Engrams

Trying to visualize the thoughts and memories in the brain.

Note: This is an excerpt/draft on a book section I've been working on part 2 of Conscious Artificial Intelligence. Why is this important ? Current AIs borrow and combine elements from Neuroscience, Statistics and other fields, the end result are specialized tools and systems that while effective at certain tasks generalize poorly, our human trait in contrast is that we generalize easily, understanding engrams then could give us a better chance at creating new AIs. There is also value in answering the questions of how, where and when we store information.

The big picture

High level schematic of engrams (the diagonally shaded squares )three instances of them... As you experience the environment a succession of engrams are perceived by your receptors and cortex,  we generally perceive the latest influx (#5 in the diagram) as our current reality in time (the black arrow), some of the information is stored as memories which you can later retrieve (this process can be both conscious and unconscious). A third instance are ideas or complex thoughts believed to be composed of combinations of engrams or engram fragments, note that these are not the only ones, as cognitive tasks increase in complexity an unknown number of engrams might be recruited and formed ( see cognitive neuroscience for an ever growing list ).

Simple Engrams

Some finer points...our artificial neural network (ANN) is a 60x60 (3,600 neurons) grid where light is registered as white pixels or 0s and darker areas as black pixels or 1s (the video feed is thresholded to only allow black and white), it is not equivalent to a retina mainly because it lacks resolution, doesn't deal with color or even grayscale/brightness, additionally encoding of visual information is not done through zeros and ones but through action potentials (think sputtering sparks without the actual sparks) which code for diverse information, so both light and dark can be encoded as repeating patterns of activity (or spike trains). This point will be clearer later.
That’s just me waving a camera in front of my laptop and this page, notice how noisy the images are, something that biological neurons share, so I’ll leave the noise in for the computer vision examples for now.

Neural Maps

You don’t usually notice the negative space but the information is there if you decide to pay attention to it.
Some but not all the maps we possess, if you stare at any one you will activate that map(s) and possibly some image after effects.

Maps in more detail

Not unlike sweeping a kernel in computer vision (see convolutions)
Notes/Explanation: Here we are recreating a map for vertical lines surrounded by white space with a 3x3 shape, in array/list format for the code savvy:template = [[0, 1, 0],
[0, 1, 0],
[0, 1, 0]]
The middle image is downsampled and is not part of the map but I included it so we can better understand what's happening, when a vertical line 3 big pixels tall is detected (in red) a corresponding neuron is activated in the Neuron Map (in golden/yellow ), so when six receptor neurons are activated (the template in green ), only one in the corresponding map is, just like the previous example but with a real stimulus and in real time.
I swear I am not trying to hypnotize you, rotating the card helps see the process in real time, for instance notice how the maps of neurons all share a certain quadrant and fire more vigorously ( by amount ).
A note on resolution: while these examples hopefully work to convey the concept of maps, in reality things are much more complex since we don't have an uniform distribution of receptors, there is a greater density around the center of your visual field and your eyes dart or jump around scanning various regions of an image in higher resolution, sometimes mediated by what you are attending to. If you focus on this point >> . << you'll notice that you can't focus on the first words in this block of text and there is less acuity the farther out things are, so in some sense your vision is like a narrow flashlight that can only illuminate a fraction of the environment at any time.

Integration and engrams

Here an asterisk like stimulus is perceived by receptors, divided in the map layer and later integrated in the association layer, about 32 Neurons connect (or synapse) with just 8 in the map layer (some connections shown in dotted lines) and finally into 1 (one) neuron in the association/integration layer (symbolized by black arrows), there are significant savings in the number of neurons, but at the cost of increasing connections.
Notes/Explanation: The top right map (in blue) is the sum of the previous maps, once more this visual is included to help us make sense of the arrangement, but has no biological counterpart.The association layer is the bottom right grid (lighter grey) and the encoding neuron is shown in dark orange (when it is firing).How we encode is worthy of a longer discussion, here I am simplifying the encoding rules by telling the association layer to fire a single neuron when roughly half the maps are active, in other words there is a threshold of things in the environment that fit these maps and that in turn this cell deems worthy enough to fire.If you've heard about grandma or famous artist neurons, this ( the single orange neuron) is a similar concept and digital analog.
Notes/Explanation:
To retrieve (or represent) a previously encoded stimuli, the order or flow of information is reversed, starting from the association layer(s) a neuron or group of neurons gets activated and in turn the corresponding maps get activated next, the result is that the original stimulus is rebuilt or reconstituted.
--- * ---Of note here is the location of the representation layer, where does it reside ?If stimuli enter through receptors are these receptors responsible for recreating the stimulus or is the representation layer located somewhere else ?It's still unclear but I believe the answer might be both. There are almost as many connections going downstream than upstream in the visual system (and other systems), REM sleep is considered to "replay" stimuli (at least partially) at the receptor level or close by, yet higher cognitive processes like working memory can still be performed after losing primary receptors hinting at higher cortical areas as the location, there could also be a combination of both or even more representation layers, we still don't know.In any case for our discussion it is sufficient to acknowledge that there is an internal representation layer (or layers).
Extending the previous example here we are storing the stimulus once detected, this is done by replaying the association neuron (in black) and the corresponding maps (in white), note there is overlap between active and stored maps ( something that also happens in real short term memory and can cause interference ).More simplifications: Neurons in the brain are for the most part not on/off switches, rather they spike, so a more accurate model would have neurons active at intervals ( or firing ). In the above example just imagine the white and black neurons are blinking.And lastly, this type of memory as represented is not realistic, short term memory decays, the blinking would slow down first and then eventually stop, theres are other ways of encoding by firing rate or quantity for instance.

Engrams

A fine mess, you can imagine how densely connected the brain gets, the multitude of connections are believed to be achieved by dendritic spines in the brain which are also plastic (ie can change) and do so in real time (seconds to minutes).

Engrams as the basic unit of cognition and generalization.

Here we have 3 stimuli and the corresponding mapping and association layers, the first stimulus a red square uses the same spatial mapping we've been using, sound is a bit harder to encode since there are time, frequency and loudness components, but to keep things simple here the map just encodes frequency, the third is a touch stimulus, run your finger over your arm hair, somewhere in your somatosensory cortex a row of neurons (the map) will become active.Of note also is the parallel nature of all these maps, the 3 stimuli can happen simultaneously.
A basic example of how a combination of engrams (text and images) could be combined into a richer association, since the connections here go both ways any of the stimulus presented can elicit the other associations. As for the how engrams integrate into new ideas, I'll leave you with the following quote :

“There is no such thing as a new idea. It is impossible. We simply take a lot of old ideas and put them into a sort of mental kaleidoscope. We give them a turn and they make new and curious combinations. We keep on turning and making new combinations indefinitely; but they are the same old pieces of colored glass that have been in use through all the ages.”

Mark Twain

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AI, Software Developer, Designer : www.k3no.com

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