_ns) #10
+	@named gpe2 = GPe(N_inhib=15;namespace=global_ns) #11
+	@named STN1 = STN(N_exci=15,I_bg=3*ones(25);namespace=global_ns) #12
+    @named STN2 = STN(N_exci=15,I_bg=3*ones(25);namespace=global_ns) #13
 	@named Thal1 = Thalamus(N_exci=25;namespace=global_ns) #14
 	@named Thal2 = Thalamus(N_exci=25;namespace=global_ns) #15
 
@@ -137,6 +137,9 @@ begin
 	
 end
 
+# ╔═╡ d8b7f8ff-89d5-45bf-a357-ce0c0d181aef
+hebbian_mod.state_post
+
 # ╔═╡ fd33687b-1277-4689-acff-ef0f6c93b036
 
 
@@ -239,6 +242,49 @@ zero([0.9,-1])
 # ╔═╡ 78c45058-d2ee-4c1c-92f9-5d43e0647ecf
 eps()
 
+# ╔═╡ 7c165152-cb7f-4070-bfbe-2e6c50e224ca
+
+
+# ╔═╡ 98392a18-6499-43b6-9bd2-d23b26117c1d
+Gray.(perf)
+
+# ╔═╡ eca74b0a-e61b-45cb-b36c-40dcffc1da36
+begin
+	function get_CS_weights(agent,blox_out,blox_in)
+	#blox_out=PFC
+	#blox_in = STR1
+	ps = parameters(agent.odesystem)
+    pv = agent.problem.p
+    map_idxs = Int.(ModelingToolkit.varmap_to_vars([ps[i] => i for i in eachindex(ps)], ps))
+
+    name_out = String(Neuroblox.namespaced_nameof(blox_out))
+    name_in = String(Neuroblox.namespaced_nameof(blox_in))
+
+    idxs_weight = findall(ps) do p
+        n = String(Symbol(p))
+        occursin("w_", n) && occursin(name_out, n) && occursin(name_in, n) && occursin("matrisome",n)
+    end
+
+	w_ar = pv[map_idxs[idxs_weight]]
+	return w_ar
+	end
+end
+
+# ╔═╡ 610da6ed-4ede-473f-b5a9-676b1a19413f
+begin
+
+	w_ar1=get_CS_weights(agent,PFC,STR1)
+end
+
+# ╔═╡ d780fc51-1e27-412f-8072-f6fc29604781
+size(w_ar1)
+
+# ╔═╡ 36621957-4ef1-498d-99b7-cb9d8b036bfc
+mean(w_ar1)
+
+# ╔═╡ 1391eb15-57a7-430d-b818-7dcd8aaac4ae
+Gray.(w_ar1/maximum(w_ar1))
+
 # ╔═╡ 5705a3d8-d5c2-4c52-82b9-91ad05a43733
 #eqs=equations(agent.odesystem);
 
@@ -270,7 +316,7 @@ begin
 		perf_smth[ii-19] = mean(perf[ii-19:ii])
 		
 	end
-	plot(perf_smth,ylims=(0,1))
+	plot(collect(20:500),perf_smth[1:481],ylims=(0,1),xlims=(0,500))
 end
 
 # ╔═╡ 432effe7-77c2-4163-99f5-1ba95a3052e8
@@ -297,7 +343,7 @@ end
 sol(0;idxs=stt)
 
 # ╔═╡ a252da3e-8554-4120-9419-c90c07be5ad2
-sol(1:10;idxs=AS.competitor_states[2])
+sol(1:10;idxs=AS.competitor_states[1])
 
 # ╔═╡ 10cfc03b-525e-4666-be1c-cc5074c40933
     sol.retcode
@@ -365,14 +411,15 @@ plot!(sol.t,[VV[thal2_ar,:]'],legend=false,yticks=[],color = "blue",size = (1000
 end
 
 # ╔═╡ fc3610bc-8f62-4f23-b740-527eb2a94e79
-plot(sol.t,ss[vlist[exc2[4]]+8,:])
+plot(sol.t,ss[vlist[exc2[4]]+7,:])
 
 # ╔═╡ 7f6120ca-61bc-4161-b827-26560bc5f28c
 plot(sol.t,mean(V[exc1,:],dims=1)')
 
 # ╔═╡ 14e2806e-5cdd-4b0b-b8c7-8c6d2c7e349c
 begin 
-
+warm=0
+	if warm==1
 	#function run_experiment_test!(agent::Agent, env::ClassificationEnvironment, t_warmup=200.0; kwargs...)
 	t_warmup=800
     #N_trials = env.N_trials
@@ -407,6 +454,9 @@ begin
         weights[w] = defs[w]
     end
 
+	w_CS1=zeros(N_trials,100)
+    w_CS2=zeros(N_trials,100)
+	end
 end
 
 
@@ -416,13 +466,25 @@ size(u0)
 # ╔═╡ 6aabdb11-ff58-4434-bdff-5c2d7e80ed68
 weights
 
+# ╔═╡ 9077290a-e7e6-4822-bb25-dc9a833a2715
+Gray.(w_CS1[1:450,:]/maximum(w_CS1[1:60,:]))
+
+# ╔═╡ 4af5a936-88de-4c5f-a41c-6f14ca451cca
+Gray.(w_CS2[1:500,:]/maximum(w_CS2[1:60,:]))
+
+# ╔═╡ d6f941e6-ad36-4672-89bd-0423dc2fe667
+begin
+plot(mean(w_CS1,dims=2))
+plot!(mean(w_CS2,dims=2))	
+end
+
 # ╔═╡ b7e84b20-0b80-478c-bc88-2883f80bcbb4
 begin
 learning=1
 	
 	if learning==1
 		
-    for ii = 18:N_trials
+    for ii = 486:N_trials
         prob4 = agent.problem
         stim_params = Neuroblox.get_trial_stimulus(env)
         prob4 = remake(prob4; p = merge(weights, stim_params), u0 = u0,tspan=(0,1600))
@@ -433,6 +495,10 @@ learning=1
             sol2 = solve(prob4, Vern7())
         #end
 		    agent.problem = prob4
+
+		    w_CS1[ii,:]=get_CS_weights(agent,PFC,STR1)
+		    w_CS2[ii,:]=get_CS_weights(agent,PFC,STR2)
+		    
             @info env.current_trial
         #u0 = sol[1:end,end] # next run should continue where the last one ended
 
@@ -483,6 +549,7 @@ env.category
 # ╠═ee0c04b9-57c9-44bc-b092-218019a545ec
 # ╠═d37ba3dd-78df-4e18-ac76-3864a2e9216e
 # ╠═8abc4a4b-4acf-4c25-a10c-33278329c9f4
+# ╠═d8b7f8ff-89d5-45bf-a357-ce0c0d181aef
 # ╠═fd33687b-1277-4689-acff-ef0f6c93b036
 # ╠═07e91c2c-3a1b-49d8-855b-9568e9697bb1
 # ╠═7e997ed5-20d1-424f-827c-f0bb2432149a
@@ -537,6 +604,16 @@ env.category
 # ╠═709f2677-8627-4069-9827-618b906be7fa
 # ╠═78c45058-d2ee-4c1c-92f9-5d43e0647ecf
 # ╠═b7e84b20-0b80-478c-bc88-2883f80bcbb4
+# ╠═9077290a-e7e6-4822-bb25-dc9a833a2715
+# ╠═4af5a936-88de-4c5f-a41c-6f14ca451cca
+# ╠═d6f941e6-ad36-4672-89bd-0423dc2fe667
+# ╠═7c165152-cb7f-4070-bfbe-2e6c50e224ca
+# ╠═98392a18-6499-43b6-9bd2-d23b26117c1d
+# ╠═610da6ed-4ede-473f-b5a9-676b1a19413f
+# ╠═eca74b0a-e61b-45cb-b36c-40dcffc1da36
+# ╠═d780fc51-1e27-412f-8072-f6fc29604781
+# ╠═36621957-4ef1-498d-99b7-cb9d8b036bfc
+# ╠═1391eb15-57a7-430d-b818-7dcd8aaac4ae
 # ╠═5705a3d8-d5c2-4c52-82b9-91ad05a43733
 # ╠═07bb7cce-498e-4ed6-b3e3-8fd31eb913fb
 # ╠═f1d77711-8db9-4c1d-9d5d-2ab172ef5f99
diff --git a/src/Neuroblox.jl b/src/Neuroblox.jl
index e85cd03e..2927b9cd 100644
--- a/src/Neuroblox.jl
+++ b/src/Neuroblox.jl
@@ -182,5 +182,6 @@ export get_namespaced_sys, nameof
 export run_experiment!, run_trial!
 export addnontunableparams, get_hemodynamic_observers
 export get_weights
+export boldsignal_endo_balloon
 
 end
\ No newline at end of file