Ferrante Michele, Migliore Michele, Ascoli Giorgio A
Krasnow Institute for Advanced Study, Center for Neural Informatics, Structures, and Plasticity, George Mason University, 4400 University Drive, MS 2A1 Fairfax, VA 22030, USA.
Proc Natl Acad Sci U S A. 2009 Oct 20;106(42):18004-9. doi: 10.1073/pnas.0904784106. Epub 2009 Oct 8.
Neuronal processing depends on the input-output (I/O) relation between the frequency of synaptic stimulation and the resultant axonal firing rate. The all-or-none properties of spike generation and active membrane mechanisms can make the neuronal I/O relation very steep. The ensuing nearly bimodal behavior may severely limit information coding, as minimal input fluctuations within the expected natural variability could cause neuronal output to jump between quiescence and maximum firing rate. Here, using biophysically and anatomically realistic computational models of individual neurons, we demonstrate that feed-forward inhibition, a ubiquitous mechanism in which inhibitory interneurons and their target cells are activated by the same excitatory input, can change a steeply sigmoid I/O curve into a double-sigmoid typical of buffer systems. The addition of an intermediate plateau stabilizes the spiking response over a broad dynamic range of input frequency, ensuring robust integration of noisy synaptic signals. Both the buffered firing rate and its input firing range can be independently and extensively modulated by biologically plausible changes in the weight and number of excitatory synapses on the feed-forward interneuron. By providing a soft switch between essentially digital and analog rate-code, this continuous control of the circuit I/O could dramatically increase the computational power of neuronal integration.
神经元处理过程依赖于突触刺激频率与由此产生的轴突放电率之间的输入-输出（I/O）关系。动作电位产生的全或无特性以及主动膜机制会使神经元的I/O关系变得非常陡峭。随之而来的近乎双峰的行为可能会严重限制信息编码，因为预期自然变异性范围内的最小输入波动可能会导致神经元输出在静息状态和最大放电率之间跳跃。在这里，我们使用单个神经元的生物物理和解剖学上逼真的计算模型，证明前馈抑制（一种普遍存在的机制，即抑制性中间神经元及其靶细胞由相同的兴奋性输入激活）可以将陡峭的S形I/O曲线转变为缓冲系统典型的双S形。中间平台的加入在广泛的输入频率动态范围内稳定了放电反应，确保了有噪声的突触信号的稳健整合。缓冲的放电率及其输入放电范围都可以通过前馈中间神经元上兴奋性突触的权重和数量的生物学上合理的变化进行独立且广泛的调节。通过在本质上数字和模拟速率编码之间提供一个软开关，这种对电路I/O的连续控制可以显著提高神经元整合的计算能力。
