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Neurons unexpectedly encode information in the timing of their firing

quantamagazine.org

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Re: Neurons unexpectedly encode information in the timing of their firing

#111

Post by mikewarot »

meroes wrote: post #110
mikewarot wrote: post #109

Earlier quoted context omitted.

A lava lamp is a chaotic system. The same initial conditions, no matter how precisely measured, will not result in the same outcome, it will diverge. It is non-deterministic in the real world.

Wait, let's be clear. In the real world a lava lamp is basically impossible predict as it evolves. And that is due to chaos. So far so good. But this chaos is deterministic. Chaos means highly sensitive to initial conditions and involves nonlinearity, but it is still entirely classical and deterministic. In the real world we do not measure things precise enough to keep track of a lava lamp's deterministic evolution,…

It might be possible to predict the pattern in the lava lamp, but to do so would require cutting it off from the rest of the universe. Light, heat, convection with room air, variations in local gravity all are going to effect the flow.

Re: Neurons unexpectedly encode information in the timing of their firing

#112

Post by lobocinza »

trhway wrote: post #81
eloff wrote: post #70

Earlier quoted context omitted.

There are lots of animals that can beat us at specific neural tasks. So I don't think it's helpful to think in those terms. For example, our visual short term memory is bested by chimpanzees, at least on certain tasks used to measure it across both chimps and humans. Lowly mice likely have better olfactory capabilities than us. It wouldn't surprise me if their brains can handle some very specific things better than w…

>There are lots of animals that can beat us at specific neural tasks. anecdotally - in front of us a woman with a dog is leaving the dog park, and the dog pulls in the opposite direction that the woman tries to go, and that goes for a few seconds until the woman "Oh!, you're right, today we parked there" and follows the dog. And on 2 occasions spread in time and space i saw a racoon confidently crossing an intensive…

I saw many times straw dogs waiting for the semaphore to go green and then walking in the pedestrian crossing when most humans weren't.

Re: Neurons unexpectedly encode information in the timing of their firing

#113

Post by tsimionescu »

meroes wrote: post #110
mikewarot wrote: post #109

Earlier quoted context omitted.

A lava lamp is a chaotic system. The same initial conditions, no matter how precisely measured, will not result in the same outcome, it will diverge. It is non-deterministic in the real world.

Wait, let's be clear. In the real world a lava lamp is basically impossible predict as it evolves. And that is due to chaos. So far so good. But this chaos is deterministic. Chaos means highly sensitive to initial conditions and involves nonlinearity, but it is still entirely classical and deterministic. In the real world we do not measure things precise enough to keep track of a lava lamp's deterministic evolution,…

> a quantum computer, which Susskind says takes only 100 qubits to outperform any Turing Machine constructable ever

It's very important to understand that this only applies for a limited set of algorithms. QCs are not universal accelerators. In particular, if picking out this deterministic patterns from the apparent chaos were an NP problem, the QC would be just as slow as any other computing machine that we know so far.

You're also misunderstanding how chaotic systems work. With a chaotic system, even if you know the precise time evolution rules, you're not going to be able to predict the outcome at time T, because a tiny difference in the initial conditions, or a tiny interference from the outside world, will mean vastly different outcomes.

In fact, QCs would be particularly BAD at predicting the outcome of a chaotic system, because QCs can only give answers up to some error bound, unlike classical computers which can perform exact calculations. But the error introduced by the QC itself is probably going to compound the imprecision in the initial measurements of your chaotic system.

One final note that is important to state: the problem with predicting chaotic systems is not physical or computational, it is mathematical. You can have even simple systems whose solution can vary orders of magnitude more than a variance in the parameters. Solving such a system is easy and fast, but the solution is physically meaningless: a 0.01% error in the measurements can mean that you solution is off by a factor of 100.

Re: Neurons unexpectedly encode information in the timing of their firing

#114

Post by HarHarVeryFunny »

sdwr wrote: post #107
HarHarVeryFunny wrote: post #55

Earlier quoted context omitted.

Right, but first the "recipient" neuron needs to fire, which requires integrated synaptic inputs to cross some threshold, which requires input spikes to arrive close to the same time. This phase precession mechanism being discussed is what allows inputs arriving from different distances (i.e. with different signal travel times) to arrive close to the same time such that the recipient fires. Once it fires, then "fire…

After reading the article, I don't think that's what they're saying. My understanding is that place cells stand in for a literal location in space, and fire when they detect that location. The phase precession is how place cell A overlaps firing with place cell B when the individual is moving from location A to B, strengthening the connection (to create a mental link/memory between the two?)

The article does discuss the place cell example (observed in rats), but basically as old news. What seems to be new (at least going by the Quanta article - I don't have access to Cell) is seeing phase precession in humans, and hints of it being a more universal mechanism also used for sequence learning, etc.

In the place cell instance, it seems one effect of this mechanism might be for place cell firing to act as a predictive input for adjacent place cells (a pretty solid real-word prior - you can't be "here" unless you just came from somewhere adjacent!), and another might be to make prior-and-current place simultaneously available which could be used to learn direction of travel.

If this is a general (or at least widespread) mechanism, not limited to place cell firing, then it opens up all sort of learning possibilities by bringing together (in time) inputs that would otherwise be asynchronous.

Re: Neurons unexpectedly encode information in the timing of their firing

#115

Post by mikewarot »

mikewarot wrote: post #16
radicaldreamer wrote: post #6

Earlier quoted context omitted.

There’s a strong bias toward things that are model-able in neuroscience. The role of microtubules, for example, are mostly ignored even though they may explain the complexity of cognition displayed by relatively “simple” brains.

Fringe Tangent: It's possible those microtubules in our brains are 1 dimensional superconductors, and thus might be capable of holding Qubits. We might have quantum memory. https://arxiv.org/ftp/arxiv/papers/1812/1812.05602.pdf

If (a huge if) our memories are stored in qubits, a sufficiently strong magnetic pulse (one much larger than found in an MRI) might be able to erase our brains.

This also has interesting (morbid?) implications for how long our memories last after death.

Re: Neurons unexpectedly encode information in the timing of their firing

#116

Post by meroes »

tsimionescu wrote: post #113
meroes wrote: post #110

Earlier quoted context omitted.

Wait, let's be clear. In the real world a lava lamp is basically impossible predict as it evolves. And that is due to chaos. So far so good. But this chaos is deterministic. Chaos means highly sensitive to initial conditions and involves nonlinearity, but it is still entirely classical and deterministic. In the real world we do not measure things precise enough to keep track of a lava lamp's deterministic evolution,…

> a quantum computer, which Susskind says takes only 100 qubits to outperform any Turing Machine constructable ever It's very important to understand that this only applies for a limited set of algorithms. QCs are not universal accelerators. In particular, if picking out this deterministic patterns from the apparent chaos were an NP problem, the QC would be just as slow as any other computing machine that we know so…

If chaos is not a problem for computation, why do we always hear weather simulations needing better and better supercomputers? If simulating/computing chaos is just about getting precise enough initial measurements, then that wouldn't seem to be a need for weather simulating right?

I was thinking along the lines of running current observed data backwards to fine tuned initial conditions. That must require lots of computational power. Are we sure quantum computers and quantum algorithms wont speed this part up. That has to be somwewhat isomorphic to factoring large numbers, which I thought QC does have a potential advantage. But maybe chaos is distinct from factoring, I don't know much about how it would be modeled computationally. I realize most of the battle is getting proper initial conditions. But quantum computing is also coming at time when quantum sensing is growing. To me with quantum computing speed ups and quantum sensors, we have the two ingredients necessary to make progress on chaotic systems. Better initial conditions and better computational methods. That was my thought. Sorry for the ramble

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