THE CONSCIOUSNESS ATLAS
Read. Experiment. Follow the evidence.
Every recovered mathematical record has an individual page. Browse original equations, code expressions, indexed components, parameter definitions, and explicitly unresolved source slots.
Counts refer to mathematical records, including implemented expressions and generated instances. They are not counts of independently validated theories. Exact occurrences and unresolved slots remain traceable. Read the complete 1–516 source index ↗ · Inspect the scan decisions ↗
This expression selects a state or parameter setting that minimizes the displayed objective. Its terms describe what is penalized or fitted; the feasible domain, term weights and an actual solver determine the resulting solution.
This relation contains a squared-state or squared-error term. Squaring makes that contribution nonnegative and increases the penalty for larger magnitudes; the surrounding definition specifies whether it is a cost, normalization or summary statistic.
This relation uses a gradient: the vector of local changes in a scalar objective with respect to its variables. It supplies a local direction or stationarity condition; an implementation still needs the differentiation variables, domain and update convention given in the surrounding model.
This relation uses a gradient: the vector of local changes in a scalar objective with respect to its variables. It supplies a local direction or stationarity condition; an implementation still needs the differentiation variables, domain and update convention given in the surrounding model.
This expression selects a state or parameter setting that minimizes the displayed objective. Its terms describe what is penalized or fitted; the feasible domain, term weights and an actual solver determine the resulting solution.
This relation contains a squared-state or squared-error term. Squaring makes that contribution nonnegative and increases the penalty for larger magnitudes; the surrounding definition specifies whether it is a cost, normalization or summary statistic.
This expression selects a state or parameter setting that minimizes the displayed objective. Its terms describe what is penalized or fitted; the feasible domain, term weights and an actual solver determine the resulting solution.
This expression selects a state or parameter setting that minimizes the displayed objective. Its terms describe what is penalized or fitted; the feasible domain, term weights and an actual solver determine the resulting solution.
This relation uses a gradient: the vector of local changes in a scalar objective with respect to its variables. It supplies a local direction or stationarity condition; an implementation still needs the differentiation variables, domain and update convention given in the surrounding model.
This relation uses a gradient: the vector of local changes in a scalar objective with respect to its variables. It supplies a local direction or stationarity condition; an implementation still needs the differentiation variables, domain and update convention given in the surrounding model.
This relation uses a gradient: the vector of local changes in a scalar objective with respect to its variables. It supplies a local direction or stationarity condition; an implementation still needs the differentiation variables, domain and update convention given in the surrounding model.
The relation accumulates its integrand over the stated interval or closed loop. The result depends on the integrand, integration variable, limits and trajectory. In the archived notes, a loop integral represents the intended accumulation of state or error change; its consciousness interpretation…
The notes combine the named components with tensor-product or direct-sum-style notation. The expression records the proposed organization of the model. Concrete dimensions, operator definitions and evaluation order are required before the composition is an executable numerical rule.
This is a composition of operators. The rightmost operator receives the input first, and successive operators transform its output. The overall behavior depends on every component’s definition and compatible input/output types.
The relation accumulates its integrand over the stated interval or closed loop. The result depends on the integrand, integration variable, limits and trajectory. In the archived notes, a loop integral represents the intended accumulation of state or error change; its consciousness interpretation…
The relation accumulates its integrand over the stated interval or closed loop. The result depends on the integrand, integration variable, limits and trajectory. In the archived notes, a loop integral represents the intended accumulation of state or error change; its consciousness interpretation…
This relation uses a gradient: the vector of local changes in a scalar objective with respect to its variables. It supplies a local direction or stationarity condition; an implementation still needs the differentiation variables, domain and update convention given in the surrounding model.
The source describes this relation in the following context: binding_field[174+i] = ext[i] for i = 0 to 199 7.4 Unified Consciousness (Equation 479) sum = Σ_{i=0}^{373} |binding_field[i]| Consciousness = 1 - exp(-sum × 0.01) This saturating exponential ensures consciousness approaches but…
The source describes this relation in the following context: K(query) = Σ_{k=0}^{15} θ_k × ψ_k(h(query)) 8.3 Consciousness Weighting (Equation 73) Φ(x) = 1 / (1 + |x - 1|) Items with importance near 1.0 get maximum weight. 8.4 Agent Coherence (Equation 74) C_ij = (θ_i · θ_j) / (||θ_i|| ×…
This relation contains a squared-state or squared-error term. Squaring makes that contribution nonnegative and increases the penalty for larger magnitudes; the surrounding definition specifies whether it is a cost, normalization or summary statistic.
The expression uses a saturating nonlinear transform to turn its input combination into an activation or bounded score. The coefficients control sensitivity and the offset controls the transition region; a bounded score by itself does not identify a scientific consciousness measure.
Subtracts the largest competing score from the candidate’s score. A positive margin indicates that the candidate exceeds every competitor; the definition requires at least one alternative or a specified empty-set convention.
Subtracts request time from response time. It measures elapsed response latency when the timestamps are comparable and the response event is clearly defined.
Combines response time and one minus operation-success rate. With positive weights, slower or less successful operations increase the proposed application cost.
This relation contains a squared-state or squared-error term. Squaring makes that contribution nonnegative and increases the penalty for larger magnitudes; the surrounding definition specifies whether it is a cost, normalization or summary statistic.
Measures mismatch between a desired design and modeled behavior, then adds operating cost and regularization. The subscripts restrict the same objective to a layer, device, neuron or task instance. Each version needs the source-specific behavior/design functions, environment and weights before it…
Measures mismatch between a desired design and modeled behavior, then adds operating cost and regularization. The subscripts restrict the same objective to a layer, device, neuron or task instance. Each version needs the source-specific behavior/design functions, environment and weights before it…
Measures mismatch between a desired design and modeled behavior, then adds operating cost and regularization. The subscripts restrict the same objective to a layer, device, neuron or task instance. Each version needs the source-specific behavior/design functions, environment and weights before it…
Measures mismatch between a desired design and modeled behavior, then adds operating cost and regularization. The subscripts restrict the same objective to a layer, device, neuron or task instance. Each version needs the source-specific behavior/design functions, environment and weights before it…
Measures mismatch between a desired design and modeled behavior, then adds operating cost and regularization. The subscripts restrict the same objective to a layer, device, neuron or task instance. Each version needs the source-specific behavior/design functions, environment and weights before it…
Measures mismatch between a desired design and modeled behavior, then adds operating cost and regularization. The subscripts restrict the same objective to a layer, device, neuron or task instance. Each version needs the source-specific behavior/design functions, environment and weights before it…
Maps the absolute difference between the current and predicted belief to a positive score. Agreement gives one; increasing disagreement lowers the value toward zero. The score measures this chosen discrepancy, not conscious experience directly.
The binary operator receives the current state, belief and observation and produces the next state/belief pair. This is an interface definition: the behavior is determined by the component Boolean operators and their encoding, which must be specified separately.
This version composes binary encodings of the same nine filters. The temporal operator acts first and the logic operator acts last. Binary operations require a defined encoding of state, belief and observation; the named sequence alone does not turn a higher-level cognitive role into a complete…
The archived proposal names XOR, AND, OR, population count, shifts and comparisons as the operations available to its filters. The slashes list primitives rather than a single evaluable formula. The actual circuit or program must specify how those primitives are connected.
Divides the number of incorrect bits by the total transmitted or checked bits. The denominator must be positive and both counts must describe the same sample. This is a standard communication-error metric used in the proposed physical-layer cost.
Scales the difference between pool and local confidence by β_max and clamps the contribution. The source’s clamp argument order is inconsistent with common signatures, so the intended [0,β_max] interval must be specified before implementation.
This is a composition of operators. The rightmost operator receives the input first, and successive operators transform its output. The overall behavior depends on every component’s definition and compatible input/output types.
This relation declares the set in which the stated variable is allowed to take values. It is a domain constraint or type declaration, not a state-update algorithm.
The expression uses a saturating nonlinear transform to turn its input combination into an activation or bounded score. The coefficients control sensitivity and the offset controls the transition region; a bounded score by itself does not identify a scientific consciousness measure.
This relation declares the set in which the stated variable is allowed to take values. It is a domain constraint or type declaration, not a state-update algorithm.
This relation declares the set in which the stated variable is allowed to take values. It is a domain constraint or type declaration, not a state-update algorithm.
Forms a confidence-dependent learning rate and clips it between the configured minimum and maximum. The bounds control allowed update intensity.