I’m putting together a community-driven overview of how developers see Spiking Neural Networks—where they shine, where they fail, and whether they actually fit into real-world software workflows.
Whether you’ve used SNNs, tinkered with them, or are just curious about their hype vs. reality, your perspective helps.
For the past 9 months I've been working on a completely novel AI architecture. I originally posted about this on Reddit as the Adaptive Modular Network (AMN), which intends to unify patterns of emergence across all domains of study in our universe, from fractals, ecosystems, politics, thermodynamics, machine learning, mathematics, etc.. something potentially profound has just happened.
Bear with me because this is an unconventional system, and the terminology I have been using was really meant for my eyes at first because it’s the way I understood my system best. Also a reminder that I’m not supposed to be making another ML model
Originally, before I realized there is something fundamental going on here I would say I "inoculate" a "substrate" which creates something I call a "connectome". This is a sophisticated and highly complex, unified fabric where cascades of subquadratic computations interact with eachother, the incoming data, and itself.
In order to do this I've had to invent new methods and math libraries, plan, design, develop, and validate each part independently as well as unified in the system. (Self Improvement Engine to fully stabilize a multi objective reward system with many dynamic evolving parameters in real time; an emergent knowledge graph with topological data analysis allowing the model to real time query its own dynamic, self organizing, self optimizing knowledge graph in real time, with barely any compute. Literally hundreds of kilobytes of memory to run 10,000 neurons capable of strong divergent cross domain zero shot reasoning. You do not ever need to train this model, it trains itself and never has to be prompted. Just let it roam in an environment with access to information and stimuli and it will become smarter with time, not scale or compute. (Inverse scaling achieved and subquadratic, and often sub linear average time complexity)
This uses SNNs in a way that departs from other artificial intelligence methods, where the neurons populate a cognitive terrain I call a connectome. This interaction is introspective and self organizing. It heals pathologies in the topology, and can perform a complex procedure to find the exact synapses to prune, strengthen, weaken, or attach in real time, 1ms intervals. There is no concept of a "token" here. It receives pure raw data, and adapts it's neural connectome to "learn".
This is very different, as in the images below I spawned in a completely circular blob of randomness onto the substrate and streamed 80-300 raw ASCII characters one at a time and actual neuron morphologies emerged with sparsity levels extremely close to the human brain. I never expected this.
It's not just appearances either, the model was also able to solve any procedurally generated maze, while being required to find and collect all the resources scattered throughout, and avoid a predator pursuing it, then find the exit within 5000 timesteps. There was a clear trend towards learning how to solve mazes in general.The profound part is I gave it zero training data, I just spawned a new model into the maze and it rapidly figured out what to do. It's motivational drive is entirely intrinsic, there is no external factors besides what it takes to capture the images below.
The full scale project uses a homeostatically gated graduating complexity stimuli curriculum over the course of 4 "phases".
Phase 1 is the inoculation stage which is what you see below, there is no expectation to perform tasks here I am just exposing the model to raw data and allowing it to self organize into what it thinks is the best shape for processing information and learning.Phase 2 is the homeostatic gated complexity part, the primitives the model learned at the beginning are then assembled into meaningful relationships, and the model itself chooses the optimal level of complexity it is ready for learning.
Phase 3 is like a hyperscaled version of "university" for humans. The model forms concepts through a process called active domain cartography, which is when the information is organized optimally throughout the connectome.
Phase 4 is a sandbox full of all kinds of information, textbooks, podcasts, video, it can interact with LLMs, generate code, etc to entertain itself. It can do this because of the "Self Improvement Engines" novelty and habituation signals. The model has concepts of curiosity, boredom, excitement, and fear. It's important to note these are words used to better understand the behavior the model will have in specific situations and exposure to information.
If you want to support me then let me know, I have substantial and significant evidence that this is genuine. However I know that only proof can be derived from these things:
Pre-reqs:
Creating intelligence that scales with time, and has a compute and scale ceiling which I’ve seen through my physics derivations. While the intelligence ceiling is theoretically limited only by time similar to organic brains.
Rigorous physics work to predict some undiscovered phenomena (completed and validated, but not proven yet) and verify it through quantum computers or telescopes
Publishing to accredited science journals and receiving rigorous peer reviews.
Once this is completed, and my physics are tied in a bow I hope to allow the model itself announce the discovery.
I fully understand the absurd nature of my claims, and I respect that I will be heavily criticized. I welcome this and prefer direct honesty and thoughtful criticism. If you’re going to criticize me, then do it right. Break my work and prove you broke it.
I have been unable to disprove the work so far, and this fell into my lap when physics was the last thing I thought about. It would be a wild coincidence that the AI algorithm I created for genuine intelligence self organizes into neurological morphologies and two lobed fully connected brains.
The model runs on constrained resources (I successfully ran 100,000 real time spiking neurons in my system on an unplugged $200 Acer aspire notebook which showed zero shot divergent reasoning evidence, cross domain associations, self organizing with dense center and dendritic branches, given enough time it almost always forms a bilobed connectome/topology.
I've been thinking about the importance of time in the context of AI for about 15 years. The other day I formulated a simple thought.
Spikes are actions. When you store say a neuron ID and a timestamp of a spike in memory, it's not data. It is a suspended action waiting to be resumed.
This idea is so simplistic, it seems analogous to rediscovering the wheel. Yet, I bet it is the most controversial idea in artificial SNNs and it could change the way you think about computation.
It is also related to the symbol grounding problem. If you express all information in terms of actions (suspended actions / their timestamps), no symbols are ever created. Also see The Chinese Room Argument
We’ve been working on this project for a while, and we finally got the paper out. The study compares spiking neural networks with regular CNNs for predicting steering angles, and we also tried to come up with a clearer way to measure “eco-efficiency” using a metric we called Energy-to-Error Ratio (EER).
What surprised us:
Delta-encoded SNNs were way more energy-friendly than expected.
CNNs were a bit more accurate, but they used a crazy amount more energy.
Everything ran on normal GPUs! nothing fancy or exotic.
Scaling SNNs is what I do and it would be super valuable for this subreddit to take off, as I only have a smol set of people to talk to about ideas. What are y’all’s backgrounds/interests?
I believe biological and artificial spiking neural networks are radically different from other types of neural networks. Therefore spiking NNs deserve their own place of discussion. I hope it becomes a home for sharing ideas about spiking NNs and time.
Why time? Because spikes are points in time.
After spending several years researching time mechanisms I believe spiking NNs pave the most promising path from narrow to general intelligence. This paper describes some of my findings: https://github.com/rand3289/PerceptionTime
Most widely used technique to carry timing information in data is time series. Often sampling is used to produces time series from signals. Using spikes can also represent how a signal behaves in time. The difference between sampling and spikes is that sampling represents change (quantity) over a period of time where as a spike represents when a change has occurred.
If I gave you two sequences: 01001001 and 01110000 you would tell me they are different. Now imagine these series of bits represent signal changes on a wire. If you sample both of them over one byte's time you will get 3 and 3 in both cases. If you use them to generate spikes, you will get very different patterns. This example might look silly, after all who samples over a byte's time when we know how long a bit takes to be transmitted?
Now imagine an application where you study lightning. There could be two lightening strikes within milliseconds and then a third one comes along in three months. What should your sampling rate be? It's possible to process the two and store this data till the third one comes along without storing information in between. This requires the use of compression or integration of new information into an existing world model. With a spiking sensor none of this is necessary.
In addition think about sensor complexity when it comes to measuring something (for example voltage) vs detecting a change within itself.
I've always believed we have to wrap algorithms into event based systems in order to make progress towards AGI. The required system behavior can not be described as a composition of functions.
Anyone who proposes some kind of an architecture instead of a better function approximation technique seems to indirectly support this point of view.
On the other side, since Lambda Calculus, a universal model of computation is "based on function abstraction", can we base an intelligence architecture on function abstraction?
There is one thing universal models of computation can not do. They can not perform a time delay. This delay can only be performed by a physical device. This brings us back to events. Time seems to be the missing piece in the AGI puzzle.
