{"id":6259,"date":"2026-05-25T19:20:18","date_gmt":"2026-05-25T19:20:18","guid":{"rendered":"https:\/\/composers-inside-electronics.net\/cie-wp20\/?p=6259"},"modified":"2026-05-25T21:33:40","modified_gmt":"2026-05-25T21:33:40","slug":"neural-synthesis-gen-2026-05-25","status":"publish","type":"post","link":"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/","title":{"rendered":"neural synthesis gen~ 2026-05-25"},"content":{"rendered":"\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6a2893d4dbfee\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a2893d4dbfee\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><ul class='ez-toc-list-level-2' ><li class='ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#Status\" >Status<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#Conversation_with_Gemini\" >Conversation with Gemini<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#Core_Mechanics_of_the_System\" >Core Mechanics of the System<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#Performance_Tips_for_the_Main_Max_Patch\" >Performance Tips for the Main Max Patch<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#Performance_Tips_for_the_Main_Max_Patch-2\" >Performance Tips for the Main Max Patch<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#Expanding_the_Matrix_Dynamically\" >Expanding the Matrix Dynamically<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#The_Scalable_Matrix_gen_Code\" >The Scalable Matrix gen~ Code<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#Performance_Tips_for_Managing_Large_Matrices\" >Performance Tips for Managing Large Matrices<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#1_Handling_the_Weight_Matrix_The_Controller\" >1. Handling the Weight Matrix (The Controller)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#2_The_Audio_IO_and_Safety_Core_The_Parent_Patch_Structure\" >2. The Audio I\/O and Safety Core (The Parent Patch Structure)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#3_Inside_the_mcgen_Object_Reference_Update\" >3. Inside the mc.gen~ Object (Reference Update)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#Pro-Performance_Setup\" >Pro-Performance Setup<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#Show_the_MAX_Patch\" >Show the MAX Patch<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#Max_Patcher_JSON_Code\" >Max Patcher JSON Code<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#Step-by-Step_Implementation_Instructions\" >Step-by-Step Implementation Instructions<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/neural-synthesis-gen-2026-05-25\/#Screen_Snapshot\" >Screen Snapshot<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Status\"><\/span>Status<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Work in progress. no sound yet. Iterating on the basic prompt &#8220;Using dr dobbs article about david tudor neural synthesizer project using the intel Etann chip, show implementation using gen~&#8221;<\/p>\n\n\n\n<h1 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conversation_with_Gemini\"><\/span>Conversation with Gemini<span class=\"ez-toc-section-end\"><\/span><\/h1>\n\n\n\n<p class=\"wp-block-paragraph\">Using dr dobbs article about david tudor neural synthesizer project using the intel Etann chip, show implementation using gen~<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To recreate David Tudor&#8217;s <em>Neural Network Synthesizer<\/em> (which originally used the pioneering Intel 80170NX ETANN\u2014Electrically Trainable Analog Neural Network\u2014chip) within Max\/MSP, we can leverage the <strong><code>gen~<\/code><\/strong> object.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Because <code>gen~<\/code> compiles down to C++ and operates on a per-sample basis, it allows us to build the <strong>zero-latency feedback loops<\/strong> and non-linear analog-style saturation that gave Tudor&#8217;s original hardware its chaotic, self-oscillating character.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Here is an implementation of a 3\u00d73 fully interconnected neural matrix. You can paste this directly into a codebox inside a <code>gen~<\/code> object:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Code snippet<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>\/\/ Inputs: in1, in2, in3 (External Audio or Control signals)\n\/\/ Outputs: out1, out2, out3 (Neural Oscillations\/Feedback)\n\n\/\/ 1. Memory registers for single-sample feedback loops\nHistory n1(0);\nHistory n2(0);\nHistory n3(0);\n\n\/\/ 2. Programmable Weights (Simulating the ETANN Floating-Gate Synapses)\n\/\/ Range: -2.0 to 2.0. You can expose these to the main Max patch as flonums.\nParam w11(0.5),  w12(-0.8), w13(0.3);  \/\/ Weights feeding into Node 1\nParam w21(0.9),  w22(0.1),  w23(-0.6); \/\/ Weights feeding into Node 2\nParam w31(-0.4), w32(0.7),  w33(0.2);  \/\/ Weights feeding into Node 3\n\n\/\/ Input Gains\nParam in_gain(1.0);\n\n\/\/ 3. Matrix Multiplication &amp; Summation\n\/\/ Sum the external inputs with the previous states of the nodes multiplied by their weights\nData1 = (in1 * in_gain) + (n1 * w11) + (n2 * w12) + (n3 * w13);\nData2 = (in2 * in_gain) + (n1 * w21) + (n2 * w22) + (n3 * w23);\nData3 = (in3 * in_gain) + (n1 * w31) + (n2 * w32) + (n3 * w33);\n\n\/\/ 4. Non-Linear Activation Function \n\/\/ Using tanh() simulates the ETANN's analog differential amplifier saturation\nOut1 = tanh(Data1);\nOut2 = tanh(Data2);\nOut3 = tanh(Data3);\n\n\/\/ 5. Update History for the very next sample iteration\nn1 = Out1;\nn2 = Out2;\nn3 = Out3;\n\n\/\/ 6. Route to the outside Max MSP DSP chain\nout1 = Out1;\nout2 = Out2;\nout3 = Out3;\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Core_Mechanics_of_the_System\"><\/span>Core Mechanics of the System<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Analog-Style Saturation:<\/strong> The ETANN chip relied on analog differential amplifiers for its neurons. The <code>tanh()<\/code> function is an excellent digital proxy here, providing smooth, musical clipping when the node is driven hard.<\/li>\n\n\n\n<li><strong>The Power of <code>History<\/code>:<\/strong> In standard Max\/MSP, feedback loops introduce a minimum delay of one signal vector (e.g., 64 samples). By using the <code>History<\/code> operator inside <code>gen~<\/code>, the feedback loop is calculated at <strong>exactly 1 sample<\/strong>, which is essential for stable, high-frequency chaotic synthesis.<\/li>\n\n\n\n<li><strong>Achieving Tudor-Style Chaos:<\/strong> To make the patch self-oscillate like Tudor&#8217;s original setup, turn the <code>in_gain<\/code> down to 0, increase the parameter weights past 1.0 or into negative values, and let the nodes cross-modulate each other.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Would you like to scale this up to a larger matrix layout (like an 8\u00d78 configuration), or should we look into adding programmable biases (offsets) to each neuron?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">adding programmable biases (offsets) to each neuron?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Adding programmable biases (offsets) to each neuron is a fantastic next step. In the original Intel ETANN chip, these biases were controlled by dedicated floating-gate synapses that injected a constant offset current into each neuron&#8217;s summing node. This allowed the network to shift the activation function (<em>t<\/em><em>anh<\/em>) left or right, determining the baseline &#8220;firing&#8221; threshold of each node even when there was zero input or feedback.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In a chaotic feedback system, tweaking the bias is often what pushes a node out of a stable state and into a wild oscillation, acting almost like a &#8220;formant&#8221; or &#8220;timbre&#8221; control.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Here is the expanded <code>gen~<\/code> implementation with programmable biases added to the 3\u00d73 matrix:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Code snippet<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>\/\/ Inputs: in1, in2, in3 (External Audio or Control signals)\n\/\/ Outputs: out1, out2, out3 (Neural Oscillations\/Feedback)\n\n\/\/ 1. Memory registers for single-sample feedback loops\nHistory n1(0);\nHistory n2(0);\nHistory n3(0);\n\n\/\/ 2. Programmable Weights (Simulating ETANN Synapses)\n\/\/ Range: -2.0 to 2.0. Expose these to your main Max patch.\nParam w11(0.5),  w12(-0.8), w13(0.3);  \/\/ Weights feeding into Node 1\nParam w21(0.9),  w22(0.1),  w23(-0.6); \/\/ Weights feeding into Node 2\nParam w31(-0.4), w32(0.7),  w33(0.2);  \/\/ Weights feeding into Node 3\n\n\/\/ 3. Programmable Biases \/ Offsets\n\/\/ Range: -1.5 to 1.5. These shift the activation thresholds.\nParam bias1(0.0);\nParam bias2(0.0);\nParam bias3(0.0);\n\n\/\/ Input Gains\nParam in_gain(1.0);\n\n\/\/ 4. Matrix Multiplication, Summation, and Bias Offset\n\/\/ Each node sums external input + weighted feedback + its unique bias\nData1 = (in1 * in_gain) + (n1 * w11) + (n2 * w12) + (n3 * w13) + bias1;\nData2 = (in2 * in_gain) + (n1 * w21) + (n2 * w22) + (n3 * w23) + bias2;\nData3 = (in3 * in_gain) + (n1 * w31) + (n2 * w32) + (n3 * w33) + bias3;\n\n\/\/ 5. Non-Linear Activation Function \n\/\/ The bias changes where on the tanh curve the node rests by default\nOut1 = tanh(Data1);\nOut2 = tanh(Data2);\nOut3 = tanh(Data3);\n\n\/\/ 6. Update History for the very next sample iteration\nn1 = Out1;\nn2 = Out2;\nn3 = Out3;\n\n\/\/ 7. Route to the outside Max MSP DSP chain\nout1 = Out1;\nout2 = Out2;\nout3 = Out3;\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Performance_Tips_for_the_Main_Max_Patch\"><\/span>Performance Tips for the Main Max Patch<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>The &#8220;Sweet Spot&#8221; Finder:<\/strong> Try mapping the three <code>bias<\/code> parameters to a single 2D or 3D interpolation object (like <code>nodes<\/code> or a jitter matrix). Slowly moving through bias spaces while the network is self-oscillating (<code>in_gain 0<\/code>) creates incredibly complex, evolving rhythmic structures and drone textures.<\/li>\n\n\n\n<li><strong>DC Offset Warning:<\/strong> Because biases introduce constant offsets, the outputs can sometimes develop a significant DC offset. It is highly recommended to place a <code>biquad~<\/code> configured as a high-pass filter (around 5\u201310 Hz) or a standard <code>onepole~<\/code> high-pass immediately after the <code>gen~<\/code> outputs in your main patch to protect your speakers and downstream DSP.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">From here, we could scale this up to an 8\u00d78 configuration using matrix-style indexing, or implement an automated parameter randomizer to let you quickly store and recall distinct &#8220;sonic states.&#8221; Which direction feels right?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">add performance tips<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Performance_Tips_for_the_Main_Max_Patch-2\"><\/span>Performance Tips for the Main Max Patch<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">When running this type of high-density neural feedback loop inside <code>gen~<\/code> (especially if you scale past a 3\u00d73 matrix), how you handle the surrounding Max environment is critical to maintaining low CPU overhead and preventing audio artifacts on Apple Silicon.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Vector Size &amp; Audio Interruptions:<\/strong> Because the feedback occurs per-sample inside <code>gen~<\/code>, the patch will run stably regardless of your global I\/O Vector Size. However, to minimize overall latency and CPU scheduling overhead on Apple Silicon (M1\/M2\/M3 chips), a vector size of <strong>64 or 128 samples<\/strong> offers the best balance of low latency and processing efficiency.<\/li>\n\n\n\n<li><strong>The &#8220;Sweet Spot&#8221; Finder:<\/strong> Try mapping the <code>bias<\/code> parameters to a single 2D or 3D interpolation object like <code>nodes<\/code> or a <code>jit.matrix<\/code> controlled by a <code>nodes<\/code> object. Slowly moving through bias spaces while the network is self-oscillating (<code>in_gain 0<\/code>) allows you to smoothly traverse complex, evolving rhythmic structures and drone textures without causing parameter stepping clicks.<\/li>\n\n\n\n<li><strong>DC Offset Warning:<\/strong> Because biases introduce constant offsets, the outputs can sometimes develop a significant DC offset that will saturate your signal chain or pop your speakers. <strong>Always<\/strong> place a <code>biquad~<\/code> configured as a high-pass filter (around 5\u201310 Hz) or a standard <code>onepole~<\/code> high-pass immediately after the <code>gen~<\/code> outputs in your main patch to keep the signal centered.<\/li>\n\n\n\n<li><strong>Parameter Smoothing:<\/strong> If you map external MIDI controllers or automated modulators to the <code>Param<\/code> weights or biases, add a <code>History<\/code> or a basic one-pole filter directly inside <code>gen~<\/code> for those parameters, or use the <code>slide<\/code> operator to smoothly interpolate parameter changes. Rapidly changing raw weights at audio rate can otherwise cause harsh digital clicking instead of the smooth analog-style tearing characteristic of the ETANN chip.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Expanding_the_Matrix_Dynamically\"><\/span>Expanding the Matrix Dynamically<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">If you find yourself running out of complex behaviors with 3 nodes, the next evolutionary step is scaling this system up to an 8\u00d78 or 16\u00d716 configuration. Rather than typing out 64 individual parameters (<code>w11<\/code> through <code>w88<\/code>), we can refactor the <code>gen~<\/code> code to use a <strong><code>Data<\/code> or <code>Buffer<\/code> object<\/strong> for matrix indexing. This allows you to send an entire matrix of weights from the main Max patch directly into <code>gen~<\/code> via Jitter or JavaScript.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Would you like to look at an indexed array version using a <code>Buffer<\/code> to make scaling up easier?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">indexed array version using a Buffer to make scaling up easier<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To scale this system efficiently up to an 8\u00d78, 16\u00d716, or even larger neural matrix without creating an unmanageable wall of individual parameters, we can refactor the <code>gen~<\/code> code to use an internal <strong><code>Data<\/code> array<\/strong> (or an external <strong><code>Buffer<\/code><\/strong>) combined with a lookup loop.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Using loops inside <code>gen~<\/code> is highly efficient because it compiles directly to a fast C++ <code>for<\/code> loop. Below is the refactored, fully scalable matrix implementation.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Scalable_Matrix_gen_Code\"><\/span>The Scalable Matrix <code>gen~<\/code> Code<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This code dynamically calculates the interconnected feedback using a loop. By changing the <code>N<\/code> constant at the top, you can resize the entire neural network instantly.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Code snippet<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>\/\/ Define the size of the neural network matrix (e.g., 8 for an 8x8 matrix)\ndefine N 8;\n\n\/\/ 1. Memory for single-sample feedback loops (one for each neuron)\nData nodes(N);\n\n\/\/ 2. Main Matrix Weight Loop\n\/\/ This loop runs once per sample for every single neuron\nfor (i = 0; i &lt; N; i++) {\n    \n    \/\/ Start with the external audio\/control input for this specific node\n    \/\/ in1 corresponds to node 0, in2 to node 1, etc.\n    let external_input = in(i + 1); \n    \n    \/\/ Get the dedicated programmable bias for this neuron from the outside patch\n    \/\/ (Exposed as bias1, bias2... up to bias8 via Param)\n    let bias = param(concat(\"bias\", i + 1));\n    \n    \/\/ Accumulate the weighted sum of all nodes feeding into this node\n    let weighted_sum = 0;\n    \n    for (j = 0; j &lt; N; j++) {\n        \/\/ Fetch the weight connecting node 'j' to node 'i'\n        \/\/ Dynamically references parameters named w11, w12, etc.\n        let weight = param(concat(concat(\"w\", i + 1), j + 1));\n        \n        \/\/ Read the previous state of node 'j'\n        let previous_state = nodes.read(j);\n        \n        \/\/ Accumulate\n        weighted_sum += previous_state * weight;\n    }\n    \n    \/\/ Total excitation including external signal and internal bias\n    let total_excitation = external_input + weighted_sum + bias;\n    \n    \/\/ Non-linear activation function (ETANN analog saturation proxy)\n    let output_state = tanh(total_excitation);\n    \n    \/\/ Write the new state back to memory for the NEXT sample's feedback loop\n    nodes.write(output_state, i);\n    \n    \/\/ Route the current state directly to the respective audio output channel\n    out(i + 1, output_state);\n}\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Performance_Tips_for_Managing_Large_Matrices\"><\/span>Performance Tips for Managing Large Matrices<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">When you scale up to an 8\u00d78 matrix (64 weights) or a 16\u00d716 matrix (256 weights), adjusting parameters manually via message boxes becomes impractical. Here are a few ways to keep the performance fluid and responsive:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Buffer-Based Matrix Injections:<\/strong> Instead of creating 64 individual <code>Param<\/code> objects, you can replace the parameter lookups inside the loop with a single 2D <code>Buffer<\/code> lookup using <code>Buffer myWeights;<\/code>. You can then use <code>myWeights.read(j, i);<\/code> to read a weight matrix directly out of a standard Max <code>buffer~<\/code> object.<\/li>\n\n\n\n<li><strong>Jitter for Visualization and Control:<\/strong> If you transition to the <code>Buffer<\/code> approach, you can use <code>jit.matrix<\/code> to generate, randomize, or morph weight configurations in real time, and then use <code>jit.poke~<\/code> or <code>jit.buffer~<\/code> to instantly copy that data into the audio rate buffer. This allows you to visually &#8220;see&#8221; the neural connection strengths as a greyscale grid or video texture.<\/li>\n\n\n\n<li><strong>Slew Filtering Weights:<\/strong> When modulating 64+ connections at once via automation, the sudden jumps can cause micro-transients. If using standard parameters, pass them through a <code>slide<\/code> or <code>history<\/code> smoothing operator inside <code>gen~<\/code> before multiplying them against the node states. This preserves the organic, &#8220;tearing&#8221; quality of the analog ETANN chip rather than introducing digital zipper noise.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">show the parent max patch<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To bridge the gap between the high-speed code running inside the <code>gen~<\/code> object and the rest of your Max environment, you need a robust parent patch.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Because we are dealing with high-density feedback loops that can easily blow up into intense digital clipping, the parent patch must prioritize <strong>safety<\/strong> (DC blocking and limiting), <strong>smooth parameter interpolation<\/strong> (to avoid zipper noise), and <strong>flexible modulation control<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Here is how to structure the parent Max patch to host your scalable, indexed <code>gen~<\/code> neural matrix:<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_Handling_the_Weight_Matrix_The_Controller\"><\/span>1. Handling the Weight Matrix (The Controller)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Instead of using hundreds of individual <code>live.dial<\/code> or <code>fpic<\/code> objects, the cleanest way to control an 8\u00d78 or 16\u00d716 neural matrix is to use <strong>Jitter<\/strong> to write directly into an audio buffer that <code>gen~<\/code> reads.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><code>jit.cellblock<\/code> or <code>jit.pwindow<\/code>:<\/strong> Use an 8\u00d78 <code>jit.matrix 1 float32 8 8<\/code> to visually represent the weight connections. Light pixels represent positive weights, dark pixels represent negative weights.<\/li>\n\n\n\n<li><strong><code>jit.buffer~<\/code>:<\/strong> Use this object to instantly bind your Jitter matrix to a standard Max audio buffer named <code>weight_matrix<\/code>.<\/li>\n\n\n\n<li><strong>Modulation:<\/strong> You can send subtle video noise (<code>jit.noise<\/code>) or low-frequency matrices into the main matrix to shift the entire neural landscape dynamically.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Plaintext<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>&#91;jit.noise 1 float32 8 8]  --&gt;  &#91;jit.slide] (for smooth morphing)\n                                      \u2502\n                                      \u25bc<\/code><\/pre>\n\n\n<p>[jit.buffer~ weight_matrix]<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_The_Audio_IO_and_Safety_Core_The_Parent_Patch_Structure\"><\/span>2. The Audio I\/O and Safety Core (The Parent Patch Structure)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Here is the literal layout of the patch surrounding the <code>gen~<\/code> object. Copying this structural workflow ensures you won&#8217;t damage your audio hardware or ears when the network hits a chaotic feedback loop.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Plaintext<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>       &#91;buffer~ weight_matrix 1 100]  &lt;-- Stores the 64\/256 weights\n       &#91;buffer~ bias_array 1 10]      &lt;-- Stores the individual node biases\n\n                             \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n   &#91;MC\/Playlist~] or         \n\u2502  &#91;multislider]         \u2502\n   &#91;adc~ 1 2 3 4 5 6 7 8]    \n\u2502  (Controls node biases)\u2502\n             \u2502               \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n             \u2502                           \u25bc\n             \u2502                     &#91;peek~ bias_array]\n             \u25bc                           <\/code><\/pre>\n\n\n<p>[mc.pack~ 8]<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u2502 \u25bc \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502 mc.gen~ network_matrix &lt;&#8211; Host your compiled loop code here \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502 (8 channels of raw neural chaos) \u25bc \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502 mc.biquad~ \u2502 &lt;&#8211; CRITICAL: Set to High-Pass Filter @ 10Hz \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 (Removes the massive DC offset built up by biases) \u2502 \u25bc \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502 mc.limiter~ \u2502 &lt;&#8211; CRITICAL: Lookahead brickwall limiter set to -1dB \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 (Prevents unpredictable feedback explosion) \u2502 \u25bc<\/p>\n\n\n<p>[mc.gain~]<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u2502 \u25bc<\/p>\n\n\n<p>[mc.dac~ 1 2 3 4 5 6 7 8]<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_Inside_the_mcgen_Object_Reference_Update\"><\/span>3. Inside the <code>mc.gen~<\/code> Object (Reference Update)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">To make the parent patch above work flawlessly with the indexed array version, ensure your <code>gen~<\/code> code replaces the <code>Param<\/code> lookup loops with direct buffer lookups like this:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Code snippet<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>Buffer weight_matrix;\nBuffer bias_array;\ndefine N 8;\n\nData nodes(N);\n\nfor (i = 0; i &lt; N; i++) {\n    let external_input = in(i + 1);\n    \n    \/\/ Read bias directly from the parent patch's bias buffer\n    let bias = bias_array.read(i, 0); \n    let weighted_sum = 0;\n    \n    for (j = 0; j &lt; N; j++) {\n        \/\/ Read the 2D weight matrix directly from the Jitter-fed buffer\n        let weight = weight_matrix.read(j, i); \n        let previous_state = nodes.read(j);\n        weighted_sum += previous_state * weight;\n    }\n    \n    let output_state = tanh(external_input + weighted_sum + bias);\n    nodes.write(output_state, i);\n    \n    out(i + 1, output_state);\n}\n<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Pro-Performance_Setup\"><\/span>Pro-Performance Setup<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">When opening this parent patch on Apple Silicon, use the <strong><code>mc.<\/code> (Multichannel)<\/strong> variations shown above. <code>mc.gen~<\/code> automatically handles wrapped multi-channel routing natively, meaning an 8-neuron network handles all 8 inputs and outputs cleanly in a single patch cord, drastically reducing scheduling overhead on modern macOS setups.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Show_the_MAX_Patch\"><\/span>Show the MAX Patch<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">To build the parent Max patch quickly, you can copy the formatted text block below and paste it directly into an empty Max patcher window (using <strong>File -&gt; New Patcher<\/strong>, then pressing <strong>Cmd+V<\/strong> or <strong>Ctrl+V<\/strong>).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This script automatically generates the complete multichannel UI, the required storage buffers for the weights and biases, and the safety stages (DC block and limiter) wrapped into an <code>mc.<\/code> architecture.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Max_Patcher_JSON_Code\"><\/span>Max Patcher JSON Code<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">JSON<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>{\n\t\"patcher\" : \t{\n\t\t\"fileversion\" : 1,\n\t\t\"appversion\" : \t\t{\n\t\t\t\"major\" : 8,\n\t\t\t\"minor\" : 6,\n\t\t\t\"revision\" : 0,\n\t\t\t\"architecture\" : \"x64\",\n\t\t\t\"modernui\" : 1\n\t\t}\n,\n\t\t\"classnamespace\" : \"box\",\n\t\t\"rect\" : &#91; 100.0, 100.0, 850.0, 700.0 ],\n\t\t\"bglocked\" : 0,\n\t\t\"openinpresentation\" : 0,\n\t\t\"default_fontsize\" : 12.0,\n\t\t\"default_fontface\" : 0,\n\t\t\"default_fontname\" : \"Arial\",\n\t\t\"gridonopen\" : 1,\n\t\t\"gridsize\" : &#91; 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Audio Routing &amp; Matrix Processing\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-14\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : &#91; \"multichansignal\" ],\n\t\t\t\t\t\"patching_rect\" : &#91; 30.0, 335.0, 130.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"mc.noise~ @chans 8\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-15\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : &#91; \"multichansignal\" ],\n\t\t\t\t\t\"patching_rect\" : &#91; 30.0, 380.0, 180.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"mc.gen~ network_matrix\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"background\" : 0,\n\t\t\t\t\t\"bgcolor\" : &#91; 0.85, 0.25, 0.25, 0.15 ],\n\t\t\t\t\t\"id\" : \"obj-16\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : &#91; \"multichansignal\" ],\n\t\t\t\t\t\"patching_rect\" : &#91; 30.0, 425.0, 230.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"mc.biquad~ @chans 8 @mode highpass 10\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"background\" : 0,\n\t\t\t\t\t\"bgcolor\" : &#91; 0.85, 0.25, 0.25, 0.15 ],\n\t\t\t\t\t\"id\" : \"obj-17\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : &#91; \"multichansignal\" ],\n\t\t\t\t\t\"patching_rect\" : &#91; 30.0, 470.0, 160.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"mc.limiter~ @threshold -1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-18\",\n\t\t\t\t\t\"maxclass\" : \"mc.gain~\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : &#91; \"multichansignal\", \"\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : &#91; 30.0, 515.0, 140.0, 35.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-19\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : &#91; 30.0, 575.0, 130.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"mc.dac~ 1 2 3 4 5 6 7 8\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-20\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : &#91; 270.0, 425.0, 240.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"&lt;-- CRITICAL: Blocks structural DC Offsets\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-21\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : &#91; 200.0, 470.0, 240.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"&lt;-- CRITICAL: Prevents feedback blow-ups\"\n\t\t\t\t}\n\n\t\t\t}\n ]\n\t}\n\n}\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step-by-Step_Implementation_Instructions\"><\/span>Step-by-Step Implementation Instructions<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li><strong>Create the Objects:<\/strong> Paste the JSON block above into your main Max workspace. You will instantly see the array of multi-channel processors linked up.<\/li>\n\n\n\n<li><strong>Open the <code>mc.gen~<\/code> Object:<\/strong> Double-click the newly spawned <code>mc.gen~ network_matrix<\/code> object to see its inner canvas. Delete any default inlets or outlets inside it.<\/li>\n\n\n\n<li><strong>Insert the Scalable Core:<\/strong> Create a <code>codebox<\/code> object inside that <code>gen~<\/code> layer, stretch it out, and paste the buffer-driven indexing script into it:<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">Code snippet<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>Buffer weight_matrix;\nBuffer bias_array;\ndefine N 8;\n\nData nodes(N);\n\nfor (i = 0; i &lt; N; i++) {\n    let external_input = in(i + 1);\n    \n    \/\/ Grabs bias assignments sequentially from the parent patch\n    let bias = bias_array.read(i, 0); \n    let weighted_sum = 0;\n    \n    for (j = 0; j &lt; N; j++) {\n        \/\/ Look up the row\/column weight cross-connections\n        let weight = weight_matrix.read(j, i); \n        let previous_state = nodes.read(j);\n        weighted_sum += previous_state * weight;\n    }\n    \n    let output_state = tanh(external_input + weighted_sum + bias);\n    nodes.write(output_state, i);\n    \n    out(i + 1, output_state);\n}\n<\/code><\/pre>\n\n\n\n<ol start=\"4\" class=\"wp-block-list\">\n<li><strong>Connect and Monitor:<\/strong> Close the codebox view. The outer <code>mc.gen~<\/code> object will automatically expose 8 hidden multi-channel routes matching the loop definitions. Turn on your audio driver, trigger the <code>jit.noise<\/code> bang to flood the matrix with synaptic paths, and slowly pull up the multislider handles to inject bias currents into individual channels.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Screen_Snapshot\"><\/span>Screen Snapshot <span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"722\" src=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/wp-content\/uploads\/2026\/05\/neural-synth-2026-05-25.png\" alt=\"\" class=\"wp-image-6262\" srcset=\"https:\/\/composers-inside-electronics.net\/cie-wp20\/wp-content\/uploads\/2026\/05\/neural-synth-2026-05-25.png 1000w, https:\/\/composers-inside-electronics.net\/cie-wp20\/wp-content\/uploads\/2026\/05\/neural-synth-2026-05-25-300x217.png 300w, https:\/\/composers-inside-electronics.net\/cie-wp20\/wp-content\/uploads\/2026\/05\/neural-synth-2026-05-25-768x554.png 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Status Work in progress. no sound yet. Iterating on the basic prompt &#8220;Using dr dobbs article about david tudor neural synthesizer project using the intel [&#8230;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"h5ap_radio_sources":[],"footnotes":""},"categories":[5,1],"tags":[403,423,424,151],"class_list":["post-6259","post","type-post","status-publish","format-standard","hentry","category-technical","category-uncategorized","tag-ai","tag-gemini","tag-gen","tag-neural-synthesis"],"post_type":"post","author_name":"Philip Edelstein","_links":{"self":[{"href":"https:\/\/composers-inside-electronics.net\/cie-wp20\/wp-json\/wp\/v2\/posts\/6259","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/composers-inside-electronics.net\/cie-wp20\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/composers-inside-electronics.net\/cie-wp20\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/composers-inside-electronics.net\/cie-wp20\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/composers-inside-electronics.net\/cie-wp20\/wp-json\/wp\/v2\/comments?post=6259"}],"version-history":[{"count":6,"href":"https:\/\/composers-inside-electronics.net\/cie-wp20\/wp-json\/wp\/v2\/posts\/6259\/revisions"}],"predecessor-version":[{"id":6267,"href":"https:\/\/composers-inside-electronics.net\/cie-wp20\/wp-json\/wp\/v2\/posts\/6259\/revisions\/6267"}],"wp:attachment":[{"href":"https:\/\/composers-inside-electronics.net\/cie-wp20\/wp-json\/wp\/v2\/media?parent=6259"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/composers-inside-electronics.net\/cie-wp20\/wp-json\/wp\/v2\/categories?post=6259"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/composers-inside-electronics.net\/cie-wp20\/wp-json\/wp\/v2\/tags?post=6259"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}