5 ms·

This is nonsense. Evidence collected over the last 40 years have detailed how memory is encoded by synaptic weights, but the premise... "there’s no obvious way

This is nonsense. Evidence collected over the last 40 years have detailed how memory is encoded by synaptic weights, but the premise... "there’s no obvious way for all that sensory data to be captured in synapses as long term memories" ...just ignores these findings. The model described in the article on the other hand, has never been observed. At the least, if you are going to propose an mRNA hypothesis don't use "T" as one of your RNA bases.

AstralStorm 6y ago
What is the physical realisation of the synaptic weight? (Here's trouble, we do not know. It's not in just connectivity or myelination.)
- jrussino 6y ago
  See my response to your sibling comment; clearly we still have a lot to learn, but there is already some understanding of the biological mechanisms of synaptic plasticity (which I think is what the parent comment is referring to as "synaptic weights"). https://en.wikipedia.org/wiki/Synaptic_plasticity https://en.wikipedia.org/wiki/Synaptic_plasticity
- subroutine 6y ago
  The physical realization is the number of postsynaptic receptors. In terms of fast excitatory transmission it is the quantity of AMPA receptors. You can potentiate or depress synapses by controlling the amount of these receptors. This has been demonstrated in vivo, and in cell culture. We are at the point where we can manipulate the strength of individual synapses. Here is a nice review article: https://royalsocietypublishing.org/doi/10.1098/rstb.2013.0136?url_ver=Z39.88-2003 https://royalsocietypublishing.org/doi/10.1098/rstb.2013.013...
titzer 6y ago
> have detailed how memory is encoded by synaptic weights Synaptic weights is our computational model of neural networks that seems to work pretty well. But this is a model. No one has discovered the biological mechanism by which synapses are "weighted". The actual computation going on in a cell is totally based on chemistry. There are a lot of different specialized networks in human and animal brains that don't necessarily translate easily to "weighted synaptic networks" all that well. For example, networks that process audio information have been shown to recognize individual frequencies (in fact, they pretty much solely do this!) Such networks seem to be primarily based on the firing rate across a synapse. For such networks is seems more reasonable to think of neurons as signal processors whose internal computation is more based on the time domain, e.g. some kind of Fourier transform-equivalent. In short, I think you should put less weight (pun intended) in our computational model of neurons (link weights) and look more at biology.
- jrussino 6y ago
  It has been over a decade since I studied neurobiology, but from what I recall we do have at least some understanding of how synapses become "weighted". For example: long-term-potentiation, the idea that "neurons that fire together wire together" https://en.wikipedia.org/wiki/Long-term_potentiation https://en.wikipedia.org/wiki/Long-term_potentiation https://www.khanacademy.org/test-prep/mcat/behavior/learning-slug/v/long-term-potentiation-and-synaptic-plasticity https://www.khanacademy.org/test-prep/mcat/behavior/learning...
  - netizen-9748 6y ago
    LTP is only one mechanism. Neurons vary their responses in multiple ways, usually some form of receptor protein modification. But again, this is one mechanism of many.
- wombatmobile 6y ago
  Perhaps mRNA are performing the synaptic weighting. After gene expression, mRNA's are left floating as debris in the cytoplasm, until they are cleaned up and expelled. If the gene expression continues chronically, the same mRNA sequences will populate the same cells, transiently, for as long as the gene expression recurs. What if the mass of the mRNA sequences influences the electrical conductivity of the cell e.g. functions as a form of antenna. In the aggregate, cells with mRNA populations e.g. neutrons active in particular thought patterns, might have stronger antenna, or differentiated electrical characteristics, and hence influence thought more or less in the network, for the duration of the gene expression.
  - subroutine 6y ago
    Sure, debris mRNA may affect neuron electrical potentials. But this would be an extremely wonky way for neurons to regulate electrochemical propagation. One that has not been observed irl, afaik. However we know a ton about how neurons directly regulate electrical potentiation by modifying the expression of postsynaptic neurotransmitter receptors. The mechanism is simple... the more receptors in a given synapse, the greater the dendritic membrane will depolarize from each upstream signal (signal being each synaptic release of a neurotransmitter vesicle by the upstream axon). Electrophysiology instruments are so sensitive now-days that we can detect, in a quantized way, the release of single synaptic vesicles. We can then do things to increase synaptic expression of neurotransmitter receptors, and find that each vesicle release results in a greater depolarization of the downstream neuron. Long story short, the quantity of neurotransmitter receptors at postsynaptic membranes is a physical correlate (probably the primary physical correlate) of synaptic weights in artificial neural networks.

AstralStorm 6y ago

What is the physical realisation of the synaptic weight? (Here's trouble, we do not know. It's not in just connectivity or myelination.)

jrussino 6y ago
See my response to your sibling comment; clearly we still have a lot to learn, but there is already some understanding of the biological mechanisms of synaptic plasticity (which I think is what the parent comment is referring to as "synaptic weights"). https://en.wikipedia.org/wiki/Synaptic_plasticity https://en.wikipedia.org/wiki/Synaptic_plasticity
subroutine 6y ago
The physical realization is the number of postsynaptic receptors. In terms of fast excitatory transmission it is the quantity of AMPA receptors. You can potentiate or depress synapses by controlling the amount of these receptors. This has been demonstrated in vivo, and in cell culture. We are at the point where we can manipulate the strength of individual synapses. Here is a nice review article: https://royalsocietypublishing.org/doi/10.1098/rstb.2013.0136?url_ver=Z39.88-2003 https://royalsocietypublishing.org/doi/10.1098/rstb.2013.013...

titzer 6y ago

> have detailed how memory is encoded by synaptic weights Synaptic weights is our computational model of neural networks that seems to work pretty well. But this is a model. No one has discovered the biological mechanism by which synapses are "weighted". The actual computation going on in a cell is totally based on chemistry. There are a lot of different specialized networks in human and animal brains that don't necessarily translate easily to "weighted synaptic networks" all that well. For example, networks that process audio information have been shown to recognize individual frequencies (in fact, they pretty much solely do this!) Such networks seem to be primarily based on the firing rate across a synapse. For such networks is seems more reasonable to think of neurons as signal processors whose internal computation is more based on the time domain, e.g. some kind of Fourier transform-equivalent. In short, I think you should put less weight (pun intended) in our computational model of neurons (link weights) and look more at biology.

jrussino 6y ago
It has been over a decade since I studied neurobiology, but from what I recall we do have at least some understanding of how synapses become "weighted". For example: long-term-potentiation, the idea that "neurons that fire together wire together" https://en.wikipedia.org/wiki/Long-term_potentiation https://en.wikipedia.org/wiki/Long-term_potentiation https://www.khanacademy.org/test-prep/mcat/behavior/learning-slug/v/long-term-potentiation-and-synaptic-plasticity https://www.khanacademy.org/test-prep/mcat/behavior/learning...
- netizen-9748 6y ago
  LTP is only one mechanism. Neurons vary their responses in multiple ways, usually some form of receptor protein modification. But again, this is one mechanism of many.
wombatmobile 6y ago
Perhaps mRNA are performing the synaptic weighting. After gene expression, mRNA's are left floating as debris in the cytoplasm, until they are cleaned up and expelled. If the gene expression continues chronically, the same mRNA sequences will populate the same cells, transiently, for as long as the gene expression recurs. What if the mass of the mRNA sequences influences the electrical conductivity of the cell e.g. functions as a form of antenna. In the aggregate, cells with mRNA populations e.g. neutrons active in particular thought patterns, might have stronger antenna, or differentiated electrical characteristics, and hence influence thought more or less in the network, for the duration of the gene expression.
- subroutine 6y ago
  Sure, debris mRNA may affect neuron electrical potentials. But this would be an extremely wonky way for neurons to regulate electrochemical propagation. One that has not been observed irl, afaik. However we know a ton about how neurons directly regulate electrical potentiation by modifying the expression of postsynaptic neurotransmitter receptors. The mechanism is simple... the more receptors in a given synapse, the greater the dendritic membrane will depolarize from each upstream signal (signal being each synaptic release of a neurotransmitter vesicle by the upstream axon). Electrophysiology instruments are so sensitive now-days that we can detect, in a quantized way, the release of single synaptic vesicles. We can then do things to increase synaptic expression of neurotransmitter receptors, and find that each vesicle release results in a greater depolarization of the downstream neuron. Long story short, the quantity of neurotransmitter receptors at postsynaptic membranes is a physical correlate (probably the primary physical correlate) of synaptic weights in artificial neural networks.