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Fabrizio Musacchio
Fabrizio Musacchio
@pixeltracker@sigmoid.social  ·  activity timestamp 2 weeks ago
Fig. 2. Analysis design. (A) Schematic of the trial-level phase synchrony extraction approach. (B) Schematic of dCPM. Edges that are significantly correlated with memory/arousal are selected. A linear model is then trained to predict memory/arousal on the left-out trial. This model is separately applied to all four study conditions (pill × emotionality) to yield four predictive networks. R, remembered.
Fig. 2. Analysis design. (A) Schematic of the trial-level phase synchrony extraction approach. (B) Schematic of dCPM. Edges that are significantly correlated with memory/arousal are selected. A linear model is then trained to predict memory/arousal on the left-out trial. This model is separately applied to all four study conditions (pill × emotionality) to yield four predictive networks. R, remembered.
Fig. 2. Analysis design. (A) Schematic of the trial-level phase synchrony extraction approach. (B) Schematic of dCPM. Edges that are significantly correlated with memory/arousal are selected. A linear model is then trained to predict memory/arousal on the left-out trial. This model is separately applied to all four study conditions (pill × emotionality) to yield four predictive networks. R, remembered.
Fabrizio Musacchio
Fabrizio Musacchio
@pixeltracker@sigmoid.social  ·  activity timestamp 2 months ago
Fig. 2. Schematic of the random-mode model. Upper: couplings J are generated as a sum of outer products, ℓ a r a T , with component strengths D a . Lower: the two-point function C ⋆ ϕ ( τ ) and four-point function Ψ ⋆ ϕ ( τ ) are calculated in terms of the statistics of D a . The two-point function depends only on the effective gain g eff , while the four-point function depends on both g eff and PR D , the effective dimension of the connectivity determined by the D a distribution.
Fig. 2. Schematic of the random-mode model. Upper: couplings J are generated as a sum of outer products, ℓ a r a T , with component strengths D a . Lower: the two-point function C ⋆ ϕ ( τ ) and four-point function Ψ ⋆ ϕ ( τ ) are calculated in terms of the statistics of D a . The two-point function depends only on the effective gain g eff , while the four-point function depends on both g eff and PR D , the effective dimension of the connectivity determined by the D a distribution.
Fig. 2. Schematic of the random-mode model. Upper: couplings J are generated as a sum of outer products, ℓ a r a T , with component strengths D a . Lower: the two-point function C ⋆ ϕ ( τ ) and four-point function Ψ ⋆ ϕ ( τ ) are calculated in terms of the statistics of D a . The two-point function depends only on the effective gain g eff , while the four-point function depends on both g eff and PR D , the effective dimension of the connectivity determined by the D a distribution.
jnpn
jnpn
@jnpn@mastodon.social  ·  activity timestamp 2 months ago
Björn Brembs
Björn Brembs
@brembs@mastodon.social  ·  activity timestamp 3 months ago