Sfilral Neural Node: A Cognitive Architecture Based on Antisymmetric Spiral Dynamics

Author: Oleg S. Basargin Researcher, Inventor, and Chairman Fund for the Study of the Nature of Time (FSNT), Russia

Abstract

The Sfilral Neural Node (SfN) introduces a novel cognitive-inspired architecture based on antisymmetric spiral dynamics. This structure enables bidirectional signal modulation, phase-sensitive memory, and symbolic reversibility within a single neural unit. Unlike conventional neurons, SfNs operate through dynamic phase transitions between excitation and inhibition, opening new pathways for context-aware and temporally reversible computation. This paper presents the conceptual foundation, structural organization, and integration strategies of SfNs within neural networks, outlining their relevance to symbolic AI, temporal pattern recognition, and cognitive modeling.

Keywords

Sfilral Neural Node, cognitive reversibility, spiral dynamics, symbolic processing, phase resonance, neural architecture, memory modeling, context-sensitive networks, interpretable AI.

1. Introduction

In recent decades, we have witnessed the rapid development of neural network architectures inspired by biological systems. However, most models remain linear or hierarchical in structure and are limited in their ability to reproduce deeper cognitive processes, such as state reversibility, resonance, and symbolic activation.

This paper proposes an alternative approach to the design of a neural node, based on the principles of antisymmetric spiral dynamics. The proposed unit, referred to as the Sfilral Neural Node (SfN), derives its architecture from the Sfilral model—a unique structure consisting of two mirror-reflected, antisymmetric coils connected by an intermediate phase loop. This topology enables the node not only to transmit signals but also to modulate them according to phase context, thereby modeling cognitive temporal reversibility.

The goal of this paper is to present the architectural and cognitive foundations of the SfN model and to demonstrate its potential applications in next-generation neural network systems, including symbolic processing, phase synchronization, and the formation of memory patterns resistant to input distortion.

2. Concept of Antisymmetric Spiral Dynamics

The concept of antisymmetric spiral dynamics originates from the physical and symbolic structure known as the Sfilral. Unlike traditional spirals that propagate in a single direction, the Sfilral embodies two mirror-opposed helices connected through an S-shaped phase loop. This configuration inherently encodes a dual-state logic: activation and deactivation, progression and regression, excitation and inhibition.

At the cognitive level, such duality mirrors the processes of associative memory retrieval, recursive reasoning, and symbolic inversion. By incorporating this spiral structure into a neural node, we aim to create a unit capable of temporal modulation and bidirectional information processing.

In contrast to linear signal propagation in classical neurons, the Sfilral node introduces a looped dynamic where inputs can be contextually re-evaluated and transformed. This creates a form of phase memory—where a previous state is not simply overwritten, but preserved and possibly reactivated, depending on the trajectory of the input signal.

This structural paradigm opens the door to modeling aspects of cognition that are typically difficult to capture in standard artificial networks, such as self-referential thought, conceptual reversibility, and symbolic abstraction.

3. Architecture of the Sfilral Neural Node

The Sfilral Neural Node (SfN) is built upon a triadic structure: two primary branches arranged in antisymmetric spiral formation, and a central S-phase connector acting as a resonance modulator. Each branch corresponds to a functional polarity—one for excitation, the other for inhibition—while the S-phase connector enables dynamic interaction and transformation between these polarities.

Inputs arriving at the SfN are not processed linearly. Instead, they traverse a path influenced by the current phase configuration of the node. For instance, if a signal enters during an excitatory phase, it follows the path of amplification through the positive spiral arm. Conversely, during an inhibitory phase, the same signal may be absorbed or redirected.

This internal switching mechanism provides the basis for what we refer to as cognitive reversibility—the ability of the node to re-evaluate or invert its output based on accumulated phase state, context, or external modulation. Moreover, the S-phase loop acts as a transient memory reservoir, storing traces of past activations that may influence future responses.

The architecture thus supports both unidirectional activation (as in conventional feedforward networks) and recurrent modulation (allowing recursive feedback without strict top-down hierarchy). This combination makes the SfN particularly well-suited for tasks requiring symbolic processing, phase resonance detection, and context-aware decision flows.

4. Principle of Cognitive Reversibility

Cognitive reversibility refers to the capacity of a system to return to a prior informational state or to process inputs in a way that inherently acknowledges temporal loops and symbolic inversions. Unlike simple recurrent feedback in traditional neural networks, reversibility in the Sfilral Neural Node (SfN) is embedded structurally—arising from its antisymmetric spiral topology.

The dual-arm architecture allows the SfN to encode both the forward activation and the potential reversal path within its internal state. When a signal propagates through the excitatory branch, it leaves a trace in the S-phase loop—a transient, context-sensitive memory layer. Depending on subsequent stimuli or phase transitions, this trace may be reactivated, prompting the system to traverse the inhibitory branch in a compensatory or evaluative mode.

This behavior models a key aspect of human cognition: the ability to revisit decisions, reinterpret meanings, and simulate counterfactuals. For example, in linguistic processing, this could correspond to the parsing of ambiguous phrases where meaning is clarified retroactively. In symbolic reasoning, it supports the mental manipulation of symbols beyond linear causality.

The reversibility mechanism also implies a form of energy symmetry: the system does not expend additional resources to undo a state but rather channels stored phase information through its structured topology. This property makes the SfN a promising element in designing networks capable of low-energy symbolic inference and phase-based decision logic.

5. Integration into Neural Networks

The integration of the Sfilral Neural Node (SfN) into neural architectures requires rethinking not only the microstructure of the node itself, but also the macro-dynamics of information flow across the network. Traditional neural networks operate under assumptions of layer-based hierarchy and feedforward causality. In contrast, SfN introduces phase-based interactions and non-linear resonance behaviors that demand novel organizational principles.

In a network composed of SfNs, each node maintains its own internal phase memory, which interacts with external signals based on phase alignment. This creates a landscape of localized resonances: clusters of nodes can synchronize, desynchronize, or even invert activation patterns depending on contextual inputs. Such behaviors are useful in domains that require high sensitivity to symbol order, reversibility, and dynamic reconfiguration—such as natural language understanding or adaptive control systems.

Moreover, the phase-sensitive coupling between nodes opens the possibility for emergent computation: rather than a fixed pathway determining the outcome, the result arises from transient patterns of resonance across the network. This aligns with models of cognition that emphasize emergence, plasticity, and self-organization.

In practice, networks built with SfNs may resemble modular cognitive units rather than uniform layers. Each module can function semi-independently, reflecting localized cognitive functions (e.g., parsing, abstraction, error correction), yet remain capable of phase-synchronized interaction with others.

This approach lays the groundwork for constructing cognitively inspired architectures that are both interpretable and dynamically flexible, with potential applications in symbolic AI, temporal pattern recognition, and systems requiring adaptive feedback under uncertainty.

6. Conclusion and Future Work

The Sfilral Neural Node (SfN) offers a novel architectural approach rooted in the principles of antisymmetric spiral dynamics, enabling cognitive operations such as symbolic modulation, temporal reversibility, and context-sensitive memory. By structurally embedding phase transitions and dual-path signal logic into the design of a single node, the SfN transcends the limitations of conventional linear and hierarchical neural architectures.

This paper has outlined the conceptual foundations, internal mechanisms, and potential network-level integrations of SfNs. These nodes pave the way for new directions in neural computation—especially in domains requiring interpretability, symbolic abstraction, and temporal depth.

Future work will focus on the implementation of SfNs in simulated environments, comparative benchmarking with classical nodes in symbolic and temporal tasks, and the exploration of hybrid architectures where Sfilral nodes operate alongside conventional neurons. Another avenue of interest includes the development of training protocols capable of guiding the phase dynamics within SfNs, potentially leading to more robust, energy-efficient, and cognitively coherent artificial systems.

Ultimately, the Sfilral framework invites a rethinking of neural intelligence—not as a linear accumulation of activations, but as a structured dance of reversals, resonances, and symbolic loops echoing the logic of mind.

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