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tune parameters #520

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tune parameters #520

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14ffe5d
tune parameters
anandpathak31 Jan 6, 2025
17d9fdc
Merge branch 'master' into 518-adjacency-plot-before-and-after-learni…
anandpathak31 Jan 6, 2025
c112fe5
Merge branch 'master' into 518-adjacency-plot-before-and-after-learni…
anandpathak31 Jan 8, 2025
b8b57b6
more parameter tuning
anandpathak31 Jan 10, 2025
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18 changes: 9 additions & 9 deletions docs/src/tutorials/hebbian_learning.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@ 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-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)

g = MetaDiGraph()

Expand All @@ -45,11 +45,11 @@ env = ClassificationEnvironment(stim, N_trials; name=:env, namespace=model_name)

fig = Figure(title="Adjacency matrix", size = (1600, 800))

adjacency(fig[1,1], agent)
adjacency(fig[1,1], agent, colorrange=(0,7)) # 7 is the maximum synaptic weight W_lim

run_experiment!(agent, env; t_warmup=200.0, alg=Vern7(), verbose=true)

adjacency(fig[1,2], agent)
adjacency(fig[1,2], agent, colorrange=(0,7))

fig

Expand Down Expand Up @@ -91,7 +91,7 @@ model_name=:g
@named AS = GreedyPolicy(; namespace=model_name, t_decision=2*time_block_dur)
@named SNcb = SNc(κ_DA=1; namespace=model_name)

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_mod = HebbianModulationPlasticity(K=0.03, 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
Expand All @@ -102,8 +102,8 @@ 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, 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)
Expand All @@ -122,7 +122,7 @@ env = ClassificationEnvironment(stim, N_trials; name=:env, namespace=model_name)

fig = Figure(title="Adjacency matrix", size = (1600, 800))

adjacency(fig[1,1], agent)
adjacency(fig[1,1], agent, colorrange=(0,0.2))

# run the whole experiment with N_trials number of trials
t=run_experiment!(agent, env; t_warmup=200.0, alg=Vern7(), verbose=true)
Expand All @@ -131,6 +131,6 @@ t=run_experiment!(agent, env; t_warmup=200.0, alg=Vern7(), verbose=true)
# trials: trial number
# correct: whether the response was correct or not
# action: what was the responce choice, choice 1 (left saccade) or choice 2 (right saccade)
adjacency(fig[1,2], agent)
adjacency(fig[1,2], agent, colorrange=(0,0.2))

fig
fig
4 changes: 2 additions & 2 deletions src/blox/reinforcement_learning.jl
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Expand Up @@ -264,7 +264,7 @@ function run_experiment!(agent::Agent, env::ClassificationEnvironment; verbose=f
push!(trace.action, action)

if verbose
println("Trial = $(trial), Category choice = $(action), Response = $(iscorrect==1 ? "Correct" : "False")")
println("Trial = $(trial), Category choice = $(action), Response = $(iscorrect==1 ? "Correct" : "Incorrect")")
end
end
return trace
Expand Down Expand Up @@ -327,7 +327,7 @@ function run_experiment!(agent::Agent, env::ClassificationEnvironment, save_path
push!(trace.action, action)

if verbose
println("Trial = $(trial), Category choice = $(action), Response = $(iscorrect==1 ? "Correct" : "False")")
println("Trial = $(trial), Category choice = $(action), Response = $(iscorrect==1 ? "Correct" : "Incorrect")")
end
end

Expand Down
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