3 ms·

> Computers are designed to interpret or translate the messy analog world where nothing is exactly 1 or 0, into a precise, deterministic digital world where eve

> Computers are designed to interpret or translate the messy analog world where nothing is exactly 1 or 0, into a precise, deterministic digital world where everything is either 1 or 0. And I think it's highly likely that the brain does not work in this manner whatsoever But neurons totally do work by encoding information with discrete signals. https://en.wikipedia.org/wiki/Action_potential#Process_in_a_typical_neuron https://en.wikipedia.org/wiki/Action_potential#Process_in_a_... Action potentials are discrete events, and just like hi or lo in a silicon computer actually correspond to a range of voltages, the exact voltage-vs-time curve of an action potential likely has considerable flexibility.

jryan49 11y ago
More directly: https://en.wikipedia.org/wiki/Action_potential#.22All-or-none.22_principle https://en.wikipedia.org/wiki/Action_potential#.22All-or-non...
- iofj 11y ago
  The brain is neither analog nor digital. The brain encodes information sent between cells as pulses. The timing of these pulses is where the information is encoded. As such it is transmitting a fourier transform of the signal it is transmitting between the cells (but with different basis functions, not cosines). When a pulse is received, this will result in a sudden jump of the action potential, and then slowly die down. If a second pulse comes while the first is still in action it gets multiplied by the current value of (this particular) input potential. Rapid pulsing (3-4 ms between pulses) means something along the lines of "multiply membrane potential by 10 for every pulse", whereas a pulse width of ~350ms means "keep the membrane potential constant". Any one of these pulses may push a neuron "over the edge" and have it transmit a pulse of it's own (or a series of pulses as pulsing the axon lowers the membrane potential by a factor. This may or may not cause it to go below the trigger level, and so sometimes the axon needs to pulse multiple times to get the membrane potential back below the trigger). This is a beautiful way to encode simplifications of analog signals. It's log scale in the sense that 2 rapid pulses encode that the function value should be an order of magnitude larger than 2 slow pulses. It is extremely sparse (pulsing speeds rarely exceed 1hz, and only for short times) so it is quite energy efficient. Your brain quite literally does nothing at all unless there's actual information to process. I find it a bit misleading that wikipedia says that the frequency of pulses encodes the info : it's true, but makes one think this frequency is constant, when in fact it is changing rapidly. Does the word frequency really make sense when any 2 measurements of pulse frequency will be different ? Normally one would call pulses digital, because they are 1 or 0. Values in between are interpreted as being either 1 or 0, definitely not analog. But the time spacing between the pulses is what matters, and this is measured to astonishing accuracy by a chemical reaction. A measurement like that, one would normally call analog. So it doesn't really match either signal type ... It is very different from any analog or digital signal I know about. One might say your neurons contain an encoded, but uncompressed mp3 of the signal they're transferring between neuron membranes : it's transmitting in the frequency domain, but without the compression that reduces the zeroes, so it's sending very long series of zeroes, interspersed with the very occasional one. This works well because transmitting a zero is done by doing nothing at all.
cinquemb 11y ago
Though, not all neuroscientists are on the same page nor agree with the claims some people want to make… "The frequency of action potentials (APs) generated by neurons is correlated with different events; however, it does not mean that such events are ‘encoded’ in the brain using the firing rate or any other ‘temporal code ’ (e.g. interspike interval)… …Simultaneous firing of action potentials (synchrony) in a neuronal ensemble increases the interaction of many- body systems and implicitly boosts the computational power [7]. The well accepted model of ‘activation function’ that describes artificial neurons simulated on Turing Machines does not approximate the process of ‘solving equations’. Therefore, the popular claim that current artificial neurons and neural networks are ‘realistic’ models of their biological counterparts is misconstrued and untrue."[0] [0] http://arxiv.org/ftp/arxiv/papers/1210/1210.1983.pdf http://arxiv.org/ftp/arxiv/papers/1210/1210.1983.pdf
- themgt 11y ago
  Wow, fantastic paper: However, the relevance of Turing model is questioned even in case of present-day computing [33] [34]. Indeed, any computing machine that follows a Turing model would be highly inefficient to simulate the activity of biological neurons and experience an increased slowdown. Since the super-Turing computing power of the brain has its origins in these ‘strong’ interactions that occur inside neurons, current models have missed the most important part. Simply, Nature doesn’t care if the N-body problem has analytical solutions [36] or can be simulated in real time on a Turing machine [37]. ... While previous models have attempted to represent Hamiltonians using Turing machines [35] the paper [1] shows that the Hamiltonian model of interaction can represent itself a far more powerful model of computation. Turing made an important step forward; however, there is no need to limit natural models of computation to Turing models. In this sense, the new framework of computation using interaction is universal in nature and provides a more general description of computation than the formal Turing model. In other words God was unaware of Turing's work and has put forward a better model for physical computation in the brain.
  - cinquemb 11y ago
    I thought it was fantastic as well (because you know a theory that tries to incorporate our understanding of matter and energy [which the brain is a subset of] in a field with a lot of hand-waving going on, shouldn't be that radical…), though for most neuroscientists it is out of reach because for the most part, have little to no understanding of electromagnetism (let's set aside the QED versions of Maxwell's equations) from my experience, which is quite ironic considering the tools that are in heavy use… The amount of money being wasted trying to make special FMRI's and custom EEG caps is lost on me when attempting to solve equations along variable boundary conditions seems like it should come first… Here's the paper the author wrote which that paper is branched from: http://www.researchgate.net/profile/Dorian_Aur/publication/202222729_Dr/links/0d1c84f6f4b8a37018000000.pdf http://www.researchgate.net/profile/Dorian_Aur/publication/2...

jryan49 11y ago

More directly: https://en.wikipedia.org/wiki/Action_potential#.22All-or-none.22_principle https://en.wikipedia.org/wiki/Action_potential#.22All-or-non...

iofj 11y ago
The brain is neither analog nor digital. The brain encodes information sent between cells as pulses. The timing of these pulses is where the information is encoded. As such it is transmitting a fourier transform of the signal it is transmitting between the cells (but with different basis functions, not cosines). When a pulse is received, this will result in a sudden jump of the action potential, and then slowly die down. If a second pulse comes while the first is still in action it gets multiplied by the current value of (this particular) input potential. Rapid pulsing (3-4 ms between pulses) means something along the lines of "multiply membrane potential by 10 for every pulse", whereas a pulse width of ~350ms means "keep the membrane potential constant". Any one of these pulses may push a neuron "over the edge" and have it transmit a pulse of it's own (or a series of pulses as pulsing the axon lowers the membrane potential by a factor. This may or may not cause it to go below the trigger level, and so sometimes the axon needs to pulse multiple times to get the membrane potential back below the trigger). This is a beautiful way to encode simplifications of analog signals. It's log scale in the sense that 2 rapid pulses encode that the function value should be an order of magnitude larger than 2 slow pulses. It is extremely sparse (pulsing speeds rarely exceed 1hz, and only for short times) so it is quite energy efficient. Your brain quite literally does nothing at all unless there's actual information to process. I find it a bit misleading that wikipedia says that the frequency of pulses encodes the info : it's true, but makes one think this frequency is constant, when in fact it is changing rapidly. Does the word frequency really make sense when any 2 measurements of pulse frequency will be different ? Normally one would call pulses digital, because they are 1 or 0. Values in between are interpreted as being either 1 or 0, definitely not analog. But the time spacing between the pulses is what matters, and this is measured to astonishing accuracy by a chemical reaction. A measurement like that, one would normally call analog. So it doesn't really match either signal type ... It is very different from any analog or digital signal I know about. One might say your neurons contain an encoded, but uncompressed mp3 of the signal they're transferring between neuron membranes : it's transmitting in the frequency domain, but without the compression that reduces the zeroes, so it's sending very long series of zeroes, interspersed with the very occasional one. This works well because transmitting a zero is done by doing nothing at all.

cinquemb 11y ago

Though, not all neuroscientists are on the same page nor agree with the claims some people want to make… "The frequency of action potentials (APs) generated by neurons is correlated with different events; however, it does not mean that such events are ‘encoded’ in the brain using the firing rate or any other ‘temporal code ’ (e.g. interspike interval)… …Simultaneous firing of action potentials (synchrony) in a neuronal ensemble increases the interaction of many- body systems and implicitly boosts the computational power [7]. The well accepted model of ‘activation function’ that describes artificial neurons simulated on Turing Machines does not approximate the process of ‘solving equations’. Therefore, the popular claim that current artificial neurons and neural networks are ‘realistic’ models of their biological counterparts is misconstrued and untrue."[0] [0] http://arxiv.org/ftp/arxiv/papers/1210/1210.1983.pdf http://arxiv.org/ftp/arxiv/papers/1210/1210.1983.pdf

themgt 11y ago
Wow, fantastic paper: However, the relevance of Turing model is questioned even in case of present-day computing [33] [34]. Indeed, any computing machine that follows a Turing model would be highly inefficient to simulate the activity of biological neurons and experience an increased slowdown. Since the super-Turing computing power of the brain has its origins in these ‘strong’ interactions that occur inside neurons, current models have missed the most important part. Simply, Nature doesn’t care if the N-body problem has analytical solutions [36] or can be simulated in real time on a Turing machine [37]. ... While previous models have attempted to represent Hamiltonians using Turing machines [35] the paper [1] shows that the Hamiltonian model of interaction can represent itself a far more powerful model of computation. Turing made an important step forward; however, there is no need to limit natural models of computation to Turing models. In this sense, the new framework of computation using interaction is universal in nature and provides a more general description of computation than the formal Turing model. In other words God was unaware of Turing's work and has put forward a better model for physical computation in the brain.
- cinquemb 11y ago
  I thought it was fantastic as well (because you know a theory that tries to incorporate our understanding of matter and energy [which the brain is a subset of] in a field with a lot of hand-waving going on, shouldn't be that radical…), though for most neuroscientists it is out of reach because for the most part, have little to no understanding of electromagnetism (let's set aside the QED versions of Maxwell's equations) from my experience, which is quite ironic considering the tools that are in heavy use… The amount of money being wasted trying to make special FMRI's and custom EEG caps is lost on me when attempting to solve equations along variable boundary conditions seems like it should come first… Here's the paper the author wrote which that paper is branched from: http://www.researchgate.net/profile/Dorian_Aur/publication/202222729_Dr/links/0d1c84f6f4b8a37018000000.pdf http://www.researchgate.net/profile/Dorian_Aur/publication/2...