• Brain Energy

  • Why is the brain’s information processing power limited by its energy supply? - 1000

  • How (theoretically and experimentally) to determine energy use by neuron on synaptic currents and Aps? - 1000

  • What evolutionary changes have ensured that energy use is minimised in order to maximise processing power? - 500

    • Consider Sarpeshkars point of operations per joule; answers seem to be architecture, hybrid system, and ????
    • Evolutionary argument that energy supply is limiting for most elements of an organism in an environment where caloric surplus is not the status quo
  • Then use clock rate as equivalent to processing power (even though the analog/digital combination of the brain means it isn’t) and examine how clock rate is limited by energy supply via TauM.
    ○ Energy supply limits brain processing to millisecond timescale.

  • Theoretically - use Atwell guide. Calculate based on ion flux concentration changes etc.

    • Experimentally; look at distribution of mitochondria between axon terminals and dendrites/soma to show respective usage? However only indicates a proportion, not actual energy use.
      ○ Could potentially use patch clamping to see current changes? If resistance is known, then V=IR and P=IV
      ○ Use PET with labelled glucose? Compare normal, subtract under Na/K block or anaesthesia to remove housekeeping cost. However there will be other events occurring normally that might be blocked
    • Gut reduction, reduction of adipose deposits, memetic change in the form of cooking, becoming carnivorous

    `not really relevant to the section, i think this is less about general energy use and more about adaptations in the brain

    • High failure rate?
    • Points of support is that architecture etc of the brain all converge to produce maximal efficiency;
      • (plasticity, sparse coding and distributed coding, topographical arrangement apposing local connections for minimal distance,
      • analog and digital; rate of signal can be on or off to a muscle, but specific timings of signals (probably due to summation events) also code in an analog fashion allowing for a more dense energy transfer with a similar energy requirement
    • Also energy costs of signalling estimated to be 50% of total energy supply
  • “Expensive Tissue Hypothesis”
    “The Energetic Significance of Cooking”
    “Navarrete, and evolution of brain size”

  • Metabolic constraints in (human) brain evolution
    We have shown previously that the human brain conforms to the neuronal scaling rules that apply to other primates [27], [44]. According to the scaling of brain metabolism with its total number of neurons proposed above, the apparently remarkable use in humans of 20% of the whole body energy budget by a brain that represents only 2% of this mass can be explained quite simply by the large number of neurons in the human brain, about 10× larger than would be expected of a rodent brain of its size [44], given the different neuronal scaling rules that we have found to apply to rodent and primate brains [24], [25].
    The finding that total brain metabolism scales linearly with the number of brain neurons implies that in primates, whose brain mass scales linearly with its number of neurons, total brain metabolism scales linearly with brain volume, that is, with an exponent of 1, which is much greater than the much-cited Kleiber’s 3/4 exponent that relates body metabolism to body mass [38]. The discrepancy suggests that, per gram, the cost of primate brain tissue scales faster than the cost of non-neuronal bodily tissues, which calls for a modification of the “expensive tissue hypothesis” of brain evolution [11], according to which brain size is a limiting factor. In our view, it is not brain size, but rather absolute number of neurons, that imposes a metabolic constraint upon brain scaling in evolution, as individuals with increasing numbers of neurons must be able to sustain their proportionately larger metabolic requirements to keep their brain functional. Animals rely on feeding for their energy intake, which can be very time-consuming. Larger energetic requirements therefore necessitate more time spent foraging [45], and energy intake is further dependent on the availability and quality of foods: orangutans, for instance, spend 4–5 hours per day feeding, but during the months of low fruit availability that is still not enough to provide all the calories required, and the animals lose weight [46]. As illustrated in Table 4, the larger the number of neurons, the higher the total caloric cost of the brain, and therefore the more time required to be spent feeding in order to support the brain alone.
    The orangutan brain is about a third the size of the human brain, and therefore, given the linear neuronal scaling rules that apply to primate brains, can be estimated to have roughly 1/3 as many neurons and to require 1/3 as many calories to support the brain alone, that is, about 180 kCal. During the months of low fruit availability, when total caloric intake by females is estimated at about 1800 kCal/day (at best, assuming 100% caloric efficiency of the foods ingested), the orangutan brain is estimated to require about 10% of the total caloric intake, which is less than sufficient to support the body (see above). It can thus be seen how any increase in total numbers of neurons in the evolution of hominins may have taxed survival in a way that may have been limiting, if not prohibitive: a doubling in the number of brain neurons from an orangutan-sized hominin ancestor would have required an additional 180 kCal/day that might not be readily available. In this context, it has been proposed that the advent of the ability to control fire to cook foods, which increases enormously the energetic yield of foods and the speed with which they are consumed [47], may have been a crucial step in allowing the near doubling of numbers of brain neurons that is estimated to have occurred between Homo erectus and Homo sapiens [48]. The evolution of the human brain, with its high metabolic cost determined by its large number of neurons, may therefore only have been possible due to the use of fire to cook foods, thus enabling individuals to ingest in very little time the entire caloric requirement for the day.

  • The real Requirements

    Postsynaptic actions of glutamate.

    Ion fluxes through non–N-methyl-D-aspartate (non-NMDA) receptor channels. Estimates of effects; release of a vesicle of glutamate leads to activation of 15, 30, and 15 to 200 postsynaptic non-NMDA channels, respectively (but the highest estimate was recognized as being perhaps more than 4-fold too high (Markram et al., 1997)), with a mean channel open time of 1, 0.6, and 1.4, milliseconds, respectively, and a channel conductance of 11.6, 12.6, and 13.3 pS (at 37°C), of which 2/3 is due to Na+ for a reversal potential of 0 mV. If the driving force is VNa − V ?120 mV, this gives an entry of 87,000, 96,000, and 140,000 to 1,867,000 Na+, respectively (the last value being possibly more than 4-fold too high). Using a weighted average from experimental data, dominated by the more reliable values from noncortical neurons, 200,000 Na+ enter and inorder to extrude them the Na+/K+ pump will hydrolyze approximately 67,000 ATP molecules per vesicle release.

  • Energy supply limits computational speed

    Rapid processing of information by the brain requires both the rapid processing of subthreshold synaptic potentials in dendrites and the propagation of information by action potentials at high frequencies. Increasing the temporal rate of either of these operations carries a heavy metabolic price, which we explain below. The timescale of processing of subthreshold signals is limited, in part, by the MEMBRANE TIME CONSTANT, τm, which is the product of the cell MEMBRANE RESISTANCE, Rm, and MEMBRANE CAPACITANCE, Cm, and is ~1–20 ms. For a VOLTAGE-UNIFORM CELL, the capacitance filters out synaptic current signal components above a frequency of 1/(2πτm), ~8–160 Hz, whereas for synaptic currents in neuronal dendrites the frequency of filtering is somewhat higher than 1/(2πτm). An upper frequency of ~200 Hz is matched fairly well to the maximum action potential firing rate of neurons in vivo, which is ~100–300 Hz (although the mean firing rate is much lower, ~4 Hz in rodents). Therefore, the timescale of both (passive) dendritic and action potential information processing is in the millisecond range.

    WHY NOT HIGHER

    To raise to higher frequencies the temporal range over which synaptic currents can convey information, it is necessary to decrease τm, and, therefore, to decrease either Cm or Rm. It might not be feasible to reduce Cm
    in all neurons (BOX 1), but Rm could be decreased by
    inserting more ion channels into the membrane. This strategy is used, for example, to speed the photo- receptor responses of flies. However, the resulting
    higher conductance and higher resting flux of Na+ and K+
    across the membrane will increase the energy tenfold increase in energy expended on the neuronal resting potential would consume all the energy available for signalling; consequently there would be no energy left to power action potentials and synaptic currents, and the overall information processing rate of the brain would fall dramatically. Conversely, if the action potential rate were increased to increase the temporal resolution of information transmission by action potentials, only a 15% increase in the average firing rate (100%/87%, as 87% of the energy use is proportional to action potential frequency) would use all the energy available for signalling, so that no energy would be left to maintain the resting potential. We conclude that it is not possible to increase ten-fold the energy expended on the resting potential, or on action potentials, to increase tenfold the upper fre- quency limit for information processing with synaptic or action potentials. Diversion of energy to increase the information processing rate for synaptic potentials in dendrites would decrease the rate for action potentials in axons, and vice versa. Therefore, the energy available to the brain for signalling limits its timescale of operation to the millisecond range. Possible approaches for circumventing this limitation are considered in BOX 1.

    ATTWELL AND GIBBS

  • Distribution of ATP Consumption for a given AP rate

    • ENERGY FOR NA EXTRUSION
    • EXPENDITURE ON NEURONAL AND GLIAL RPs
    • SCALING ENERGY CONSUMPTION BY ACTION POTENTIAL FREQUENCY
    • DISTRIBUTION OF ENERGY USAGE
    • ENERGY USAGE AND SPECIFIC RATES OF CONSUMPTION OF ATP, GLUCOSE AND OXYGEN
  • Presynaptic
    Post Synaptic
    And Propagation.
    Na extrusion?

  • “AP propagation in an unmyelinated axon occurs as a consequence of the influx of sodium ions through distributed voltage gated-channels and an efflux of potassium ions (Attwell and Laughlin, 2001). This, in turn, leads to an increase in the activity of Na+ – K+ ATPase pump, which acts to restore the levels of sodium and potassium ions, against concentration gradients, to resting levels. Since we know the precise density of sodium channels and the precise number of sodium ions that enter the axonal arbor for the propagation of an AP in the model, we can estimate the number of ATP molecules (and hence energy) required to operate the Na+ – K+ ATPase pump to restore the levels of ions to their resting state. Since calcium ion influx is a significant component of the axon potential in dopamine neurons, we applied the same approach for calcium.”

  • BY KNOWING NA EFFLUX ETC AND CHANGES IN POTENTIAL, ATP USE BY THE NA/K ATPASE TO CORRECT SHOULD BE MAIN COMPONENT OF ENERGY USE IN AP.

  • COST OF ACTION POTENTIAL

  • BITS AND PIECES

  • Synapse Energy Is Mainly Provided by Mitochondria

    The previous sections assessed how synapse properties can maximize the information that synapses transmit while reducing the energy used. But how is the massive energy use of synapses sustained? Averaged over time, in the adult brain ATP is almost entirely generated by the complete oxidation of glucose. Glycolysis followed by oxidative phosphorylation results in a ratio of oxygen to glucose consumption of nearly 6:1, and mitochondria (using the citric acid cycle followed by oxidative phosphorylation) provide ∼93% of the ATP generated (Sokoloff, 1960), with only ∼7% coming from glycolysis. Consistent with this, mitochondria are preferentially localized to pre- and postsynaptic sites where ATP is consumed (Wong-Riley, 1989; Chang et al., 2006). Nevertheless, when neuronal activity increases during perceptual tasks like those used in functional imaging experiments, which increase energy consumption by only a small percentage (Schölvinck et al., 2008; Lin et al., 2010), it has been suggested that ATP might be generated preferentially by glycolysis.

    http://www.sciencedirect.com/science/article/pii/S0896627312007568

  • Indicate that mitochondrial location is activity dependent due to miro/milton Ca2+ interaction

  • Can use mitochondrial distribution to show “For the dendrites + soma column, Wong-Riley’s data are the percentage of mitochondria in dendrites, whereas our prediction is the percentage of energy expended on postsynaptic currents, dendritic and somatic action potentials, and the neuronal resting potential. For the axons + terminals column, Wong-Riley’s data are the percentage of mitochondria in axons and presynaptic terminals, and our prediction is for the percentage of energy expended on axonal action potentials, presynaptic Ca2+ entry, accumulating glutamate into vesicles, and recycling vesicles.”

    Not a way of isolating just the energy used in producing an action potential

  • Division of energy between Cellular processes

    It would be interesting to distinguish the oxygen use by synaptic currents and action
    potentials. Caesar et al. (2003) have activated synaptic input and prevented action potential 185
    production by applying muscimol and baclofen, GABAa and GABAb receptor agonists, to keep cells hyperpolarised. An alternative method of investigating this question would be to apply glutamate, or AMPA, and test the effect on the oxygen response of blocking action potentials with TTX. Using this method, it would not be possible to quantify the relative usage by synaptic currents and action potentials. However, in TTX, glutamate-evoked Na+ entry could be measured in voltage clamp experiments and oxygen usage measured simultaneously. One could then correlate the oxygen use with the Na^ entry. Similarly in current clamp mode, with glutamate blockers present, one could inject current and correlate the number of action potentials produced with the oxygen usage.

  • “The energy budget confirms that brain is, by the nature of its work, “expensive tissue” (Aiello and Wheeler, 1995). The signaling-related energy use of 30 ?mol ATP/g/min is equal to that in human leg muscle running the marathon (Hochachka, 1994). Such a high metabolic rate will limit the brain’s size (Aiello and Wheeler, 1995) and favor mechanisms that use energy efficiently (Sarpeshkar, 1998).”

  • Energy Limitation on size of the brain - length of connections as well as tissue generation cost?

  • Myelination and white matter; even though energetically not really favourable, it lowers upfront, constant energy requirement in favour of developmental costs. This relates to the unusually long developmental stage in humans. - ENERGY BUDGET OF WHITE MATTER,

    Key point is that the total energy supply to the brain is limited to start with; variation seen in fMRI are, in the grand scheme, minimal.

  • Limitation of bouton size to allow glutamate clearance on same timescale as AMPA activity; within 1 msec.

  • FAT OVER GUT
    “an organism’s capacity to store body fat, which is a relatively inexpensive way to buffer food scarcity, can be reduced in lineages in which a bigger brain allows better-quality food intake or lowers the energetic costs of other life functions.”

  • Published anatomical and electrophysiological data were employed to calculate the ATP used to

    1. reverse the Na+ influx producing excitatory synaptic currents and action potentials,
    2. the Na+ influx occurring at the resting potential,
    3. the Ca2+ entry driving neurotransmitter release
    4. the ATP expended on other less energy consuming processes inherent in CNS information processing.

    Measured ion fluxes were converted into values for ATP consumption using the fact that the Na+/K+-ATPase consumes one ATP per 3 Na+ extruded, while 3 Na+/Ca2+ exchange followed by Na+ extrusion uses 1 ATP per Ca2+ extruded. Similarly, the ATP needed to restore Ca2+ to intracellular stores, and expended on transmitter and vesicle recycling, were estimated (Attwell and Laughlin, 2001).

    The energy expended on restoring Cl gradients after inhibitory transmission was previously estimated to be <1% of that needed to restore an equivalent change of the Na+ gradient and so was ignored (Howarth et al, 2010)

  • The energy consumption of APs is due to the influx of Na+ ions and efflux of K+ ions through voltage-gated ion channels, which charge the membrane capacitance to the peak of the AP and then discharge it back to resting potential. To maintain signaling the Na+/K+ ATPase pumps these ions back across the membrane using energy provided by ATP [3]. There are three basic reasons why APs use significant quantities of energy. First, to make a robust signal, the membrane capacitance is usually charged by more than 50 mV to the peak of the AP. Second, because APs travel considerable distances along densely packed axons, collaterals and dendrites, the total area of membrane invaded by APs is large and so, therefore, is the capacitance that must be charged to the peak voltage. Third, the flux of Na+ and K+ ions exceeds the minimum required to charge the membrane to peak potential because the Na+ and K+ currents overlap [4], [5].

  • “ patch-clamp data provided quantification of the ions entering to generate typical synaptic currents, which also need to be pumped out (for simplicity all neurons, rather than ∼85% [Abeles, 1991], were assumed to be excitatory). “

  • “We find that the brain’s metabolic requirements peak in childhood, when it uses glucose at a rate equivalent to 66% of the body’s resting metabolism and 43% of the body’s daily energy requirement, and that brain glucose demand relates inversely to body growth from infancy to puberty.”
    http://www.pnas.org/content/111/36/13010.full

{"cards":[{"_id":"744ff4f77ae07be11e00024f","treeId":"744ff2677ae07be11e00024c","seq":9272876,"position":1,"parentId":null,"content":"Brain Energy\n"},{"_id":"744ff53b7ae07be11e000250","treeId":"744ff2677ae07be11e00024c","seq":9272883,"position":1,"parentId":"744ff4f77ae07be11e00024f","content":"Why is the brain's information processing power limited by its energy supply? - 1000\n\n\n"},{"_id":"744ff5db7ae07be11e000251","treeId":"744ff2677ae07be11e00024c","seq":9272880,"position":1,"parentId":"744ff53b7ae07be11e000250","content":"- Evolutionary argument that energy supply is limiting for most elements of an organism in an environment where caloric surplus is not the status quo"},{"_id":"745a42437ae07be11e000262","treeId":"744ff2677ae07be11e00024c","seq":9279381,"position":0.5,"parentId":"744ff5db7ae07be11e000251","content":"\"Expensive Tissue Hypothesis\"\n\"The Energetic Significance of Cooking\"\n\"Navarrete, and evolution of brain size\""},{"_id":"745a48397ae07be11e000263","treeId":"744ff2677ae07be11e00024c","seq":9278689,"position":1,"parentId":"745a42437ae07be11e000262","content":"FAT OVER GUT\n\"an organism's capacity to store body fat, which is a relatively inexpensive way to buffer food scarcity, can be reduced in lineages in which a bigger brain allows better-quality food intake or lowers the energetic costs of other life functions.\""},{"_id":"74520ec57ae07be11e000261","treeId":"744ff2677ae07be11e00024c","seq":9275088,"position":1,"parentId":"744ff5db7ae07be11e000251","content":"Metabolic constraints in (human) brain evolution\nWe have shown previously that the human brain conforms to the neuronal scaling rules that apply to other primates [27], [44]. According to the scaling of brain metabolism with its total number of neurons proposed above, the apparently remarkable use in humans of 20% of the whole body energy budget by a brain that represents only 2% of this mass can be explained quite simply by the large number of neurons in the human brain, about 10× larger than would be expected of a rodent brain of its size [44], given the different neuronal scaling rules that we have found to apply to rodent and primate brains [24], [25].\nThe finding that total brain metabolism scales linearly with the number of brain neurons implies that in primates, whose brain mass scales linearly with its number of neurons, total brain metabolism scales linearly with brain volume, that is, with an exponent of 1, which is much greater than the much-cited Kleiber's 3/4 exponent that relates body metabolism to body mass [38]. The discrepancy suggests that, per gram, the cost of primate brain tissue scales faster than the cost of non-neuronal bodily tissues, which calls for a modification of the “expensive tissue hypothesis” of brain evolution [11], according to which brain size is a limiting factor. In our view, it is not brain size, but rather absolute number of neurons, that imposes a metabolic constraint upon brain scaling in evolution, as individuals with increasing numbers of neurons must be able to sustain their proportionately larger metabolic requirements to keep their brain functional. Animals rely on feeding for their energy intake, which can be very time-consuming. Larger energetic requirements therefore necessitate more time spent foraging [45], and energy intake is further dependent on the availability and quality of foods: orangutans, for instance, spend 4–5 hours per day feeding, but during the months of low fruit availability that is still not enough to provide all the calories required, and the animals lose weight [46]. As illustrated in Table 4, the larger the number of neurons, the higher the total caloric cost of the brain, and therefore the more time required to be spent feeding in order to support the brain alone.\nThe orangutan brain is about a third the size of the human brain, and therefore, given the linear neuronal scaling rules that apply to primate brains, can be estimated to have roughly 1/3 as many neurons and to require 1/3 as many calories to support the brain alone, that is, about 180 kCal. During the months of low fruit availability, when total caloric intake by females is estimated at about 1800 kCal/day (at best, assuming 100% caloric efficiency of the foods ingested), the orangutan brain is estimated to require about 10% of the total caloric intake, which is less than sufficient to support the body (see above). It can thus be seen how any increase in total numbers of neurons in the evolution of hominins may have taxed survival in a way that may have been limiting, if not prohibitive: a doubling in the number of brain neurons from an orangutan-sized hominin ancestor would have required an additional 180 kCal/day that might not be readily available. In this context, it has been proposed that the advent of the ability to control fire to cook foods, which increases enormously the energetic yield of foods and the speed with which they are consumed [47], may have been a crucial step in allowing the near doubling of numbers of brain neurons that is estimated to have occurred between Homo erectus and Homo sapiens [48]. The evolution of the human brain, with its high metabolic cost determined by its large number of neurons, may therefore only have been possible due to the use of fire to cook foods, thus enabling individuals to ingest in very little time the entire caloric requirement for the day.\n"},{"_id":"744ff6b37ae07be11e000252","treeId":"744ff2677ae07be11e00024c","seq":9272882,"position":2,"parentId":"744ff53b7ae07be11e000250","content":"Then use clock rate as equivalent to processing power (even though the analog/digital combination of the brain means it isn't) and examine how clock rate is limited by energy supply via TauM.\n\t\t○ Energy supply limits brain processing to millisecond timescale."},{"_id":"745dff1b7ae07be11e000270","treeId":"744ff2677ae07be11e00024c","seq":9280845,"position":0.5,"parentId":"744ff6b37ae07be11e000252","content":"## The real Requirements\n### Postsynaptic actions of glutamate. \nIon fluxes through non–N-methyl-D-aspartate (non-NMDA) receptor channels. Estimates of effects; release of a vesicle of glutamate leads to activation of 15, 30, and 15 to 200 postsynaptic non-NMDA channels, respectively (but the highest estimate was recognized as being perhaps more than 4-fold too high (Markram et al., 1997)), with a mean channel open time of 1, 0.6, and 1.4, milliseconds, respectively, and a channel conductance of 11.6, 12.6, and 13.3 pS (at 37°C), of which 2/3 is due to Na+ for a reversal potential of 0 mV. If the driving force is VNa − V ?120 mV, this gives an entry of 87,000, 96,000, and 140,000 to 1,867,000 Na+, respectively (the last value being possibly more than 4-fold too high). Using a weighted average from experimental data, dominated by the more reliable values from noncortical neurons, 200,000 Na+ enter and inorder to extrude them the Na+/K+ pump will hydrolyze approximately 67,000 ATP molecules per vesicle release."},{"_id":"744ff92f7ae07be11e000254","treeId":"744ff2677ae07be11e00024c","seq":9278698,"position":1,"parentId":"744ff6b37ae07be11e000252","content":"##Energy supply limits computational speed \nRapid processing of information by the brain requires both the rapid processing of subthreshold synaptic potentials in dendrites and the propagation of information by action potentials at high frequencies. Increasing the temporal rate of either of these operations carries a heavy metabolic price, which we explain below. The timescale of processing of subthreshold signals is limited, in part, by the MEMBRANE TIME CONSTANT, τm, which is the product of the cell MEMBRANE RESISTANCE, Rm, and MEMBRANE CAPACITANCE, Cm, and is ~1–20 ms. For a VOLTAGE-UNIFORM CELL, the capacitance filters out synaptic current signal components above a frequency of 1/(2πτm), ~8–160 Hz, whereas for synaptic currents in neuronal dendrites the frequency of filtering is somewhat higher than 1/(2πτm). An upper frequency of ~200 Hz is matched fairly well to the maximum action potential firing rate of neurons in vivo, which is ~100–300 Hz (although the mean firing rate is much lower, ~4 Hz in rodents). Therefore, the timescale of both (passive) dendritic and action potential information processing is in the millisecond range.\n\n## WHY NOT HIGHER\nTo raise to higher frequencies the temporal range over which synaptic currents can convey information, it is necessary to decrease τm, and, therefore, to decrease either Cm or Rm. It might not be feasible to reduce Cm\nin all neurons (BOX 1), but Rm could be decreased by\ninserting more ion channels into the membrane. This strategy is used, for example, to speed the photo- receptor responses of flies. However, the resulting\nhigher conductance and higher resting flux of Na+ and K+\nacross the membrane will increase the energy tenfold increase in energy expended on the neuronal resting potential would consume all the energy available for signalling; consequently there would be no energy left to power action potentials and synaptic currents, and the overall information processing rate of the brain would fall dramatically. Conversely, if the action potential rate were increased to increase the temporal resolution of information transmission by action potentials, only a 15% increase in the average firing rate (100%/87%, as 87% of the energy use is proportional to action potential frequency) would use all the energy available for signalling, so that no energy would be left to maintain the resting potential. We conclude that it is not possible to increase ten-fold the energy expended on the resting potential, or on action potentials, to increase tenfold the upper fre- quency limit for information processing with synaptic or action potentials. Diversion of energy to increase the information processing rate for synaptic potentials in dendrites would decrease the rate for action potentials in axons, and vice versa. Therefore, the energy available to the brain for signalling limits its timescale of operation to the millisecond range. Possible approaches for circumventing this limitation are considered in BOX 1.\n\n\nATTWELL AND GIBBS"},{"_id":"744fff6d7ae07be11e000258","treeId":"744ff2677ae07be11e00024c","seq":9272918,"position":2,"parentId":"744ff4f77ae07be11e00024f","content":"\t\nHow (theoretically and experimentally) to determine energy use by neuron on synaptic currents and Aps? - 1000\n"},{"_id":"744fffc47ae07be11e000259","treeId":"744ff2677ae07be11e00024c","seq":9272922,"position":1,"parentId":"744fff6d7ae07be11e000258","content":"Theoretically - use Atwell guide. Calculate based on ion flux concentration changes etc."},{"_id":"7450462a7ae07be11e00025e","treeId":"744ff2677ae07be11e00024c","seq":9274093,"position":0.5,"parentId":"744fffc47ae07be11e000259","content":"Distribution of ATP Consumption for a given AP rate"},{"_id":"745041187ae07be11e00025d","treeId":"744ff2677ae07be11e00024c","seq":9280848,"position":1,"parentId":"744fffc47ae07be11e000259","content":"- ENERGY FOR NA EXTRUSION\n- EXPENDITURE ON NEURONAL AND GLIAL RPs\n- SCALING ENERGY CONSUMPTION BY ACTION POTENTIAL FREQUENCY\n- DISTRIBUTION OF ENERGY USAGE\n- ENERGY USAGE AND SPECIFIC RATES OF CONSUMPTION OF ATP, GLUCOSE AND OXYGEN\n- "},{"_id":"745059397ae07be11e00025f","treeId":"744ff2677ae07be11e00024c","seq":9278885,"position":1,"parentId":"745041187ae07be11e00025d","content":"Published anatomical and electrophysiological data were employed to calculate the ATP used to\n1. reverse the Na+ influx producing excitatory synaptic currents and action potentials, \n2. the Na+ influx occurring at the resting potential, \n3. the Ca2+ entry driving neurotransmitter release\n4. the ATP expended on other less energy consuming processes inherent in CNS information processing. \n\nMeasured ion fluxes were converted into values for ATP consumption using the fact that the Na+/K+-ATPase consumes one ATP per 3 Na+ extruded, while 3 Na+/Ca2+ exchange followed by Na+ extrusion uses 1 ATP per Ca2+ extruded. Similarly, the ATP needed to restore Ca2+ to intracellular stores, and expended on transmitter and vesicle recycling, were estimated (Attwell and Laughlin, 2001). \n\nThe energy expended on restoring Cl gradients after inhibitory transmission was previously estimated to be <1% of that needed to restore an equivalent change of the Na+ gradient and so was ignored (Howarth et al, 2010)"},{"_id":"745e12c57ae07be11e000271","treeId":"744ff2677ae07be11e00024c","seq":9281165,"position":1.5,"parentId":"744fffc47ae07be11e000259","content":"Presynaptic\nPost Synaptic\nAnd Propagation.\nNa extrusion?"},{"_id":"745a9f007ae07be11e000266","treeId":"744ff2677ae07be11e00024c","seq":9278813,"position":2,"parentId":"744fffc47ae07be11e000259","content":"\"AP propagation in an unmyelinated axon occurs as a consequence of the influx of sodium ions through distributed voltage gated-channels and an efflux of potassium ions (Attwell and Laughlin, 2001). This, in turn, leads to an increase in the activity of Na+ – K+ ATPase pump, which acts to restore the levels of sodium and potassium ions, against concentration gradients, to resting levels. Since we know the precise density of sodium channels and the precise number of sodium ions that enter the axonal arbor for the propagation of an AP in the model, we can estimate the number of ATP molecules (and hence energy) required to operate the Na+ – K+ ATPase pump to restore the levels of ions to their resting state. Since calcium ion influx is a significant component of the axon potential in dopamine neurons, we applied the same approach for calcium.\""},{"_id":"745aa5d87ae07be11e000267","treeId":"744ff2677ae07be11e00024c","seq":9278875,"position":3,"parentId":"744fffc47ae07be11e000259","content":"BY KNOWING NA EFFLUX ETC AND CHANGES IN POTENTIAL, ATP USE BY THE NA/K ATPASE TO CORRECT SHOULD BE MAIN COMPONENT OF ENERGY USE IN AP."},{"_id":"745af98d7ae07be11e000269","treeId":"744ff2677ae07be11e00024c","seq":9278883,"position":4,"parentId":"744fffc47ae07be11e000259","content":"COST OF ACTION POTENTIAL"},{"_id":"745af9dc7ae07be11e00026a","treeId":"744ff2677ae07be11e00024c","seq":9278884,"position":1,"parentId":"745af98d7ae07be11e000269","content":"The energy consumption of APs is due to the influx of Na+ ions and efflux of K+ ions through voltage-gated ion channels, which charge the membrane capacitance to the peak of the AP and then discharge it back to resting potential. To maintain signaling the Na+/K+ ATPase pumps these ions back across the membrane using energy provided by ATP [3]. There are three basic reasons why APs use significant quantities of energy. First, to make a robust signal, the membrane capacitance is usually charged by more than 50 mV to the peak of the AP. Second, because APs travel considerable distances along densely packed axons, collaterals and dendrites, the total area of membrane invaded by APs is large and so, therefore, is the capacitance that must be charged to the peak voltage. Third, the flux of Na+ and K+ ions exceeds the minimum required to charge the membrane to peak potential because the Na+ and K+ currents overlap [4], [5]."},{"_id":"745b07d87ae07be11e00026b","treeId":"744ff2677ae07be11e00024c","seq":9278894,"position":5,"parentId":"744fffc47ae07be11e000259","content":"BITS AND PIECES"},{"_id":"745b08067ae07be11e00026c","treeId":"744ff2677ae07be11e00024c","seq":9278895,"position":1,"parentId":"745b07d87ae07be11e00026b","content":"\" patch-clamp data provided quantification of the ions entering to generate typical synaptic currents, which also need to be pumped out (for simplicity all neurons, rather than ∼85% [Abeles, 1991], were assumed to be excitatory). \""},{"_id":"745000367ae07be11e00025a","treeId":"744ff2677ae07be11e00024c","seq":9272927,"position":2,"parentId":"744fff6d7ae07be11e000258","content":"- Experimentally; look at distribution of mitochondria between axon terminals and dendrites/soma to show respective usage? However only indicates a proportion, not actual energy use.\n ○ Could potentially use patch clamping to see current changes? If resistance is known, then V=IR and P=IV\n ○ Use PET with labelled glucose? Compare normal, subtract under Na/K block or anaesthesia to remove housekeeping cost. However there will be other events occurring normally that might be blocked\n"},{"_id":"745af3597ae07be11e000268","treeId":"744ff2677ae07be11e00024c","seq":9278881,"position":1,"parentId":"745000367ae07be11e00025a","content":"## Synapse Energy Is Mainly Provided by Mitochondria\n\nThe previous sections assessed how synapse properties can maximize the information that synapses transmit while reducing the energy used. But how is the massive energy use of synapses sustained? Averaged over time, in the adult brain ATP is almost entirely generated by the complete oxidation of glucose. Glycolysis followed by oxidative phosphorylation results in a ratio of oxygen to glucose consumption of nearly 6:1, and mitochondria (using the citric acid cycle followed by oxidative phosphorylation) provide ∼93% of the ATP generated (Sokoloff, 1960), with only ∼7% coming from glycolysis. Consistent with this, mitochondria are preferentially localized to pre- and postsynaptic sites where ATP is consumed (Wong-Riley, 1989; Chang et al., 2006). Nevertheless, when neuronal activity increases during perceptual tasks like those used in functional imaging experiments, which increase energy consumption by only a small percentage (Schölvinck et al., 2008; Lin et al., 2010), it has been suggested that ATP might be generated preferentially by glycolysis.\n\nhttp://www.sciencedirect.com/science/article/pii/S0896627312007568"},{"_id":"745b33c07ae07be11e00026f","treeId":"744ff2677ae07be11e00024c","seq":9278921,"position":1.5,"parentId":"745000367ae07be11e00025a","content":"Indicate that mitochondrial location is activity dependent due to miro/milton Ca2+ interaction"},{"_id":"745b31ba7ae07be11e00026e","treeId":"744ff2677ae07be11e00024c","seq":9278919,"position":2,"parentId":"745000367ae07be11e00025a","content":"Can use mitochondrial distribution to show \"For the dendrites + soma column, Wong-Riley’s data are the percentage of mitochondria in dendrites, whereas our prediction is the percentage of energy expended on postsynaptic currents, dendritic and somatic action potentials, and the neuronal resting potential. For the axons + terminals column, Wong-Riley’s data are the percentage of mitochondria in axons and presynaptic terminals, and our prediction is for the percentage of energy expended on axonal action potentials, presynaptic Ca2+ entry, accumulating glutamate into vesicles, and recycling vesicles.\"\n\nNot a way of isolating just the energy used in producing an action potential"},{"_id":"745ef0b77ae07be11e000272","treeId":"744ff2677ae07be11e00024c","seq":9281169,"position":3,"parentId":"745000367ae07be11e00025a","content":"## Division of energy between Cellular processes\n\nIt would be interesting to distinguish the oxygen use by synaptic currents and action\npotentials. Caesar et al. (2003) have activated synaptic input and prevented action potential 185\nproduction by applying muscimol and baclofen, GABAa and GABAb receptor agonists, to keep cells hyperpolarised. An alternative method of investigating this question would be to apply glutamate, or AMPA, and test the effect on the oxygen response of blocking action potentials with TTX. Using this method, it would not be possible to quantify the relative usage by synaptic currents and action potentials. However, in TTX, glutamate-evoked Na+ entry could be measured in voltage clamp experiments and oxygen usage measured simultaneously. One could then correlate the oxygen use with the Na^ entry. Similarly in current clamp mode, with glutamate blockers present, one could inject current and correlate the number of action potentials produced with the oxygen usage."},{"_id":"7450015f7ae07be11e00025b","treeId":"744ff2677ae07be11e00024c","seq":9279379,"position":3,"parentId":"744ff4f77ae07be11e00024f","content":"What evolutionary changes have ensured that energy use is minimised in order to maximise processing power? - 500\n\n - Consider Sarpeshkars point of operations per joule; answers seem to be architecture, hybrid system, and ????\n\t\n"},{"_id":"7450019d7ae07be11e00025c","treeId":"744ff2677ae07be11e00024c","seq":9278932,"position":1,"parentId":"7450015f7ae07be11e00025b","content":"- Gut reduction, reduction of adipose deposits, memetic change in the form of cooking, becoming carnivorous\n\n`not really relevant to the section, i think this is less about general energy use and more about adaptations in the brain\n\n- High failure rate?"},{"_id":"744ff7107ae07be11e000253","treeId":"744ff2677ae07be11e00024c","seq":9278937,"position":3,"parentId":"7450015f7ae07be11e00025b","content":"- Points of support is that architecture etc of the brain all converge to produce maximal efficiency; \n - (plasticity, sparse coding and distributed coding, topographical arrangement apposing local connections for minimal distance,\n - analog and digital; rate of signal can be on or off to a muscle, but specific timings of signals (probably due to summation events) also code in an analog fashion allowing for a more dense energy transfer with a similar energy requirement\n- Also energy costs of signalling estimated to be 50% of total energy supply\n"},{"_id":"74515a907ae07be11e000260","treeId":"744ff2677ae07be11e00024c","seq":9274734,"position":0.5,"parentId":"744ff7107ae07be11e000253","content":"\"The energy budget confirms that brain is, by the nature of its work, “expensive tissue” (Aiello and Wheeler, 1995). The signaling-related energy use of 30 ?mol ATP/g/min is equal to that in human leg muscle running the marathon (Hochachka, 1994). Such a high metabolic rate will limit the brain’s size (Aiello and Wheeler, 1995) and favor mechanisms that use energy efficiently (Sarpeshkar, 1998).\""},{"_id":"744ffa307ae07be11e000255","treeId":"744ff2677ae07be11e00024c","seq":9272899,"position":1,"parentId":"744ff7107ae07be11e000253","content":"Energy Limitation on size of the brain - length of connections as well as tissue generation cost?\n"},{"_id":"744ffa767ae07be11e000256","treeId":"744ff2677ae07be11e00024c","seq":9278702,"position":2,"parentId":"744ff7107ae07be11e000253","content":"Myelination and white matter; even though energetically not really favourable, it lowers upfront, constant energy requirement in favour of developmental costs. This relates to the unusually long developmental stage in humans. - ENERGY BUDGET OF WHITE MATTER, \n\nKey point is that the total energy supply to the brain is limited to start with; variation seen in fMRI are, in the grand scheme, minimal. \n"},{"_id":"745a592e7ae07be11e000264","treeId":"744ff2677ae07be11e00024c","seq":9278703,"position":1,"parentId":"744ffa767ae07be11e000256","content":"\"We find that the brain’s metabolic requirements peak in childhood, when it uses glucose at a rate equivalent to 66% of the body’s resting metabolism and 43% of the body’s daily energy requirement, and that brain glucose demand relates inversely to body growth from infancy to puberty.\"\nhttp://www.pnas.org/content/111/36/13010.full"},{"_id":"745a88937ae07be11e000265","treeId":"744ff2677ae07be11e00024c","seq":9278810,"position":3,"parentId":"744ff7107ae07be11e000253","content":"Limitation of bouton size to allow glutamate clearance on same timescale as AMPA activity; within 1 msec."}],"tree":{"_id":"744ff2677ae07be11e00024c","name":"Brain Energy","publicUrl":"744ff2677ae07be11e00024c"}}