jang et al 2026 draft up to discussion

Author: rcoreilly

content/axon.md

@@ -15,7 +15,7 @@ A fundamental challenge for any theoretical understanding of something as comple
 
 In this context, a primary goal in generating the extensive content here on [compcogneuro.org](https://compcogneuro.org) is to document the full extent to which the current version of Axon captures the existing scientific findings in neuroscience and cognitive psychology, so that the interested reader may form their own opinion about the extent to which the model provides an accurate picture of brain function. Feedback on any and all such issues is encouraged, using the [github discussions](https://github.com/compcogneuro/web/discussions) forum. The [github issues](https://github.com/compcogneuro/web/issues) can be used to report typos or other such "bugs", and pull requests for suggested fixes or other contributions are always welcome, and provide a way to document contributions.
 
-Ultimately, the definitive step in the scientific method is direct empirical tests of the specific predictions from the model, of which there have been a large number over the years, as documented in the relevant places herein. Perhaps the most central such test is reported in [[Jiang et al 2026]], which directly tests the [[temporal derivative]] form of [[synaptic plasticity]] that drives learning in the Axon model.
+Ultimately, the definitive step in the scientific method is direct empirical tests of the specific predictions from the model, of which there have been a large number over the years, as documented in the relevant places herein. Perhaps the most central such test is reported in [[Jang et al 2026]], which directly tests the [[temporal derivative]] form of [[synaptic plasticity]] that drives learning in the Axon model.
 
 ## Computational motivation
 
@@ -57,7 +57,7 @@ The central elements of Axon in terms of neural and computational mechanisms are
 
     The discrete spiking behavior of these neurons enables effective graded information integration over time in a way that continuous [[rate code activation]] communication does not, by allowing many different signals to be communicated over time, competing for the overall control of the network activation state as a function of the collective integration of spikes within the neurons in the network. As a result, Axon models are overall much more robust and well-behaved overall compared to their [[Leabra]] rate-code based counterparts, especially with respect to [[constraint satisfaction]] computation.
 
-* [[Error-driven learning]] based on errors computed via a [[temporal derivative]] that naturally supports [[predictive learning]], as the difference over time of network activity states representing the prediction followed by the outcome. Local [[synaptic plasticity]] based on the competition between kinases updating at different rates, i.e., the [[kinase algorithm]], naturally computes the error gradient via the temporal derivative dynamic. The result is a fully biologically plausible form of the computationally powerful [[error backpropagation]] algorithm, as shown by the [[GeneRec]] algorithm. Initial empirical support for this mechanism is reported in [[Jiang et al 2026]], in electrophysiological measurements of synaptic plasticity in a rodent preparation.
+* [[Error-driven learning]] based on errors computed via a [[temporal derivative]] that naturally supports [[predictive learning]], as the difference over time of network activity states representing the prediction followed by the outcome. Local [[synaptic plasticity]] based on the competition between kinases updating at different rates, i.e., the [[kinase algorithm]], naturally computes the error gradient via the temporal derivative dynamic. The result is a fully biologically plausible form of the computationally powerful [[error backpropagation]] algorithm, as shown by the [[GeneRec]] algorithm. Initial empirical support for this mechanism is reported in [[Jang et al 2026]], in electrophysiological measurements of synaptic plasticity in a rodent preparation.
 
     The combination of robust error-driven learning and biologically-detailed spiking neurons in Axon enables these neurons to learn to perform arbitrary computational and cognitive tasks. Furthermore, the availability of a clear computational measure of performance in terms of overall learning capability across a wide range of tasks has enabled the optimization of all the biological parameters to maximize learning performance. There is a consistent set of such parameters that generally works best across all the tasks investigated to date, and thus the additional degrees of freedom associated with these parameters are generally eliminated from consideration in constructing new models, greatly reducing the effective degrees of freedom of the model.
 
@@ -105,7 +105,7 @@ There will be many practical challenges associated with scaling up these models,
 
 We can summarize the overall approach by way of answering several key questions that one might reasonably ask in evaluating a theoretical and computational model of the human brain:
 
-1. _Is it scientifically accurate?_ The Axon mechanisms are all grounded in detailed [[neuroscience]] data, and produce known [[cognition|cognitive]] and behavioral phenomena accurately. There are no significant errors of commission in the mechanisms included: each such mechanism has solid evidence in support of it, including critically the basis for powerful error-driven learning via a [[temporal derivative]] computed by the competition between a faster LTP-promoting CaMKII pathway and a slower opposing LTD-promoting DAPK1 pathway in the [[kinase algorithm]]. An initial experimental test of this hypothesis ([[Jiang et al 2026]]) shows consistent evidence.
+1. _Is it scientifically accurate?_ The Axon mechanisms are all grounded in detailed [[neuroscience]] data, and produce known [[cognition|cognitive]] and behavioral phenomena accurately. There are no significant errors of commission in the mechanisms included: each such mechanism has solid evidence in support of it, including critically the basis for powerful error-driven learning via a [[temporal derivative]] computed by the competition between a faster LTP-promoting CaMKII pathway and a slower opposing LTD-promoting DAPK1 pathway in the [[kinase algorithm]]. An initial experimental test of this hypothesis ([[Jang et al 2026]]) shows consistent evidence.
 
 2. _Does it have a clear principled basis for effective computation?_ The principle of [[search]] through high-dimensional spaces unifies our understanding of both learning and online computation through [[optimized representations]] (via [[constraint satisfaction]] supported by [[bidirectional connectivity]]). The principal challenge is the [[curse of dimensionality]], which requires _dedicated-parallel_, _gradient-based_ search mechanisms. Most of the existing [[reinforcement learning#model-based]] reinforcement learning mechanisms do not scale well because they involve serial search of one form or another. By contrast, the [[Rubicon]] framework leverages the parallel mechanisms in Axon, along with a neuroscience-based [[computational-cognitive-neuroscience#reverse engineering]] of the results of millions of years of parallel [[evolution|evolutionary]] search to build in stronger [[bias-variance tradeoff|biases]] that shape and constrain goal-driven learning.
 

content/bidirectional-connectivity.md

@@ -9,7 +9,7 @@ Relative to most [[abstract neural network]] (ANN) models, [[Axon]] is unique in
 
 Learning is also much more difficult in the context of complex activation dynamics, and interestingly there are surprisingly impressive results from [[reservoir computing]] networks that eschew learning within bidirectionally connected networks entirely, using them instead as "reservoirs" of complex dynamical activity states from which signals can be decoded via simpler feedforward [[error-driven learning]] mechanisms.
 
-By contrast, the form of learning in [[Axon]] depends critically on bidirectional excitatory connectivity for propagating error signals throughout the network, and can tune large, complex bidirectional networks to develop effective [[predictive learning]] representations of the environment, leveraging the principle of learning based on a [[temporal derivative]]. There is now experimental evidence consistent with this form of learning in at least one specific, widely-studied pathway involving cortical pyramidal neurons and synaptic mechanisms that exist throughout the neocortex ([[Jiang et al 2026]]).
+By contrast, the form of learning in [[Axon]] depends critically on bidirectional excitatory connectivity for propagating error signals throughout the network, and can tune large, complex bidirectional networks to develop effective [[predictive learning]] representations of the environment, leveraging the principle of learning based on a [[temporal derivative]]. There is now experimental evidence consistent with this form of learning in at least one specific, widely-studied pathway involving cortical pyramidal neurons and synaptic mechanisms that exist throughout the neocortex ([[Jang et al 2026]]).
 
 From a computational cost perspective, bidirectional connectivity is very expensive because it doubles the number of synaptic connections, and requires roughly 200x iterations through the network to process a single input. This significantly limits the ability to scale up the models, which has been the primary driver of impressive computational performance in current feedforward ANN models. Nevertheless, as parallel compute hardware continues to improve, this limitation will hopefully be overcome (and the current version of [[Axon]] runs efficiently on any GPU, using WebGPU so it works through the browser too). For the time being, the models do focus more on capturing the principles rather than the kinds of raw performance improvements that come with scaling (see [[bias-variance tradeoff]] for more discussion).