Molecular computation in neurons: a modeling perspective

By | December 21, 2013

Included here is a summary of the following article:

Bhalla, Upinder S. "Molecular computation in neurons: a modeling perspective." Current Opinion in Neurobiology 25 (2014): 31-37.

Introduction

  • Chemical signaling probably accounts for a hundred times as much computation as electrical signaling.
  • What is involved in these computations? There are over 1400 distinct proteins in the post synaptic region.
  • Chemical computation includes:
    • presynaptic signaling
    • bistability from chemical feedback loops
    • bistability from receptor trafficking
    • 'off' state with few receptors
    • synaptic tagging and local activity spread
    • spine structural change in plasticity
    • input patterns control plasticity
    • activity controls gene expression

Review

Pattern selectivity

  • Calcium influx can trigger both increased or decreased synaptic efficacy, and there may be local as well as global differences throughout the neuron
  • Mitogen-activated protein kinase (MAPK) cascade shows preference for bursts that are spaced by 5-10 min
  • Kinase cascades have the ability to respond to a range of time-scales from seconds to minutes
  • After all, the spike-timing dependent plasticity (STDP) is a direct result of channel biophysics

Spatial models and spatial pattern selectivity

  • Synaptic tagging - strong activity at one synapse enables the potentiation of a nearby synapse receiving much weaker input
  • Reaction-diffusion models demonstrate how long term potentiation and long term depression spreads to neighboring spines
  • The spatial spread of calmodulin (CaM) plays a role in decoding synaptic input patterns

Signal Propagation

  • Precision in spatial signaling can be achieved through chemistry via diffusion but perhaps through other complex means
  • Chemical signaling might support long range signal propagation such as through active transport via cellular motors
  • Many chemical computations can occur in parallel in each neuron

Long-term storage: biochemical bistability

  • Three processes act against the stable long-term storage abilities of synapses: turnover, diffusion, and stochasticity.
  • It has been showed that bistability, which is often a demonstration of how a positive feedback loop may strengthen memories, has a very narrow range in synaptic/dendritic protein synthesis.
  • There are many biochemically plausible candidates for memory storage at the synapse.

Plasticity and molecular traffic

  • How does a synapse retain its state given diffusion processes that occur?
  • One idea is molecular crowding that can preserve receptors in the synapse for periods of hours
  • It is the molecular movement of receptors as opposed to their signaling identity that is needed for LTP, for example.

Losing state: stochasticity and bistability

  • Given how small a synapse is, and that there are only one to two hundred calcium receptors, there are strong fluctuations in chemical activity. Such "noise" affects state transitions.
  • These stochastic fluctuations may even be sufficient to cause state flips in biochemical bistables, erasing any memories they store. Many bistable models fail the stochasticity test.

Continuous synaptic strengths

  • Most studies report continuous plasticity (as opposed to bistable states) without explicitly addressing whether this is at the single synapse level.
  • Continuous states could be accomplished by having many stable states, which are indeed possible, but only through using many bistable states to accomplish a multistate system.

Conclusion

  • Most computation at the neuron level is chemical, such as the roles of pattern decoding and memory storage
  • It may be advantageous to use electrical computation when it is a higher level that encompasses much low level chemical computation
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