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文件:An information integration theory of consciousness.png (查看源代码)
2026年3月22日 (日) 22:01的版本
、 2026年3月22日 (星期日)Effective information, minimum information bipartition, and complexes. a. Effective information. Shown is a single subset S of 4 elements ({1,2,3,4}, blue circle), forming part of a larger system X (black ellipse). This subset is bisected into A and B by a bipartition ({1,3}/{2,4}, indicated by the dotted grey line). Arrows indicate causally effective connections linking A to B and B to A across the bipartition (other connections may link both A and B to the rest of the system X). To measure EI…
== 文件说明 ==
Effective information, minimum information bipartition, and complexes. a. Effective information. Shown is a single
subset S of 4 elements ({1,2,3,4}, blue circle), forming part of a larger system X (black ellipse). This subset is bisected into A
and B by a bipartition ({1,3}/{2,4}, indicated by the dotted grey line). Arrows indicate causally effective connections linking A to
B and B to A across the bipartition (other connections may link both A and B to the rest of the system X). To measure
EI(A→B), maximum entropy H max is injected into the outgoing connections from A (corresponding to independent noise
sources). The entropy of the states of B that is due to the input from A is then measured. Note that A can affect B directly
through connections linking the two subsets, as well as indirectly via X. Applying maximum entropy to B allows one to measure
EI(B→A). The effective information for this bipartition is EI(A B) = EI(A→B) + EI(B→A). b. Minimum information bipartition.
For subset S = {1,2,3,4}, the horizontal bipartition {1,3}/{2,4} yields a positive value of EI. However, the bipartition {1,2}/{3,4}
yields EI = 0 and is a minimum information bipartition (MIB) for this subset. The other bipartitions of subset S = {1,2,3,4} are
{1,4}/{2,3}, {1}/{2,3,4}, {2}/{1,3,4}, {3}/{1,2,4}, {4}/{1,2,3}, all with EI>0. c. Analysis of complexes. By considering all subsets of sys-
tem X one can identify its complexes and rank them by the respective values of Φ – the value of EI for their minimum informa-
tion bipartition. Assuming that other elements in X are disconnected, it is easy to see that Φ>0 for subset {3,4} and {1,2}, but
Φ = 0 for subsets {1,3}, {1,4}, {2,3}, {2,4}, {1,2,3}, {1,2,4}, {1,3,4}, {2,3,4}, and {1,2,3,4}. Subsets {3,4} and {1,2} are not part of a
larger subset having higher Φ, and therefore they constitute complexes. This is indicated schematically by having them encir-
cled by a grey oval (darker grey indicates higher Φ). Methodological note. In order to identify complexes and their Φ(S) for sys-
tems with many different connection patterns, each system X was implemented as a stationary multidimensional Gaussian
process such that values for effective information could be obtained analytically (details in [8]). Briefly, in order to identify com-
plexes and their Φ(S) for systems with many different connection patterns, we implemented numerous model systems X com-
posed of n neural elements with connections CON ij specified by a connection matrix CON(X) (no self-connections). In order
to compare different architectures, CON(X) was normalized so that the absolute value of the sum of the afferent synaptic
weights per element corresponded to a constant value w<1 (here w = 0.5). If the system's dynamics corresponds to a multivar-
iate Gaussian random process, its covariance matrix COV(X) can be derived analytically. As in previous work, we consider the
vector X of random variables that represents the activity of the elements of X, subject to independent Gaussian noise R of
magnitude c. We have that, when the elements settle under stationary conditions, X = X * CON(X) + cR. By defining Q = (1-
CON(X))-1 and averaging over the states produced by successive values of R, we obtain the covariance matrix COV(X) =
<X*X> = <Q t * Rt * R * Q> = Q t * Q, where the superscript t refers to the transpose. Under Gaussian assumptions, all devi-
ations from independence among the two complementary parts A and B of a subset S of X are expressed by the covariances
among the respective elements. Given these covariances, values for the individual entropies H(A) and H(B), as well as for the
joint entropy of the subset H(S) = H(AB) can be obtained as, for example, H(A) = (1/2)ln [(2π e)n |COV(A)|], where |•| denotes
the determinant. The mutual information between A and B is then given by MI(A;B) = H(A) + H(B) - H(AB). Note that MI(A:B)
is symmetric and positive. To obtain the effective information between A and B within model systems, independent noise
sources in A are enforced by setting to zero strength the connections within A and afferent to A. Then the covariance matrix
for A is equal to the identity matrix (given independent Gaussian noise), and any statistical dependence between A and B must
be due to the causal effects of A on B, mediated by the efferent connections of A. Moreover, all possible outputs from A that
could affect B are evaluated. Under these conditions, EI(A→B) = MI(A Hmax ;B). The independent Gaussian noise R applied to A
is multiplied by cp , the perturbation coefficient, while the independent Gaussian noise applied to the rest of the system is given
by ci , the intrinsic noise coefficient. Here c p = 1 and ci = 0.00001 in order to emphasize the role of the connectivity and mini-
mize that of noise. To identify complexes and obtain their capacity for information integration, one considers every subset S of
X composed of k elements, with k = 2,..., n. For each subset S, we consider all bipartitions and calculate EI(A B) for each of
them. We find the minimum information bipartition MIB(S), the bipartition for which the normalized effective information reaches
a minimum, and the corresponding value of Φ(S). We then find the complexes of X as those subsets S with Φ>0 that are not
included within a subset having higher Φ and rank them based on their Φ(S) value. The complex with the maximum value of
Φ(S) is the main complex. MATLAB functions used for calculating effective information and complexes are at http://tononi.psy
chiatry.wisc.edu/informationintegration/toolbox.html.
G
Effective information, minimum information bipartition, and complexes. a. Effective information. Shown is a single
subset S of 4 elements ({1,2,3,4}, blue circle), forming part of a larger system X (black ellipse). This subset is bisected into A
and B by a bipartition ({1,3}/{2,4}, indicated by the dotted grey line). Arrows indicate causally effective connections linking A to
B and B to A across the bipartition (other connections may link both A and B to the rest of the system X). To measure
EI(A→B), maximum entropy H max is injected into the outgoing connections from A (corresponding to independent noise
sources). The entropy of the states of B that is due to the input from A is then measured. Note that A can affect B directly
through connections linking the two subsets, as well as indirectly via X. Applying maximum entropy to B allows one to measure
EI(B→A). The effective information for this bipartition is EI(A B) = EI(A→B) + EI(B→A). b. Minimum information bipartition.
For subset S = {1,2,3,4}, the horizontal bipartition {1,3}/{2,4} yields a positive value of EI. However, the bipartition {1,2}/{3,4}
yields EI = 0 and is a minimum information bipartition (MIB) for this subset. The other bipartitions of subset S = {1,2,3,4} are
{1,4}/{2,3}, {1}/{2,3,4}, {2}/{1,3,4}, {3}/{1,2,4}, {4}/{1,2,3}, all with EI>0. c. Analysis of complexes. By considering all subsets of sys-
tem X one can identify its complexes and rank them by the respective values of Φ – the value of EI for their minimum informa-
tion bipartition. Assuming that other elements in X are disconnected, it is easy to see that Φ>0 for subset {3,4} and {1,2}, but
Φ = 0 for subsets {1,3}, {1,4}, {2,3}, {2,4}, {1,2,3}, {1,2,4}, {1,3,4}, {2,3,4}, and {1,2,3,4}. Subsets {3,4} and {1,2} are not part of a
larger subset having higher Φ, and therefore they constitute complexes. This is indicated schematically by having them encir-
cled by a grey oval (darker grey indicates higher Φ). Methodological note. In order to identify complexes and their Φ(S) for sys-
tems with many different connection patterns, each system X was implemented as a stationary multidimensional Gaussian
process such that values for effective information could be obtained analytically (details in [8]). Briefly, in order to identify com-
plexes and their Φ(S) for systems with many different connection patterns, we implemented numerous model systems X com-
posed of n neural elements with connections CON ij specified by a connection matrix CON(X) (no self-connections). In order
to compare different architectures, CON(X) was normalized so that the absolute value of the sum of the afferent synaptic
weights per element corresponded to a constant value w<1 (here w = 0.5). If the system's dynamics corresponds to a multivar-
iate Gaussian random process, its covariance matrix COV(X) can be derived analytically. As in previous work, we consider the
vector X of random variables that represents the activity of the elements of X, subject to independent Gaussian noise R of
magnitude c. We have that, when the elements settle under stationary conditions, X = X * CON(X) + cR. By defining Q = (1-
CON(X))-1 and averaging over the states produced by successive values of R, we obtain the covariance matrix COV(X) =
<X*X> = <Q t * Rt * R * Q> = Q t * Q, where the superscript t refers to the transpose. Under Gaussian assumptions, all devi-
ations from independence among the two complementary parts A and B of a subset S of X are expressed by the covariances
among the respective elements. Given these covariances, values for the individual entropies H(A) and H(B), as well as for the
joint entropy of the subset H(S) = H(AB) can be obtained as, for example, H(A) = (1/2)ln [(2π e)n |COV(A)|], where |•| denotes
the determinant. The mutual information between A and B is then given by MI(A;B) = H(A) + H(B) - H(AB). Note that MI(A:B)
is symmetric and positive. To obtain the effective information between A and B within model systems, independent noise
sources in A are enforced by setting to zero strength the connections within A and afferent to A. Then the covariance matrix
for A is equal to the identity matrix (given independent Gaussian noise), and any statistical dependence between A and B must
be due to the causal effects of A on B, mediated by the efferent connections of A. Moreover, all possible outputs from A that
could affect B are evaluated. Under these conditions, EI(A→B) = MI(A Hmax ;B). The independent Gaussian noise R applied to A
is multiplied by cp , the perturbation coefficient, while the independent Gaussian noise applied to the rest of the system is given
by ci , the intrinsic noise coefficient. Here c p = 1 and ci = 0.00001 in order to emphasize the role of the connectivity and mini-
mize that of noise. To identify complexes and obtain their capacity for information integration, one considers every subset S of
X composed of k elements, with k = 2,..., n. For each subset S, we consider all bipartitions and calculate EI(A B) for each of
them. We find the minimum information bipartition MIB(S), the bipartition for which the normalized effective information reaches
a minimum, and the corresponding value of Φ(S). We then find the complexes of X as those subsets S with Φ>0 that are not
included within a subset having higher Φ and rank them based on their Φ(S) value. The complex with the maximum value of
Φ(S) is the main complex. MATLAB functions used for calculating effective information and complexes are at http://tononi.psy
chiatry.wisc.edu/informationintegration/toolbox.html.
G