From d06eeccb105b82ac484eb517b4225bf772dbc453 Mon Sep 17 00:00:00 2001 From: anandpathak31 Date: Thu, 31 Oct 2024 00:36:02 -0400 Subject: [PATCH 01/15] start learning tutorial --- docs/src/data/stimuli_set.csv | 1001 ++++++++++++++++++++++++ docs/src/tutorials/hebbian_learning.jl | 46 ++ 2 files changed, 1047 insertions(+) create mode 100644 docs/src/data/stimuli_set.csv create mode 100644 docs/src/tutorials/hebbian_learning.jl diff --git a/docs/src/data/stimuli_set.csv b/docs/src/data/stimuli_set.csv new file mode 100644 index 00000000..f954d80c --- /dev/null +++ b/docs/src/data/stimuli_set.csv @@ -0,0 +1,1001 @@ +pixel_1,pixel_2,pixel_3,pixel_4,pixel_5,pixel_6,pixel_7,pixel_8,pixel_9,pixel_10,pixel_11,pixel_12,pixel_13,pixel_14,pixel_15,pixel_16,pixel_17,pixel_18,pixel_19,pixel_20,category +0,0,0,0,0,0,0,0,0,0,0,0,0.6,1,0.6,0,0,0,0,0,2 +0,0,0,0.6,0,0.6,1,0.6,0,0,0,0,0,0,0,0,0,0,0,0,1 +0,0,0,0.6,0.6,1,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +0,0,0,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0,0,2 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gain access to multiple solvers from this +using Random ## for generating random variables +using CairoMakie ## for customized plotting recipies for blox +using CSV ## to read data from CSV files +using DataFrames ## to format the data into DataFrames +using Downloads ## to download image stimuli files + +time_block_dur = 90 ## ms (size of discrete time blocks) +N_trials = 10 ##number of trials +trial_dur = 1000 ##ms + +# create an image source block which takes image data from a .csv file and gives input to visual cortex + +image_set = CSV.read(Downloads.download("raw.githubusercontent.com/Neuroblox/NeurobloxDocsHost/refs/heads/main/data/image_example.csv"), DataFrame) ## reading data into DataFrame format +## change the source file to stimuli_set.csv + +global_namespace=:g +## define stimulus source blox +## t_stimulus: how long the stimulus is on (in msec) +## t_pause : how long th estimulus is off (in msec) +@named stim = ImageStimulus(image_set[[1:N_trials],:]; namespace=global_namespace, t_stimulus=trial_dur, t_pause=0); + +## cortical blox +@named VAC = CorticalBlox(N_wta=10, N_exci=5, density=0.05, weight=1,I_bg_ar=0;namespace=global_namespace) +@named AC = CorticalBlox(N_wta=2, N_exci=5, density=0.05, weight=1,I_bg_ar=0;namespace=global_namespace) +## ascending system blox, modulating frequency set to 16 Hz +@named ASC1 = NextGenerationEIBlox(;namespace=global_namespace, 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) + +## definelearning rule +hebbian_cort = HebbianPlasticity(K=5e-2, W_lim=7, t_pre=trial_dur-eps(), t_post=trial_dur-eps()) + + +g = MetaDiGraph() + +add_edge!(g, stim => VAC, weight=14) +add_edge!(g, ASC1 => VAC, weight=44) +add_edge!(g, ASC1 => AC, weight=44) +add_edge!(g, VAC => AC, weight=3, density=0.1, learning_rule = hebbian_cort) ## give learning rule as parameter + + +agent = Agent(g; name=:ag, t_block = time_block_dur); ## define agent +env = ClassificationEnvironment(stim; name=:env, namespace=global_ns) +run_experiment!(agent, env; alg=Vern7(), reltol=1e-9,abstol=1e-9) + + From 541d0a7f32cd749f04e805a7eb91b98bc1ccebdc Mon Sep 17 00:00:00 2001 From: anandpathak31 Date: Thu, 31 Oct 2024 16:50:22 -0400 Subject: [PATCH 02/15] reduce size --- docs/src/tutorials/hebbian_learning.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/src/tutorials/hebbian_learning.jl b/docs/src/tutorials/hebbian_learning.jl index 80e18cbb..1aa1c05b 100644 --- a/docs/src/tutorials/hebbian_learning.jl +++ b/docs/src/tutorials/hebbian_learning.jl @@ -22,7 +22,7 @@ global_namespace=:g @named stim = ImageStimulus(image_set[[1:N_trials],:]; namespace=global_namespace, t_stimulus=trial_dur, t_pause=0); ## cortical blox -@named VAC = CorticalBlox(N_wta=10, N_exci=5, density=0.05, weight=1,I_bg_ar=0;namespace=global_namespace) +@named VAC = CorticalBlox(N_wta=4, N_exci=5, density=0.05, weight=1,I_bg_ar=0;namespace=global_namespace) @named AC = CorticalBlox(N_wta=2, N_exci=5, density=0.05, weight=1,I_bg_ar=0;namespace=global_namespace) ## ascending system blox, modulating frequency set to 16 Hz @named ASC1 = NextGenerationEIBlox(;namespace=global_namespace, 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) From e2a5ebb00fffb2c4a119ffbf10e8f279cfdaa151 Mon Sep 17 00:00:00 2001 From: Haris Orgn Date: Fri, 1 Nov 2024 08:53:32 +0200 Subject: [PATCH 03/15] change data link --- docs/src/tutorials/hebbian_learning.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/src/tutorials/hebbian_learning.jl b/docs/src/tutorials/hebbian_learning.jl index 1aa1c05b..62c73f39 100644 --- a/docs/src/tutorials/hebbian_learning.jl +++ b/docs/src/tutorials/hebbian_learning.jl @@ -12,7 +12,7 @@ trial_dur = 1000 ##ms # create an image source block which takes image data from a .csv file and gives input to visual cortex -image_set = CSV.read(Downloads.download("raw.githubusercontent.com/Neuroblox/NeurobloxDocsHost/refs/heads/main/data/image_example.csv"), DataFrame) ## reading data into DataFrame format +image_set = CSV.read(Downloads.download("raw.githubusercontent.com/Neuroblox/NeurobloxDocsHost/refs/heads/main/data/stimuli_set.csv"), DataFrame) ## reading data into DataFrame format ## change the source file to stimuli_set.csv global_namespace=:g From 37380a1214b74b913ea07727356390e60f1fd3af Mon Sep 17 00:00:00 2001 From: Haris Orgn Date: Fri, 1 Nov 2024 08:54:21 +0200 Subject: [PATCH 04/15] remove data files (they are hosted in NeurobloxDocsHost) --- docs/src/data/image_example.csv | 1027 ------------------------------- docs/src/data/stimuli_set.csv | 1001 ------------------------------ docs/src/data/tract_lengths.csv | 76 --- docs/src/data/weights.csv | 77 --- 4 files changed, 2181 deletions(-) delete mode 100644 docs/src/data/image_example.csv delete mode 100644 docs/src/data/stimuli_set.csv delete mode 100644 docs/src/data/tract_lengths.csv delete mode 100644 docs/src/data/weights.csv diff --git a/docs/src/data/image_example.csv b/docs/src/data/image_example.csv deleted file mode 100644 index 0d90b0fc..00000000 --- a/docs/src/data/image_example.csv +++ /dev/null @@ -1,1027 +0,0 @@ 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From a491202d5817b76359fa893d422c1f61e03e85be Mon Sep 17 00:00:00 2001 From: anandpathak31 Date: Mon, 4 Nov 2024 12:23:20 -0500 Subject: [PATCH 05/15] cleanup --- docs/src/tutorials/hebbian_learning.jl | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/src/tutorials/hebbian_learning.jl b/docs/src/tutorials/hebbian_learning.jl index 62c73f39..8e2668c7 100644 --- a/docs/src/tutorials/hebbian_learning.jl +++ b/docs/src/tutorials/hebbian_learning.jl @@ -1,4 +1,4 @@ -using Neuroblox +using Neuroblox using OrdinaryDiffEq ## to build the ODE problem and solve it, gain access to multiple solvers from this using Random ## for generating random variables using CairoMakie ## for customized plotting recipies for blox @@ -19,7 +19,7 @@ global_namespace=:g ## define stimulus source blox ## t_stimulus: how long the stimulus is on (in msec) ## t_pause : how long th estimulus is off (in msec) -@named stim = ImageStimulus(image_set[[1:N_trials],:]; namespace=global_namespace, t_stimulus=trial_dur, t_pause=0); +@named stim = ImageStimulus(image_set[1:N_trials,:]; namespace=global_namespace, t_stimulus=trial_dur, t_pause=0); ## cortical blox @named VAC = CorticalBlox(N_wta=4, N_exci=5, density=0.05, weight=1,I_bg_ar=0;namespace=global_namespace) @@ -41,6 +41,6 @@ add_edge!(g, VAC => AC, weight=3, density=0.1, learning_rule = hebbian_cort) ## agent = Agent(g; name=:ag, t_block = time_block_dur); ## define agent env = ClassificationEnvironment(stim; name=:env, namespace=global_ns) -run_experiment!(agent, env; alg=Vern7(), reltol=1e-9,abstol=1e-9) +run_experiment!(agent, env; alg=Vern7()) From 720eecb8f9a2b164d6d6ae27b1ad36f2610128f7 Mon Sep 17 00:00:00 2001 From: anandpathak31 Date: Mon, 4 Nov 2024 23:35:39 -0500 Subject: [PATCH 06/15] cortico-striatal small circuit --- docs/src/tutorials/hebbian_learning.jl | 79 ++++++++++++++++++++++++-- 1 file changed, 75 insertions(+), 4 deletions(-) diff --git a/docs/src/tutorials/hebbian_learning.jl b/docs/src/tutorials/hebbian_learning.jl index 8e2668c7..1a4d270f 100644 --- a/docs/src/tutorials/hebbian_learning.jl +++ b/docs/src/tutorials/hebbian_learning.jl @@ -1,3 +1,6 @@ +# # Synaptic plasticity tutorial + + using Neuroblox using OrdinaryDiffEq ## to build the ODE problem and solve it, gain access to multiple solvers from this using Random ## for generating random variables @@ -6,8 +9,11 @@ using CSV ## to read data from CSV files using DataFrames ## to format the data into DataFrames using Downloads ## to download image stimuli files + +# ## Cortico-cortical plasticity + time_block_dur = 90 ## ms (size of discrete time blocks) -N_trials = 10 ##number of trials +N_trials = 5 ##number of trials trial_dur = 1000 ##ms # create an image source block which takes image data from a .csv file and gives input to visual cortex @@ -28,7 +34,7 @@ global_namespace=:g @named ASC1 = NextGenerationEIBlox(;namespace=global_namespace, 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) ## definelearning rule -hebbian_cort = HebbianPlasticity(K=5e-2, W_lim=7, t_pre=trial_dur-eps(), t_post=trial_dur-eps()) +hebbian_cort = HebbianPlasticity(K=5e-3, W_lim=7, t_pre=trial_dur-eps(), t_post=trial_dur-eps()) g = MetaDiGraph() @@ -40,7 +46,72 @@ add_edge!(g, VAC => AC, weight=3, density=0.1, learning_rule = hebbian_cort) ## agent = Agent(g; name=:ag, t_block = time_block_dur); ## define agent -env = ClassificationEnvironment(stim; name=:env, namespace=global_ns) -run_experiment!(agent, env; alg=Vern7()) +env = ClassificationEnvironment(stim; name=:env, namespace=global_namespace) +run_experiment!(agent, env)#; alg=Vern7()) + + +# ## cortico-striatal circuit performing category learning + + +time_block_dur = 90.0 ## ms (size of discrete time blocks) +N_trials = 10 ##number of trials +trial_dur = 1000 ##ms +# create an image source block which takes image data from a .csv file and gives input to visual cortex + +image_set = CSV.read(Downloads.download("raw.githubusercontent.com/Neuroblox/NeurobloxDocsHost/refs/heads/main/data/stimuli_set.csv"), DataFrame) ## reading data into DataFrame format +## change the source file to stimuli_set.csv +global_namespace=:g +## define stimulus source blox +## t_stimulus: how long the stimulus is on (in msec) +## t_pause : how long th estimulus is off (in msec) +@named stim = ImageStimulus(image_set[1:N_trials,:]; namespace=global_namespace, t_stimulus=trial_dur, t_pause=0); + +## cortical blox +@named VAC = CorticalBlox(N_wta=4, N_exci=5, density=0.05, weight=1,I_bg_ar=0;namespace=global_namespace) +@named AC = CorticalBlox(N_wta=2, N_exci=5, density=0.05, weight=1,I_bg_ar=0;namespace=global_namespace) +## ascending system blox, modulating frequency set to 16 Hz +@named ASC1 = NextGenerationEIBlox(;namespace=global_namespace, 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) + +## striatum blocks +@named STR1 = Striatum(N_inhib=5;namespace=global_namespace) +@named STR2 = Striatum(N_inhib=5;namespace=global_namespace) + +@named tan_pop1 = TAN(;namespace=global_namespace) +@named tan_pop2 = TAN(;namespace=global_namespace) + +@named AS = GreedyPolicy(namespace=global_namespace, t_decision=2*time_block_dur+eps()) +@named SNcb = SNc(namespace=global_namespace) + + +hebbian_mod = HebbianModulationPlasticity(K=0.04, decay=0.01, α=2.5, θₘ=1, modulator=SNcb, t_pre=trial_dur-eps(), t_post=trial_dur-eps(), t_mod=time_block_dur) +hebbian_cort = HebbianPlasticity(K=5e-3, W_lim=7, t_pre=trial_dur-eps(), t_post=trial_dur-eps()) + +## circuit + + + +g = MetaDiGraph() + +add_edge!(g, stim => VAC, weight=14) +add_edge!(g, ASC1 => VAC, weight=44) +add_edge!(g, ASC1 => AC, weight=44) +add_edge!(g, VAC => AC, weight=3, density=0.1, learning_rule = hebbian_cort) +add_edge!(g, tan_pop1 => STR1, weight = 1, t_event = time_block_dur) +add_edge!(g, tan_pop2 => STR2, weight = 1, t_event = time_block_dur) +add_edge!(g, STR1 => tan_pop1, weight = 1) +add_edge!(g, STR2 => tan_pop1, weight = 1) +add_edge!(g, STR1 => tan_pop2, weight = 1) +add_edge!(g, STR2 => tan_pop2, weight = 1) +add_edge!(g, STR1 => STR2, weight = 1, t_event = 2*(time_block_dur+eps())) +add_edge!(g, STR2 => STR1, weight = 1, t_event = 2*(time_block_dur+eps())) +add_edge!(g, STR1 => AS) +add_edge!(g, STR2 => AS) +add_edge!(g, STR1 => SNcb, weight = 1) +add_edge!(g, STR2 => SNcb, weight = 1) + + +agent = Agent(g; name=:ag, t_block = time_block_dur); ## define agent +env = ClassificationEnvironment(stim; name=:env, namespace=global_namespace) +run_experiment!(agent, env)#; \ No newline at end of file From d2abe33578078382cf83c6440e0fa41d97ecc5ce Mon Sep 17 00:00:00 2001 From: anandpathak31 Date: Mon, 4 Nov 2024 23:51:06 -0500 Subject: [PATCH 07/15] clean up --- docs/src/tutorials/hebbian_learning.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/src/tutorials/hebbian_learning.jl b/docs/src/tutorials/hebbian_learning.jl index 1a4d270f..14518236 100644 --- a/docs/src/tutorials/hebbian_learning.jl +++ b/docs/src/tutorials/hebbian_learning.jl @@ -12,7 +12,7 @@ using Downloads ## to download image stimuli files # ## Cortico-cortical plasticity -time_block_dur = 90 ## ms (size of discrete time blocks) +time_block_dur = 90.0 ## ms (size of discrete time blocks) N_trials = 5 ##number of trials trial_dur = 1000 ##ms @@ -55,7 +55,7 @@ run_experiment!(agent, env)#; alg=Vern7()) time_block_dur = 90.0 ## ms (size of discrete time blocks) N_trials = 10 ##number of trials -trial_dur = 1000 ##ms +trial_dur = 600 ##ms # create an image source block which takes image data from a .csv file and gives input to visual cortex From aae44d854f79e6987f01b060c6ae468faf5eed69 Mon Sep 17 00:00:00 2001 From: harisorgn Date: Wed, 13 Nov 2024 16:48:29 +0200 Subject: [PATCH 08/15] minor style changes --- docs/src/tutorials/hebbian_learning.jl | 62 +++++++++++--------------- 1 file changed, 26 insertions(+), 36 deletions(-) diff --git a/docs/src/tutorials/hebbian_learning.jl b/docs/src/tutorials/hebbian_learning.jl index 14518236..69682be6 100644 --- a/docs/src/tutorials/hebbian_learning.jl +++ b/docs/src/tutorials/hebbian_learning.jl @@ -1,5 +1,4 @@ -# # Synaptic plasticity tutorial - +# # Synaptic plasticity using Neuroblox using OrdinaryDiffEq ## to build the ODE problem and solve it, gain access to multiple solvers from this @@ -9,7 +8,6 @@ using CSV ## to read data from CSV files using DataFrames ## to format the data into DataFrames using Downloads ## to download image stimuli files - # ## Cortico-cortical plasticity time_block_dur = 90.0 ## ms (size of discrete time blocks) @@ -21,22 +19,21 @@ trial_dur = 1000 ##ms image_set = CSV.read(Downloads.download("raw.githubusercontent.com/Neuroblox/NeurobloxDocsHost/refs/heads/main/data/stimuli_set.csv"), DataFrame) ## reading data into DataFrame format ## change the source file to stimuli_set.csv -global_namespace=:g +model_name=:g ## define stimulus source blox ## t_stimulus: how long the stimulus is on (in msec) ## t_pause : how long th estimulus is off (in msec) -@named stim = ImageStimulus(image_set[1:N_trials,:]; namespace=global_namespace, t_stimulus=trial_dur, t_pause=0); +@named stim = ImageStimulus(image_set; namespace=global_namespace, t_stimulus=trial_dur, t_pause=0); ## cortical blox -@named VAC = CorticalBlox(N_wta=4, N_exci=5, density=0.05, weight=1,I_bg_ar=0;namespace=global_namespace) -@named AC = CorticalBlox(N_wta=2, N_exci=5, density=0.05, weight=1,I_bg_ar=0;namespace=global_namespace) +@named VAC = CorticalBlox(; namespace=model_name, N_wta=4, N_exci=5, density=0.05, weight=1) +@named AC = CorticalBlox(; namespace=model_name, N_wta=2, N_exci=5, density=0.05, weight=1) ## ascending system blox, modulating frequency set to 16 Hz -@named ASC1 = NextGenerationEIBlox(;namespace=global_namespace, 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) +@named ASC1 = NextGenerationEIBlox(; namespace=model_name, Cₑ=2*26,Cᵢ=1*26, alpha_invₑₑ=10.0/26, alpha_invₑᵢ=0.8/26, alpha_invᵢₑ=10.0/26, alpha_invᵢᵢ=0.8/26, kₑᵢ=0.6*26, kᵢₑ=0.6*26) -## definelearning rule +## define learning rule hebbian_cort = HebbianPlasticity(K=5e-3, W_lim=7, t_pre=trial_dur-eps(), t_post=trial_dur-eps()) - g = MetaDiGraph() add_edge!(g, stim => VAC, weight=14) @@ -44,14 +41,11 @@ add_edge!(g, ASC1 => VAC, weight=44) add_edge!(g, ASC1 => AC, weight=44) add_edge!(g, VAC => AC, weight=3, density=0.1, learning_rule = hebbian_cort) ## give learning rule as parameter - -agent = Agent(g; name=:ag, t_block = time_block_dur); ## define agent +agent = Agent(g; name=model_name, t_block = time_block_dur); ## define agent env = ClassificationEnvironment(stim; name=:env, namespace=global_namespace) -run_experiment!(agent, env)#; alg=Vern7()) - - -# ## cortico-striatal circuit performing category learning +run_experiment!(agent, env) +# ## Cortico-striatal circuit performing category learning time_block_dur = 90.0 ## ms (size of discrete time blocks) N_trials = 10 ##number of trials @@ -62,36 +56,33 @@ trial_dur = 600 ##ms image_set = CSV.read(Downloads.download("raw.githubusercontent.com/Neuroblox/NeurobloxDocsHost/refs/heads/main/data/stimuli_set.csv"), DataFrame) ## reading data into DataFrame format ## change the source file to stimuli_set.csv -global_namespace=:g +model_name=:g ## define stimulus source blox ## t_stimulus: how long the stimulus is on (in msec) ## t_pause : how long th estimulus is off (in msec) -@named stim = ImageStimulus(image_set[1:N_trials,:]; namespace=global_namespace, t_stimulus=trial_dur, t_pause=0); +@named stim = ImageStimulus(image_set; namespace=global_namespace, t_stimulus=trial_dur, t_pause=0); ## cortical blox -@named VAC = CorticalBlox(N_wta=4, N_exci=5, density=0.05, weight=1,I_bg_ar=0;namespace=global_namespace) -@named AC = CorticalBlox(N_wta=2, N_exci=5, density=0.05, weight=1,I_bg_ar=0;namespace=global_namespace) +@named VAC = CorticalBlox(; namespace=model_name, N_wta=4, N_exci=5, density=0.05, weight=1) +@named AC = CorticalBlox(; namespace=model_name, N_wta=2, N_exci=5, density=0.05, weight=1) ## ascending system blox, modulating frequency set to 16 Hz -@named ASC1 = NextGenerationEIBlox(;namespace=global_namespace, 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) +@named ASC1 = NextGenerationEIBlox(; namespace=model_name, Cₑ=2*26,Cᵢ=1*26, alpha_invₑₑ=10.0/26, alpha_invₑᵢ=0.8/26, alpha_invᵢₑ=10.0/26, alpha_invᵢᵢ=0.8/26, kₑᵢ=0.6*26, kᵢₑ=0.6*26) ## striatum blocks -@named STR1 = Striatum(N_inhib=5;namespace=global_namespace) -@named STR2 = Striatum(N_inhib=5;namespace=global_namespace) +@named STR1 = Striatum(; namespace=model_name, N_inhib=5) +@named STR2 = Striatum(; namespace=model_name, N_inhib=5) -@named tan_pop1 = TAN(;namespace=global_namespace) -@named tan_pop2 = TAN(;namespace=global_namespace) +@named tan_pop1 = TAN(; namespace=model_name) +@named tan_pop2 = TAN(; namespace=model_name) -@named AS = GreedyPolicy(namespace=global_namespace, t_decision=2*time_block_dur+eps()) -@named SNcb = SNc(namespace=global_namespace) +@named AS = GreedyPolicy(; namespace=model_name, t_decision=2*time_block_dur) +@named SNcb = SNc(; namespace=model_name) - -hebbian_mod = HebbianModulationPlasticity(K=0.04, decay=0.01, α=2.5, θₘ=1, modulator=SNcb, t_pre=trial_dur-eps(), t_post=trial_dur-eps(), t_mod=time_block_dur) -hebbian_cort = HebbianPlasticity(K=5e-3, W_lim=7, t_pre=trial_dur-eps(), t_post=trial_dur-eps()) +hebbian_mod = HebbianModulationPlasticity(K=0.04, decay=0.01, α=2.5, θₘ=1, modulator=SNcb, t_pre=trial_dur, t_post=trial_dur, t_mod=time_block_dur) +hebbian_cort = HebbianPlasticity(K=5e-3, W_lim=7, t_pre=trial_dur, t_post=trial_dur) ## circuit - - g = MetaDiGraph() add_edge!(g, stim => VAC, weight=14) @@ -104,14 +95,13 @@ add_edge!(g, STR1 => tan_pop1, weight = 1) add_edge!(g, STR2 => tan_pop1, weight = 1) add_edge!(g, STR1 => tan_pop2, weight = 1) add_edge!(g, STR2 => tan_pop2, weight = 1) -add_edge!(g, STR1 => STR2, weight = 1, t_event = 2*(time_block_dur+eps())) -add_edge!(g, STR2 => STR1, weight = 1, t_event = 2*(time_block_dur+eps())) +add_edge!(g, STR1 => STR2, weight = 1, t_event = 2*time_block_dur) +add_edge!(g, STR2 => STR1, weight = 1, t_event = 2*time_block_dur) add_edge!(g, STR1 => AS) add_edge!(g, STR2 => AS) add_edge!(g, STR1 => SNcb, weight = 1) add_edge!(g, STR2 => SNcb, weight = 1) - -agent = Agent(g; name=:ag, t_block = time_block_dur); ## define agent +agent = Agent(g; name=model_name, t_block = time_block_dur); ## define agent env = ClassificationEnvironment(stim; name=:env, namespace=global_namespace) run_experiment!(agent, env)#; \ No newline at end of file From 78e979a4508e6b4ebbed996f33b6f4aaa6b66dcf Mon Sep 17 00:00:00 2001 From: Haris Orgn Date: Mon, 18 Nov 2024 17:12:11 +0200 Subject: [PATCH 09/15] add number of trials argument to `ClassificationEnvironment` constructor --- src/blox/reinforcement_learning.jl | 27 +++++++++++++++++++++------ 1 file changed, 21 insertions(+), 6 deletions(-) diff --git a/src/blox/reinforcement_learning.jl b/src/blox/reinforcement_learning.jl index 5f2061b0..d8fd643d 100644 --- a/src/blox/reinforcement_learning.jl +++ b/src/blox/reinforcement_learning.jl @@ -102,7 +102,7 @@ mutable struct ClassificationEnvironment{S} <: AbstractEnvironment const N_trials::Int const t_trial::Float64 current_trial::Int - + function ClassificationEnvironment(data::DataFrame; name, namespace=nothing, t_stimulus, t_pause) stim = ImageStimulus( data; @@ -111,18 +111,33 @@ mutable struct ClassificationEnvironment{S} <: AbstractEnvironment t_stimulus, t_pause ) - - category = data[!, :category] + N_trials = stim.N_stimuli - t_trial = t_stimulus + t_pause - new{typeof(stim)}(Symbol(name), Symbol(namespace), stim, category, N_trials, t_trial, 1) + ClassificationEnvironment(stim, N_trials; name, namespace) end + function ClassificationEnvironment(data::DataFrame, N_trials; name, namespace=nothing, t_stimulus, t_pause) + stim = ImageStimulus( + data; + name=:stim, + namespace=namespaced_name(namespace, name), + t_stimulus, + t_pause + ) + + ClassificationEnvironment(stim, N_trials; name, namespace) + end + function ClassificationEnvironment(stim::ImageStimulus; name, namespace=nothing) - t_trial = stim.t_stimulus + stim.t_pause N_trials = stim.N_stimuli + ClassificationEnvironment(stim, N_trials; name, namespace) + end + + function ClassificationEnvironment(stim::ImageStimulus, N_trials; name, namespace=nothing) + t_trial = stim.t_stimulus + stim.t_pause + new{typeof(stim)}(Symbol(name), Symbol(namespace), stim, stim.category, N_trials, t_trial, 1) end end From 87ca2c34e8767aae25b568a223fb49299721a38e Mon Sep 17 00:00:00 2001 From: Haris Orgn Date: Mon, 18 Nov 2024 17:12:39 +0200 Subject: [PATCH 10/15] reduce learning rate to avoid weight oscillations (negative values) --- docs/src/tutorials/hebbian_learning.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/src/tutorials/hebbian_learning.jl b/docs/src/tutorials/hebbian_learning.jl index 69682be6..cc6b658d 100644 --- a/docs/src/tutorials/hebbian_learning.jl +++ b/docs/src/tutorials/hebbian_learning.jl @@ -32,7 +32,7 @@ model_name=:g @named ASC1 = NextGenerationEIBlox(; namespace=model_name, Cₑ=2*26,Cᵢ=1*26, alpha_invₑₑ=10.0/26, alpha_invₑᵢ=0.8/26, alpha_invᵢₑ=10.0/26, alpha_invᵢᵢ=0.8/26, kₑᵢ=0.6*26, kᵢₑ=0.6*26) ## define learning rule -hebbian_cort = HebbianPlasticity(K=5e-3, W_lim=7, t_pre=trial_dur-eps(), t_post=trial_dur-eps()) +hebbian_cort = HebbianPlasticity(K=5e-5, W_lim=7, t_pre=trial_dur, t_post=trial_dur) g = MetaDiGraph() From d8aedc75080297c71a78043993d86f4863ec27a2 Mon Sep 17 00:00:00 2001 From: Haris Orgn Date: Mon, 18 Nov 2024 17:12:50 +0200 Subject: [PATCH 11/15] update cortico-cortical plasticity eaxmple --- docs/src/tutorials/hebbian_learning.jl | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/docs/src/tutorials/hebbian_learning.jl b/docs/src/tutorials/hebbian_learning.jl index cc6b658d..7702ca6e 100644 --- a/docs/src/tutorials/hebbian_learning.jl +++ b/docs/src/tutorials/hebbian_learning.jl @@ -10,7 +10,6 @@ using Downloads ## to download image stimuli files # ## Cortico-cortical plasticity -time_block_dur = 90.0 ## ms (size of discrete time blocks) N_trials = 5 ##number of trials trial_dur = 1000 ##ms @@ -18,12 +17,12 @@ trial_dur = 1000 ##ms image_set = CSV.read(Downloads.download("raw.githubusercontent.com/Neuroblox/NeurobloxDocsHost/refs/heads/main/data/stimuli_set.csv"), DataFrame) ## reading data into DataFrame format ## change the source file to stimuli_set.csv - + model_name=:g ## define stimulus source blox ## t_stimulus: how long the stimulus is on (in msec) ## t_pause : how long th estimulus is off (in msec) -@named stim = ImageStimulus(image_set; namespace=global_namespace, t_stimulus=trial_dur, t_pause=0); +@named stim = ImageStimulus(image_set; namespace=model_name, t_stimulus=trial_dur, t_pause=0); ## cortical blox @named VAC = CorticalBlox(; namespace=model_name, N_wta=4, N_exci=5, density=0.05, weight=1) @@ -41,9 +40,14 @@ add_edge!(g, ASC1 => VAC, weight=44) add_edge!(g, ASC1 => AC, weight=44) add_edge!(g, VAC => AC, weight=3, density=0.1, learning_rule = hebbian_cort) ## give learning rule as parameter -agent = Agent(g; name=model_name, t_block = time_block_dur); ## define agent -env = ClassificationEnvironment(stim; name=:env, namespace=global_namespace) -run_experiment!(agent, env) +agent = Agent(g; name=model_name); ## define agent +env = ClassificationEnvironment(stim, N_trials; name=:env, namespace=model_name) + +adjacency(agent) + +run_experiment!(agent, env; t_warmup=200.0, alg=Vern7()) + +adjacency(agent) # ## Cortico-striatal circuit performing category learning From cf39c12dd8388a07638159e6e0d106c63f099be4 Mon Sep 17 00:00:00 2001 From: Haris Orgn Date: Mon, 25 Nov 2024 17:28:08 +0200 Subject: [PATCH 12/15] add before & after adjacency plots --- docs/src/tutorials/hebbian_learning.jl | 25 +++++++++++++++++++------ 1 file changed, 19 insertions(+), 6 deletions(-) diff --git a/docs/src/tutorials/hebbian_learning.jl b/docs/src/tutorials/hebbian_learning.jl index 7702ca6e..e41d9096 100644 --- a/docs/src/tutorials/hebbian_learning.jl +++ b/docs/src/tutorials/hebbian_learning.jl @@ -43,11 +43,15 @@ add_edge!(g, VAC => AC, weight=3, density=0.1, learning_rule = hebbian_cort) ## agent = Agent(g; name=model_name); ## define agent env = ClassificationEnvironment(stim, N_trials; name=:env, namespace=model_name) -adjacency(agent) +fig = Figure(title="Adjacency matrix", size = (1600, 800)) + +adjacency(fig[1,1], agent) run_experiment!(agent, env; t_warmup=200.0, alg=Vern7()) -adjacency(agent) +adjacency(fig[1,2], agent) + +fig # ## Cortico-striatal circuit performing category learning @@ -64,7 +68,7 @@ model_name=:g ## define stimulus source blox ## t_stimulus: how long the stimulus is on (in msec) ## t_pause : how long th estimulus is off (in msec) -@named stim = ImageStimulus(image_set; namespace=global_namespace, t_stimulus=trial_dur, t_pause=0); +@named stim = ImageStimulus(image_set; namespace=model_name, t_stimulus=trial_dur, t_pause=0); ## cortical blox @named VAC = CorticalBlox(; namespace=model_name, N_wta=4, N_exci=5, density=0.05, weight=1) @@ -83,7 +87,7 @@ model_name=:g @named SNcb = SNc(; namespace=model_name) hebbian_mod = HebbianModulationPlasticity(K=0.04, decay=0.01, α=2.5, θₘ=1, modulator=SNcb, t_pre=trial_dur, t_post=trial_dur, t_mod=time_block_dur) -hebbian_cort = HebbianPlasticity(K=5e-3, W_lim=7, t_pre=trial_dur, t_post=trial_dur) +hebbian_cort = HebbianPlasticity(K=5e-5, W_lim=7, t_pre=trial_dur, t_post=trial_dur) ## circuit @@ -107,5 +111,14 @@ add_edge!(g, STR1 => SNcb, weight = 1) add_edge!(g, STR2 => SNcb, weight = 1) agent = Agent(g; name=model_name, t_block = time_block_dur); ## define agent -env = ClassificationEnvironment(stim; name=:env, namespace=global_namespace) -run_experiment!(agent, env)#; \ No newline at end of file +env = ClassificationEnvironment(stim, N_trials; name=:env, namespace=model_name) + +fig = Figure(title="Adjacency matrix", size = (1600, 800)) + +adjacency(fig[1,1], agent) + +run_experiment!(agent, env; t_warmup=200.0, alg=Vern7()) + +adjacency(fig[1,2], agent) + +fig \ No newline at end of file From 12f82c9f409684639f3abf16bff9d6e18834d1a2 Mon Sep 17 00:00:00 2001 From: Haris Orgn Date: Tue, 26 Nov 2024 11:36:51 +0200 Subject: [PATCH 13/15] add trace to return trial ID and agent accuracy in `run_experiment!` --- src/blox/reinforcement_learning.jl | 22 ++++++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/src/blox/reinforcement_learning.jl b/src/blox/reinforcement_learning.jl index d8fd643d..f1bcf218 100644 --- a/src/blox/reinforcement_learning.jl +++ b/src/blox/reinforcement_learning.jl @@ -260,9 +260,16 @@ function run_experiment!(agent::Agent, env::ClassificationEnvironment; t_warmup= weights[w] = defs[w] end - for _ in Base.OneTo(N_trials) - run_trial!(agent, env, weights, nothing; saveat = t_stops, kwargs...) + trace = NamedTuple{(:trial, :correct)}((Int[], Bool[])) + + for t in Base.OneTo(N_trials) + _, iscorrect = run_trial!(agent, env, weights, nothing; saveat = t_stops, kwargs...) + + push!(trace.trial, t) + push!(trace.correct, iscorrect) end + + return trace end function run_experiment!(agent::Agent, env::ClassificationEnvironment, save_path::String; t_warmup=0, kwargs...) @@ -310,11 +317,18 @@ function run_experiment!(agent::Agent, env::ClassificationEnvironment, save_path save_idxs = union(idxs_V, idxs_learning) =# + trace = NamedTuple{(:trial, :correct)}((Int[], Bool[])) + for trial in Base.OneTo(N_trials) - sol = run_trial!(agent, env, weights, nothing; kwargs...) + sol, iscorrect = run_trial!(agent, env, weights, nothing; kwargs...) save_voltages(sol, save_path, trial) + + push!(trace.trial, trial) + push!(trace.correct, iscorrect) end + + return trace end function run_warmup(agent::Agent, env::ClassificationEnvironment, t_warmup; kwargs...) @@ -368,7 +382,7 @@ function run_trial!(agent::Agent, env::ClassificationEnvironment, weights, u0; k agent.problem = remake(prob; p = new_params) - return sol + return sol, feedback end function save_voltages(sol, filepath, numtrial) From b0eafd68df215d2b75c68f9edd6710c753b64093 Mon Sep 17 00:00:00 2001 From: Haris Orgn Date: Tue, 26 Nov 2024 11:37:11 +0200 Subject: [PATCH 14/15] update tutorial --- docs/src/tutorials/hebbian_learning.jl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/src/tutorials/hebbian_learning.jl b/docs/src/tutorials/hebbian_learning.jl index e41d9096..b748401b 100644 --- a/docs/src/tutorials/hebbian_learning.jl +++ b/docs/src/tutorials/hebbian_learning.jl @@ -56,8 +56,8 @@ fig # ## Cortico-striatal circuit performing category learning time_block_dur = 90.0 ## ms (size of discrete time blocks) -N_trials = 10 ##number of trials -trial_dur = 600 ##ms +N_trials = 100 ##number of trials +trial_dur = 1000 ##ms # create an image source block which takes image data from a .csv file and gives input to visual cortex @@ -87,7 +87,7 @@ model_name=:g @named SNcb = SNc(; namespace=model_name) hebbian_mod = HebbianModulationPlasticity(K=0.04, decay=0.01, α=2.5, θₘ=1, modulator=SNcb, t_pre=trial_dur, t_post=trial_dur, t_mod=time_block_dur) -hebbian_cort = HebbianPlasticity(K=5e-5, W_lim=7, t_pre=trial_dur, t_post=trial_dur) +hebbian_cort = HebbianPlasticity(K=5e-4, W_lim=7, t_pre=trial_dur, t_post=trial_dur) ## circuit @@ -117,7 +117,7 @@ fig = Figure(title="Adjacency matrix", size = (1600, 800)) adjacency(fig[1,1], agent) -run_experiment!(agent, env; t_warmup=200.0, alg=Vern7()) +t=run_experiment!(agent, env; t_warmup=200.0, alg=Vern7()) adjacency(fig[1,2], agent) From 9b24511aebfb902b2a5ecbf980fa58ad6b8b5bea Mon Sep 17 00:00:00 2001 From: anandpathak31 Date: Tue, 24 Dec 2024 05:25:02 -0500 Subject: [PATCH 15/15] tune parameters for learning tutorial --- docs/src/tutorials/hebbian_learning.jl | 10 ++++++---- src/blox/reinforcement_learning.jl | 19 +++++++++++-------- 2 files changed, 17 insertions(+), 12 deletions(-) diff --git a/docs/src/tutorials/hebbian_learning.jl b/docs/src/tutorials/hebbian_learning.jl index b748401b..2ba7145f 100644 --- a/docs/src/tutorials/hebbian_learning.jl +++ b/docs/src/tutorials/hebbian_learning.jl @@ -80,13 +80,13 @@ model_name=:g @named STR1 = Striatum(; namespace=model_name, N_inhib=5) @named STR2 = Striatum(; namespace=model_name, N_inhib=5) -@named tan_pop1 = TAN(; namespace=model_name) -@named tan_pop2 = TAN(; namespace=model_name) +@named tan_pop1 = TAN(κ=10; namespace=model_name) +@named tan_pop2 = TAN(κ=10; namespace=model_name) @named AS = GreedyPolicy(; namespace=model_name, t_decision=2*time_block_dur) -@named SNcb = SNc(; namespace=model_name) +@named SNcb = SNc(κ_DA=1; namespace=model_name) -hebbian_mod = HebbianModulationPlasticity(K=0.04, decay=0.01, α=2.5, θₘ=1, modulator=SNcb, t_pre=trial_dur, t_post=trial_dur, t_mod=time_block_dur) +hebbian_mod = HebbianModulationPlasticity(K=0.05, decay=0.01, α=2.5, θₘ=1, modulator=SNcb, t_pre=trial_dur, t_post=trial_dur, t_mod=time_block_dur) hebbian_cort = HebbianPlasticity(K=5e-4, W_lim=7, t_pre=trial_dur, t_post=trial_dur) ## circuit @@ -97,6 +97,8 @@ add_edge!(g, stim => VAC, weight=14) add_edge!(g, ASC1 => VAC, weight=44) add_edge!(g, ASC1 => AC, weight=44) add_edge!(g, VAC => AC, weight=3, density=0.1, learning_rule = hebbian_cort) +add_edge!(g, AC=>STR1, weight = 0.075, density = 0.04, learning_rule = hebbian_mod) +add_edge!(g, AC=>STR2, weight = 0.075, density = 0.04, learning_rule = hebbian_mod) add_edge!(g, tan_pop1 => STR1, weight = 1, t_event = time_block_dur) add_edge!(g, tan_pop2 => STR2, weight = 1, t_event = time_block_dur) add_edge!(g, STR1 => tan_pop1, weight = 1) diff --git a/src/blox/reinforcement_learning.jl b/src/blox/reinforcement_learning.jl index f1bcf218..5156aef9 100644 --- a/src/blox/reinforcement_learning.jl +++ b/src/blox/reinforcement_learning.jl @@ -260,13 +260,14 @@ function run_experiment!(agent::Agent, env::ClassificationEnvironment; t_warmup= weights[w] = defs[w] end - trace = NamedTuple{(:trial, :correct)}((Int[], Bool[])) + trace = NamedTuple{(:trial, :correct, :action)}((Int[], Bool[], Int[])) - for t in Base.OneTo(N_trials) - _, iscorrect = run_trial!(agent, env, weights, nothing; saveat = t_stops, kwargs...) - - push!(trace.trial, t) + for trial in Base.OneTo(N_trials) + _, iscorrect, action= run_trial!(agent, env, weights, nothing; kwargs...) + + push!(trace.trial, trial) push!(trace.correct, iscorrect) + push!(trace.action, action) end return trace @@ -317,15 +318,16 @@ function run_experiment!(agent::Agent, env::ClassificationEnvironment, save_path save_idxs = union(idxs_V, idxs_learning) =# - trace = NamedTuple{(:trial, :correct)}((Int[], Bool[])) + trace = NamedTuple{(:trial, :correct, :action)}((Int[], Bool[], Int[])) for trial in Base.OneTo(N_trials) - sol, iscorrect = run_trial!(agent, env, weights, nothing; kwargs...) + sol, iscorrect, action= run_trial!(agent, env, weights, nothing; kwargs...) save_voltages(sol, save_path, trial) push!(trace.trial, trial) push!(trace.correct, iscorrect) + push!(trace.action, action) end return trace @@ -364,6 +366,7 @@ function run_trial!(agent::Agent, env::ClassificationEnvironment, weights, u0; k if isnothing(action_selection) feedback = 1 + action = 0 else action = action_selection(sol) feedback = env(action) @@ -382,7 +385,7 @@ function run_trial!(agent::Agent, env::ClassificationEnvironment, weights, u0; k agent.problem = remake(prob; p = new_params) - return sol, feedback + return sol, feedback, action end function save_voltages(sol, filepath, numtrial)