In your 2010 Nature paper, you show that image recognition in human brains as detected by 4 electrodes is correlated with spike direction but not spike rate or inter spike intervals. This is in contrast to current neural models such as in LLMs that model spike rate as the relevant signal. This raises some questions.
1. What aspect of AI are we not yet capable of solving using neural networks modeling spiking rate? 2. Why can't we model spike direction on a computer? Why do we need specialized hardware? 3. Do you have any simulations of pattern recognition or some other problems relevant to AI that are improved by modeling spike direction instead of spike rate? 4. How does spike direction even work? My understanding is that spikes always travel from the neuron cell body along the axon to the synapses. Looking at the data in your paper, you are measuring voltages around 0.1 mV in 1 ms spikes and detecting phase shifts around 0.5 ms between the 4 electrodes. Action potentials between the inside and outside of the cell are actually about 100 mV, but smaller outside the cell, of course. It seems to me that the electrodes are actually picking up signals from several surrounding neurons and the "direction" is actually measuring spikes from different input neurons at the dendrite. I am aware that stereoscopic sound perception up to 1500 Hz depends on spike timing from each ear to communicate relative phase information. But this would not be hard to model in a computer. LLMs pass the Turing test using nothing more than text prediction. Is there something else we need? You seem to make this distinction between intelligence and modeling intelligence, as if there was an important difference, like between flying and modeling flight. I'm not clear on what problem you are trying to solve, what your proposed solution is, how it would work any better, or what you would measure to know that it worked. Are you saying we need different hardware for consciousness or something? I didn't read your Medium article because it's pay walled. -- Matt Mahoney, [email protected] On Mon, Aug 25, 2025, 2:01 PM Dorian Aur <[email protected]> wrote: > Thank you Matt, valid and important point. > > The architecture goes beyond replacing RAM with memristors - it introduces > a fundamentally different physical substrate and* computational model*. > Unlike traditional digital neural networks, which simulate activity > symbolically on von Neumann machines, *EDI <https://bit.ly/45JPjsg>* is > grounded in continuous spatial dynamics driven by real ionic and charge > interactions akin to what occurs in biological neurons, see this paper > https://www.nature.com/articles/npre.2010.5345.2 > > The distinction is not just in the hardware, it is in the *computational > paradigm*: information is processed and stored in the same medium through > dynamic field interactions, not separated across memory and processing > units. This allows true material-based learning and self-organization, > which digital LLMs do not display. > Regarding simulations, initial models are currently in development. > However, the goal is not to simulate EDI in its entirety, as many of its > properties cannot be meaningfully captured in a digital environment. EDI > fundamentally departs from conventional AI architectures. Muchlike you > can't simulate flight in a way that generates real lift, some properties of > EDI, e.g. emergent spatiotemporal behavior, only manifest in physical > substrates - a new class of intelligence *from the physics of the system > itself.* > By using LLMs initially to pre-instantiate the early stages of > intelligence within an EDI framework (see papers's paragraph) , you can > train the system efficiently.If you replace the LLM's memory architecture > with memristors, you begin to approach the architecture we envisioned in > the manuscript Electrodynamic Intelligence (EDI). Once EDI develops its > own internal adaptive dynamics, grounded in physical memory, coupling, and > energy-efficient computation, the need for LLMs themselves diminishes. > > As biologically fragile organisms, we face significant limitations when it > comes to colonizing Mars or other planetary environments. To thrive beyond > Earth, we’ll need systems that can adapt, and build autonomously in extreme > conditions. We need an *Optimus 5.0 *equipped with an EDI brain, vital > for off-world infrastructure, habitat construction, and autonomous > problem-solving - today feels almost alien intelligence. *Wouldn't > that feel like having an LLM in the 1940s? * > > With targeted investment from both public and private sectors, a > functional *EDI prototype* could realistically be developed within 2–3 > years, maybe less given the current pace of innovation > > - --Dorian Aur > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Ta9b77fda597cc07a-M880de667a71c285f8a396de2 Delivery options: https://agi.topicbox.com/groups/agi/subscription
