Homann Jan, Freed Michael A
Departments of Physics and Astronomy, and.
Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08540.
J Neurosci. 2017 Feb 8;37(6):1468-1478. doi: 10.1523/JNEUROSCI.2814-16.2016. Epub 2016 Dec 30.
Neurons perform computations by integrating excitatory and inhibitory synaptic inputs. Yet, it is rarely understood what computation is being performed, or how much excitation or inhibition this computation requires. Here we present evidence for a neuronal computation that maximizes the signal-to-noise power ratio (SNR). We recorded from OFF delta retinal ganglion cells in the guinea pig retina and monitored synaptic currents that were evoked by visual stimulation (flashing dark spots). These synaptic currents were mediated by a decrease in an outward current from inhibitory synapses (disinhibition) combined with an increase in an inward current from excitatory synapses. We found that the SNR of combined excitatory and disinhibitory currents was voltage sensitive, peaking at membrane potentials near resting potential. At the membrane potential for maximal SNR, the amplitude of each current, either excitatory or disinhibitory, was proportional to its SNR. Such proportionate scaling is the theoretically best strategy for combining excitatory and disinhibitory currents to maximize the SNR of their combined current. Moreover, as spot size or contrast changed, the amplitudes of excitatory and disinhibitory currents also changed but remained in proportion to their SNRs, indicating a dynamic rebalancing of excitatory and inhibitory currents to maximize SNR. We present evidence that the balance of excitatory and disinhibitory inputs to a type of retinal ganglion cell maximizes the signal-to-noise ratio power ratio (SNR) of its postsynaptic currents. This is significant because chemical synapses on a retinal ganglion cell require the probabilistic release of transmitter. Consequently, when the same visual stimulus is presented repeatedly, postsynaptic currents vary in amplitude. Thus, maximizing SNR may be a strategy for producing the most reliable signal possible given the inherent unreliability of synaptic transmission.
神经元通过整合兴奋性和抑制性突触输入来执行计算。然而，人们很少了解正在执行何种计算，或者这种计算需要多少兴奋或抑制。在这里，我们提供了一种神经元计算的证据，该计算可使信噪比（SNR）最大化。我们记录了豚鼠视网膜中 OFF 型 δ 视网膜神经节细胞，并监测了视觉刺激（闪烁的暗点）诱发的突触电流。这些突触电流是由抑制性突触外向电流的减少（去抑制）与兴奋性突触内向电流的增加共同介导的。我们发现，兴奋性和去抑制性电流组合的 SNR 对电压敏感，在接近静息电位的膜电位处达到峰值。在 SNR 最大的膜电位下，每种电流（兴奋性或去抑制性）的幅度与其 SNR 成正比。这种比例缩放是将兴奋性和去抑制性电流组合以最大化其组合电流 SNR 的理论上最佳策略。此外，随着光斑大小或对比度的变化，兴奋性和去抑制性电流的幅度也会变化，但仍与它们的 SNR 成比例，这表明兴奋性和抑制性电流会动态重新平衡以最大化 SNR。我们提供的证据表明，一种视网膜神经节细胞的兴奋性和去抑制性输入的平衡可使其突触后电流的信噪比功率比（SNR）最大化。这很重要，因为视网膜神经节细胞上的化学突触需要递质的概率性释放。因此，当反复呈现相同的视觉刺激时，突触后电流的幅度会发生变化。因此，鉴于突触传递固有的不可靠性，最大化 SNR 可能是产生最可靠信号的一种策略。
