behavioral-neuroscience

Layer 3 — Biology24 concepts in this subtree

Behavioral neuroscience — NIH NIMH / NIDA / NIAAA mechanistic grain; study of the neural substrate of behavior in intact, behaving organisms, spanning classical and operant conditioning, instinctive and learned behavior, reward and…

Rescorla-Wagner: ΔV = α·β·(λ − V); trial-by-trial error-driven update
Matching law (Herrnstein 1961): R₁/R₂ = r₁/r₂ under concurrent VI schedules
Weber's law: ΔI/I = k (constant); Fechner's log-perception derivation
Rescorla-Wagner asymptote: ΔV(V=λ)=0; ΔV(V=0)=α·β·λ
Matching law identity: R_i = k·r_i ⟹ R₁/R₂ − r₁/r₂ = 0
Weber fraction: ΔI/I = k; at ΔI=3, I=100 → k=3/100
Temporal discounting: V_hyp = A/(1+k·t); V_exp = A·exp(−k·t); t_1/2 ratio = 1/ln 2
Generalization gradient: p_gauss(d) = exp(−d²/2σ²); p_shep(d) = exp(−d/σ); metric-space-dependent
Dual-process habituation: dH/dt=−aH+I; dS/dt=−bS+J; R=R₀−H+S; H_ss=I/a, S_ss=J/b
Discount: V_hyp=A/(1+kt); V_exp=A·e^{−kt}; t½_hyp/t½_exp=1/ln2; ∫V_exp dt=A/k (V_hyp diverges)
Gradient: p_gauss(σ)=e^{−1/2}; p_shep(σ)=e^{−1}; d½_gauss=σ√(2ln2); d½_shep=σ·ln2
Habituation: H(t)=(I/a)(1−e^{−at}); H_∞=I/a; t½_H=ln2/a; R_ss=R_0−I/a+J/b
Place cells (O Keefe 1971)
Optogenetics (Deisseroth 2005)
Dopamine RPE (Schultz 1997)
Addiction (Koob-Volkow 2010)
Fear (LeDoux 1996)
Circadian (Young-Rosbash-Hall Nobel 2017)
Classical conditioning (Pavlov 1903)
Operant (Skinner 1938)
Dopamine RPE (Schultz 1997)
Morris water maze (1981)
Equipotentiality (Lashley 1929)
Amygdala fear (LeDoux 1996)
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