a0111z
A thought experiment: Imagine that each node (an oscillating group of cells) was processing the whole of consciousness (or part of it) but at a different scale or represents a different perspective,
So that when you combine all the participating nodes you get a sea of overlapping perspectives.
Like each node is part of a light field, and where light field display intersects the image is joined up,
It is similar in concept, this thought experiment, to every node representing a pixel in a Light Field Labs display. A display where every pixel has a spherical representation of whole image pattern, from every angle (360 degrees) but representing the view through that position in space, through some position in space.
The potential consequence of that is that reality inside our heads closely matches actual reality. What I mean is that it's like the volume of your head is filled up volumetrically with representations of the volume of space around you, thanks to your incoming senses, your neural pathways, your neural arrays sensing & transmitting patterns in sequences to other patterns, and all of your mental patterns being bound together by oscillation.
Originally this note was about how all the little tempo-spatial phase changes in each area of the brain, from the synapse/dendrite scale to the cortical column scale is creating a different part of the tomographic lightfield rendering in the mind.
Each node, or neural circuit is part of the holographic tomographic computational rendering that is the mind.
I think what I was arguing here, but didn't finish, is that the phase changes at a higher levels, layers, or further back arrays, result in features that scale, and abstractions & concepts that scale beyond features. In a neural network information telescopes from microlensing at the sensory inputs to macrolensing at the hierachical peak, and then the feedback from the hierarchical peak drives behavior.
"(...)the timing and spatial patterns of oscillations in the fronto-auditory network of vocalizing bats (Carollia perspicillata) predict the purpose of vocalization: echolocation or communication." "Transfer entropy analyses" what is transfer entropy analysis lol? "revealed predominant top-down (frontal-to-auditory cortex) information flow during spontaneous activity and pre-vocal periods." "frontal-auditory field (FAF;" "auditory cortex (AC" "Directed connectivity in the FAF-AC network varied dynamically according to whether animals produced communication or echolocation calls, and to the timing relative to vocal onset." "In previous work, we reported low-frequency (1–12 Hz) phase coherence in the FAF-AC circuit during spontaneous activity, with emergence of γ-band (>25 Hz) coherence at the onset of external acoustic stimulation" "Causal interactions were quantified using directed phase transfer entropy (dPTE), a metric that measures the degree of preferential information transfer between signals based on phase time series" Figure 2 "dPTE matrices were used as adjacency matrices for directed graphs, which characterized patterns of directional information flow in the FAF-AC network (Fig. 2)." "Within FAF and during pre-vocal echolocation periods, information flowed predominantly from deep to superficial layers in δ and β1 frequencies (Fig. 2b), and in the opposite direction for α-LFPs" Hmm... ", also during no-voc periods" "Consistent with previous reports4,14,16, we show that neural activity in the frontal cortex predicts vocal outputs. Taken together, the data from this and previous work suggest that oscillations in frontal regions may be instrumental for vocal production. From our perspective, the above is further supported by call-type specific, pre-vocal LFP spectral dynamics and information transfer patterns in the FAF-AC network. The relationship between oscillations and vocal production remains, nevertheless, correlational: our results do not allow to rigorously assert a causal role of LFPs for the initiation or planning of vocalizations." See Figure 6 https://www.nature.com/articles/s41467-022-31230-6
"Uncovering features of synapses in primary visual cortex through contrastive representation learning"
https://www.biorxiv.org/content/10.1101/2022.06.07.495207v1
https://www.biorxiv.org/content/10.1101/2022.06.09.495367v1
So my book notes and my book is intended to talk about how activity in the synapse scales up to the whole brain, that the picture of a memory is magnified from a single neuron because it alters the timing of the firing of the downstream neurons that the single neuron impacts by inhibiting nearby neurons, (the neuron that fires inhibits nearby neurons to prevent them from firing, the folks at Numenta talk about this in their research) but what I'm saying is that this is like a photocopier effect, essentially magnifying that single memory to the entire cortical column because of the area that is inhibited represents the pattern that neuron represents with its connections.
That creates a phase rendering in the oscillation group of neurons (that is acting like a single sensor/transmitter like a group of firelies referencing the book Sync).
So the phase changes are constrastive, and that points to how while there is for example a powerband of alpha oscillations for example, within that power band there are multiple alpha frequencies, and the contrastive differences amount to the rendering that makes up the information of your mind, allowing the self to perceive reality and at the same time to be rendered by reality.
This paper is not related to that
"Defective synaptic plasticity in a model of Coffin-Lowry Syndrome is rescued by simultaneously targeting PKA and MAPK pathways"
https://www.biorxiv.org/content/10.1101/2022.06.07.495143v1
The ground of being as the tonic oscillation of unconscious expectation based awareness
This next link is an example of deep learning applied to medical imaging, what is interesting is the idea that a synapse which is gap junction between transmitters & receptors can be thought of as a fractal of a neuron, with inhibitory & excitatory neurons having inhibitory & excitatory synapses on them, and functionally speaking a sodium receptor inside a synapse is like an excitatory receptor, and a potassium receptor is like an inhibitory receptor. So we have two fractals levels here. That's before you dive into what is happening inside the receptor. Biology, and reality is so weird. So weird it's actually a functional fractal, not always a geometric fractal, but functionally a fractal, with the same operations happening at different scales. Excitation & inhibition, it's this like positive & negative charges in a functional sense?
"SynapseCLR, a self-supervised contrastive representation learning method for 3D electron microscopy (EM) data, and use the method to extract feature representations of synapses from a 3D EM dataset from mouse visual cortex." Uncovering features of synapses in primary visual cortex through contrastive representation learning "we show that excitatory vs. inhibitory neuronal cell types can be assigned to individual synapses and highly truncated neurites with accuracy exceeding 99.8%, making this population accessible to connectomics analysis. Finally, we present a data-driven and unsupervised study of the manifold of synaptic structural variation, revealing its intrinsic axes of variation and showing that synapse structure is also strongly correlated with inhibitory neuronal subtypes." https://www.biorxiv.org/content/10.1101/2022.06.07.495207v1
and its like the mnemonic paper "The mnemonic basis of subjective experience" "we argue that these subjective qualities can be understood in terms of their similarity to other experiences" https://www.nature.com/articles/s44159-022-00068-6
but its also like the description of how human beings see colors, as relative contrasts, or differences meaning that in human perception patterns are distinct from some baseline pattern new sensory inputs are contrasted with the tonic oscillations set by past sensory inputs
On a larger scale we can make an argument that the differential firing rates of different large scale clustors of cells provides macro scale contrasts between microscale learned representations so that the organism can differentiate between concepts in its internal representations, or mental imagery, or qualia, or mind. "Differential processing of decision information in subregions of rodent medial prefrontal cortex" https://www.biorxiv.org/content/10.1101/2022.08.04.502840v1
NAPOT neural array projection OSCILLATORY tomography