Reference library
Analog computation via neural networks
Paper summary
Studies analog recurrent neural computation with a fixed network structure. Under idealized real-valued parameters, the model's computational power and resource bounds relate to nonuniform circuits and can exceed ordinary digital computation under the stated assumptions.
Relevance to the project
Clarifies why numerical precision must be explicit when discussing what neural primitives can compute. The project's finite-precision engine should not inherit ideal analog claims.
Exact real-number assumptions are not available in ordinary floating-point execution.
Source record
- Reference ID
- pdf-https-www-sontaglab-org-ftpdir-siegelmann-sontag-tcs1994-pdf
- Archived version
- Direct PDF cited in project notes; publication status needs source-specific inspection
- Summary provenance
- reference-library/papers/pdf-https-www-sontaglab-org-ftpdir-siegelmann-sontag-tcs1994-pdf/metadata.json · summary
- Review status
- initial orientation summary; not a critical full-paper review
- Reading scope
- Selected abstract/opening and available closing discussion/conclusion passages in the preserved text extract; broader project notes provide context. No reproduction of experiments, exhaustive methods review, or equation/figure audit.