> <i>Provided a connectome for a whole brain, it’s possible to simulate the model forward, using single-neuron models of varying biological plausibility, from linear-integrate-and-fire to detailed multi-compartment models with Hodgkin-Huxley dynamics. Simulate the body and environment, in addition, and you have yourself a whole-brain-body-environment simulation. We’re not there yet, but one could imagine this could happen in the not-too-distant future, especially for smaller organisms</i><p>Carl Rogers' Nineteen Propositions speak of an "Objective phenomenological field theory", wherein <i>a</i> self is only interpretable in context to the fields; which we now understand are typically nonlinear, and literally n-way nonlocally entangleable.<p>Is brain state completely describeable without modeling
<i>Representational drift</i>; the field around the brain created by spreading activation that FWIU presumably nonlinearly affects connectome dynamics?<p>(Plenoptic function, Wave field recording, stochastic data carving from the aether, reflections in raindrops, mirrors in spacetime, NIR light stimulates neuronal growth, GA with perturbation and tissue ethics, brain2computer, brain2brain, BRIAN)<p>Hypothesis: There's not enough latent information in training data to infer or clone human cognitive processes without stream of consciousness training data.<p>From <a href="https://news.ycombinator.com/item?id=32819221">https://news.ycombinator.com/item?id=32819221</a> :<p>> <a href="http://www.brain-score.org/" rel="nofollow">http://www.brain-score.org/</a><p>>> <i>Is it necessary to simulate the quantum chemistry of a biological neural network in order to functionally approximate a BNN with an ANN?</i><p>>> <i>"Neurons Are Fickle. Electric Fields Are More Reliable for Information" (2022)</i><p>[>>> <i>A new study suggests that electric fields may represent information held in working memory, allowing the brain to overcome “representational drift,” or the inconsistent participation of individual neurons</i>]<p>From <a href="https://news.ycombinator.com/item?id=35877402#35886145">https://news.ycombinator.com/item?id=35877402#35886145</a> :<p>> <i>Representational drift: Emerging theories for continual learning and experimental future directions" (2022) <a href="https://www.sciencedirect.com/science/article/pii/S0959438822001039" rel="nofollow">https://www.sciencedirect.com/science/article/pii/S095943882...</a> :<p>> </i>Recent work has revealed that the neural activity patterns correlated with sensation, cognition, and action often are not stable and instead undergo large scale changes over days and weeks—a phenomenon called representational drift. Here, we highlight recent observations of drift, how drift is unlikely to be explained by experimental confounds, and how the brain can likely compensate for drift to allow stable computation. We propose that drift might have important roles in neural computation to allow continual learning, both for separating and relating memories that occur at distinct times. Finally, we present an outlook on future experimental directions that are needed to further characterize drift and to test emerging theories for drift's role in computation.*<p>How does a connectomic description of the brain differ from a wave field recording of a brain; and given "Representational drift" can a connectomic model be sufficient or stable in functional localization?