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Building a Holographic Digit Classifier with NumPy and MNIST

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 In this post, we’ll explore how to build a simple, yet powerful, holographic memory system to classify handwritten digits from the popular MNIST dataset . Using NumPy , we’ll create a system that represents digits as holographic encodings —dense vector representations—and compares new images using cosine similarity . This approach draws inspiration from vector symbolic architectures (VSAs) and holographic reduced representations (HRRs) used in associative memory and cognitive modeling. 📦 Step 1: Organizing the Dataset Assume you have MNIST digit data where each digit ( 0 to 9 ) is stored in its own folder. Each image has been preprocessed and saved as a .npy file (a NumPy array), possibly as a vector encoding or 2D array. Folder structure: python-repl Copy Edit /mnist_vectors/ ├── 0/ │ ├── img_0.npy │ └── ... ├── 1/ │ └── ... ... ├── 9/ 🧪 Step 2: Create the Holographic Memory File We’ll create a single file ( digit_holograms.npz ) that stores a prototype ve...
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Mapping the Drosophila Eye

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  T o emulate the Drosophila eye sensory system, I had to map what O mmatidia  to the optical sensory neurons. I used the Flywire.ai right eye map from this map:  Retina Grid . Taking each ommatidia, I first had to map the column id associated to the ommatidia. Here is that map by column id: I was able to create the eye map into a grid that has 28 columns and 28 rows, and put this grid into a MS SQL table: C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26 C27 C28 0 0 0 0 0 0 0 0 787 0 660 207 494 448 476 380 65 295 352 729 41 784 783 0 0 0 0 0 0 0 0 0 0 0 796 788 402 438 322 767 631 138 282 202 186 237 223 343 222 645 8 782 781 0 0 0 0 0 0 0 779 778 629 453 324 570 353 472 621 284 329 680 618 707 625 693 753 744 692 442 7...
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Making Dronesophila Part 1

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       E mulating the Drosophila nervous system was step one which I did successfully on a single laptop using functional groups. This works VERY well.   The functional groups I created were: Neuron Group    Synapse Count central                          1455116 optic_lobes                1217922 visual_projection      268547 AN                                    115292 Kenyon_Cell                92875 ALPN                          88302 CX                                    85831...
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Use Cases for the emulation of the Drosophila nervous system

 I am often asked, especially by VC types, what can you do with animal emulations? What they are really asking is how can this be applied and how can we make money? I will admit, emulating a nervous system is like telling people you are developing a new computer operating system. There are thousands of things you can run on an operating system so asking for the One thing, is analogous to asking what is the one thing you can run on your new OS that stands apart from all others? There is no One thing but thousands of things.  If I emulated a complete human level system, what is the One thing it can do that would exceed all others? The human brain is capable of thousands and thousands of things it can do. I think you get the picture.  The One thing that I have seen with all animal nervous system emulations is generalized intelligence. The one thing that current AI cannot accomplish which is being able to quickly adapt to any given situation and as we climb the evolutionary ...
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How do we use the Connectome for AGI?

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Using the connectome for AGI is the tricky part of the process. Tricky because it does take some thought behind how to associate sensory neurons to real world concepts and how to read the motor output in order to determine the results. To do this we break down the process simply as Sensory Input > Cortical Processing > Motor Output ( > Sensory Processing > Cortical Processing > ...). Sensory Processing Each connectome has a set of sensory neurons. When we create our AGI, we connect weights to the sensory neurons based on how we perceive the sensory input on a particular sensory neuron. As an example, if we want our AGI system to avoid particular situations, we connect that sensory input to sensory neurons that would cause the connectome to sense pain or unpleasant conditions. If we want our AGI system to want or like a particular input, we connect that sensory input to sensory neurons that would cause pleasant conditions in the connectome. Using Robotics makes this p...
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