Reconfigurable Nonlinear Photonic Networks for In-Situ Learning and Memory Formation via Driven-Dissipative Dynamics

• URL: http://arxiv.org/abs/2605.19911v1
• Date: Tue, 19 May 2026 14:38:33 GMT
• Title: Reconfigurable Nonlinear Photonic Networks for In-Situ Learning and Memory Formation via Driven-Dissipative Dynamics
• Abstract summary: Photonic neuromorphic computing offers a promising route to overcoming the limitations of conventional von Neumann architectures.<n>I propose a physically grounded neuromorphic framework in which memory, and learning emerge directly from driven-dissipative dynamics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Photonic neuromorphic computing offers a promising route to overcoming the limitations of conventional von Neumann architectures by exploiting the high bandwidth, low latency, and massive parallelism of optical systems. However, most existing implementations rely on fixed dynamical substrates such as classic reservoir computing, where learning is restricted to external readout layers and memory is limited to transient fading effects. In this work, I propose a Reconfigurable Nonlinear Photonic Decision Network (RNPDN), a physically grounded neuromorphic framework in which computation, memory, and learning emerge directly from driven-dissipative dynamics. Through numerical simulations, I demonstrate the simultaneous realization of key properties: local physical learning rules enabling adaptive state evolution, a tunable stability-plasticity tradeoff governed by decay and hysteresis mechanisms, controlled memory formation and erasure via bistable photonic states, fading memory, in-situ learning, and hardware-faithful nonlinear dynamics incorporating saturation and dissipation. In contrast to conventional approaches, the proposed system enables intrinsic adaptation within the physical layer while supporting both transient and persistent memory. These results establish a unified framework for adaptive photonic information processing and provide a pathway toward scalable and energy-efficient neuromorphic photonic hardware.

Related papers

• Dynamics and Representation Structure of Local Approximations to Gradient-Based Learning in Linear Recurrent Neural Networks [56.42974363744522]
  We study the learning dynamics of random feedback local online (RFLO) and truncated backpropagation through time (tBPTT)<n>Our work provides analytical insight into how locality constraints shape learning dynamics, with implications for neuroscientific models of learning and alternative optimization approaches for RNNs.
  arXiv  Detail & Related papers  (2026-05-29T18:19:45Z)
• TIDE: Asymmetric Neural Circuits for Stabilized Temporal Inhibitory-Excitatory Dynamics [51.37317469455798]
  Recent Continuous Thought Machine architecture decouples internal computation from external inputs via neural dynamics.<n>We propose to model neural dynamics using asymmetric Excitatory-Inhibitory (E-I) networks.<n>We introduce Temporal Inhibitory-Excitatory Dynamic Engine (TIDE), a neuro-inspired architecture that computes internal representations through neural dynamics stabilized.
  arXiv  Detail & Related papers  (2026-05-19T05:59:13Z)
• Optical Neural Networks from Coherent Transient Dynamics in Waveguide QED [0.0]
  Optical neural networks promise ultrafast, low-energy information processing by performing computation directly with photons.<n>We propose an all-optical fully connected neural network architecture in which the basic neuronal functions are realized by coherent quantum dynamics.<n>Full-physics simulations demonstrate high classification accuracy on MNIST and colored-object recognition tasks.
  arXiv  Detail & Related papers  (2026-05-18T02:11:34Z)
• Kirchhoff-Inspired Neural Networks for Evolving High-Order Perception [51.28469959548178]
  We propose the Kirchhoff-Inspired Neural Network (KINN), a state-variable-based network architecture constructed based on Kirchhoff's current law.<n>KINN derives numerically stable state updates from fundamental ordinary differential equations, enabling the explicit decoupling and encoding of higher-order evolutionary components.
  arXiv  Detail & Related papers  (2026-03-25T06:17:31Z)
• ReDON: Recurrent Diffractive Optical Neural Processor with Reconfigurable Self-Modulated Nonlinearity [4.488347887618485]
  We introduce the Recurrent Diffractive Optical Neural Processor (ReDON), a novel architecture featuring reconfigurable, recurrent self-modulated nonlinearity.<n>Inspired by the gated linear unit (GLU) used in large language models, ReDON senses a fraction of the propagating optical field and modulates its phase or intensity via a lightweight parametric function.<n>On image recognition and segmentation benchmarks, ReDON improves test accuracy and mean intersection-over-union (mIoU) by up to 20% compared with prior DONNs.
  arXiv  Detail & Related papers  (2026-02-27T02:49:05Z)
• Physics-Informed Image Restoration via Progressive PDE Integration [11.13952011916121]
  We propose a training framework that integrates physics-informed PDE dynamics into state-of-the-art restoration architectures.<n>Our approach naturally captures the directional flow characteristics of motion blur while enabling principled global spatial modeling.<n>Our PDE-enhanced deblurring models achieve superior restoration quality with minimal overhead, adding only approximately 1% to inference GMACs.
  arXiv  Detail & Related papers  (2025-11-09T06:10:20Z)
• Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers [4.1673753346810765]
  We present a framework for the end-to-end training of hybrid networks with reconfigurable physical layers.<n>A key component of our approach is the implicit projector-splitting integrator algorithm, which updates the lightweight surrogate model after each forward pass.<n>We demonstrate our method across diverse deep learning tasks, including: computer vision, audio classification, and language modeling.
  arXiv  Detail & Related papers  (2025-09-18T16:17:44Z)
• Fractional Spike Differential Equations Neural Network with Efficient Adjoint Parameters Training [63.3991315762955]
  Spiking Neural Networks (SNNs) draw inspiration from biological neurons to create realistic models for brain-like computation.<n>Most existing SNNs assume a single time constant for neuronal membrane voltage dynamics, modeled by first-order ordinary differential equations (ODEs) with Markovian characteristics.<n>We propose the Fractional SPIKE Differential Equation neural network (fspikeDE), which captures long-term dependencies in membrane voltage and spike trains through fractional-order dynamics.
  arXiv  Detail & Related papers  (2025-07-22T18:20:56Z)
• Event-Driven Imaging in Turbid Media: A Confluence of Optoelectronics and Neuromorphic Computation [9.53078750806038]
  A new optical-computational method is introduced to unveil images of targets whose visibility is severely obscured by light scattering in dense, turbid media. The scheme is human vision inspired whereby diffuse photons collected from the turbid medium are first transformed to spike trains by a dynamic vision sensor as in the retina. Image reconstruction is achieved under conditions of turbidity where an original image is unintelligible to the human eye or a digital video camera.
  arXiv  Detail & Related papers  (2023-09-13T00:38:59Z)
• NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory [70.10550467873499]
  We propose NeuralClothSim, a new quasistatic cloth simulator using thin shells. Our memory-efficient solver operates on a new continuous coordinate-based surface representation called neural deformation fields.
  arXiv  Detail & Related papers  (2023-08-24T17:59:54Z)
• Unsupervised Domain Transfer with Conditional Invertible Neural Networks [83.90291882730925]
  We propose a domain transfer approach based on conditional invertible neural networks (cINNs) Our method inherently guarantees cycle consistency through its invertible architecture, and network training can efficiently be conducted with maximum likelihood. Our method enables the generation of realistic spectral data and outperforms the state of the art on two downstream classification tasks.
  arXiv  Detail & Related papers  (2023-03-17T18:00:27Z)
• Reservoir Computing with Magnetic Thin Films [35.32223849309764]
  New unconventional computing hardware has emerged with the potential to exploit natural phenomena and gain efficiency. Physical reservoir computing demonstrates this with a variety of unconventional systems. We perform an initial exploration of three magnetic materials in thin-film geometries via microscale simulation.
  arXiv  Detail & Related papers  (2021-01-29T17:37:17Z)
• Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning [68.9515120904028]
  Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the building block.
  arXiv  Detail & Related papers  (2020-07-21T11:48:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.