Illustration of the magnetic beam focuser.

<p><b>A:</b> Particle distribution in a cross-section of the focal plane, shown for 50,000 particles, both for a set of two ideal quadrupoles and our configuration (CT-RTRL results). The cost function, the root mean squared distance (RMSD) of the particles w.r.t. the focal point as they pass through the focal plane, is shown underneath the panels. On the right we show the scale compared to the original beam width (red circle). Note that the spread of the particles for the quadrupoles is largely due to a relatively large spread in particle velocities. <b>B:</b> Illustration of the particle beam envelope (light green) and the spatial configuration of magnets (blue red cones), indicating position, direction and magnitude. <b>C:</b> Cut-through illustrations of the particle beam envelope and the lateral magnetic field at different positions throughout the beam focuser.</p

Embedding a trajectory in an MSD-network.

<p><b>A:</b> Depiction of the initial condition of the MSD-system. The grey circles have fixed positions, the black circles are point masses that are non-fixed, and the red circle is the point mass of which we wish to control the trajectory. The connecting lines represent massless linear spring-dampers. <b>B:</b> Illustration of the trajectory of the selected node after optimisation. The full blue line is the actual trajectory, (including the trajectory after completing the pentagram) and the dashed line is the target. <b>C:</b> Comparison of the convergence speed of gradient descent and CMA-ES for the same initial parameter set.</p

Results for optimised SOA networks.

<p><b>A:</b> Schematic representation of the SOA-network. The black lines are photonic waveguides. The circles are the SOAs, with the arrows indicating in which direction the light passes through. Each SOA receives an external input signal, and at its output, a channel goes to the output (represented by the dotted lines). These channels are optimally combined optically and the output signal is the optical power of this combination. <b>B:</b> Illustration of the photonic D flip-flop. The top two time traces show the two input channels, the clock (set) signal and the data signal. The red time trace is the desired output power of the SOA network, and the blue one is a superposition of the measured output power for 10 different instances, each with different noise and internal phase variations. <b>C:</b> Illustration of the photonic 5-bit one-hot detector. The black time trace is the input signal, a random bit stream, and the red time trace is the desired output power of the SOA network. The blue one is a superposition of the measured output power for 10 different instances, each with different noise and internal phase variations.</p

Supplementary document for Rigorous dynamical model of a silicon ring resonator with phase change material for a neuromorphic node. - 5903756.pdf

Supplemental document

MSD robots.

<p>We have represented the springs that constitute the MSD robots with lines, where we visualized their modulation amplitude by making springs with large modulation amplitudes thicker and redder. The blue line represents the ground. <b>A:</b> Shape of the robot at the initialization of training. All springs have an equally large modulation amplitude. <b>B:</b> Snapshot of an example robot after finishing training. Only 4 of its springs still have a non-negligible modulation amplitude, and they provide virtually all locomotive power.</p

Media 1: Nonlinear lattice model for spatially guided solitons in nonlinear photonic crystals

Originally published in Optics Express on 07 March 2005 (oe-13-5-1544

Media 2: Nonlinear lattice model for spatially guided solitons in nonlinear photonic crystals

Originally published in Optics Express on 07 March 2005 (oe-13-5-1544

Emergent self-adaptation in an integrated photonic neural network for backpropagation-free learning

Plastic self-adaptation, nonlinear recurrent dynamics and multi-scale memory are desired features in hardware implementations of neural networks, because they enable them to learn, adapt and process information similarly to the way biological brains do. In this work, we experimentally demonstrate these properties occurring in arrays of photonic neurons. Importantly, this is realised autonomously in an emergent fashion, without the need for an external controller setting weights and without explicit feedback of a global reward signal. Using a hierarchy of such arrays coupled to a backpropagation-free training algorithm based on simple logistic regression, we are able to achieve a performance of 98.2% on the MNIST task, a popular benchmark task looking at classification of written digits. The plastic nodes consist of silicon photonics microring resonators covered by a patch of phase-change material that implements nonvolatile memory. The system is compact, robust, and straightforward to scale up through the use of multiple wavelengths. Moreover, it constitutes a unique platform to test and efficiently implement biologically plausible learning schemes at a high processing speed

Integrated Photonic Reservoir Computing with All-Optical Readout

Integrated photonic reservoir computing has been demonstrated to be able to tackle different problems because of its neural network nature. A key advantage of photonic reservoir computing over other neuromorphic paradigms is its straightforward readout system, which facilitates both rapid training and robust, fabrication variation-insensitive photonic integrated hardware implementation for real-time processing. We present our recent development of a fully-optical, coherent photonic reservoir chip integrated with an optical readout system, capitalizing on these benefits. Alongside the integrated system, we also demonstrate a weight update strategy that is suitable for the integrated optical readout hardware. Using this online training scheme, we successfully solved 3-bit header recognition and delayed XOR tasks at 20 Gbps in real-time, all within the optical domain without excess delays