Precise spike timing in silicon

31 32 A fundamental question in neuroscience is how neurons perform precise operations despite inherent variability. This 33 question also applies to neuromorphic engineering, where low-power microchips emulate the brain using large 34 populations of diverse silicon neurons. Biological neurons in the auditory pathway display precise spike timing— 35 critical for sound localization and interpretation of complex waveforms such as speech—even though they are a 36 heterogeneous population. Silicon neurons are also heterogeneous, due to a key design constraint in neuromorphic 37 engineering: Smaller transistors offer lower power consumption and more neurons per unit area of silicon, but also 38 more variability between transistors and thus between silicon neurons. Utilizing this variability in a neuromorphic 39 model of the auditory brainstem with 1080 silicon neurons, we found that a low-voltage-activated potassium 40 conductance (gKL) enables precise spike timing via two mechanisms: statically reducing the resting membrane time 41 constant and dynamically suppressing late synaptic inputs. The relative contribution of these two mechanisms is 42 unknown because blocking gKL in vitro eliminates dynamic adaptation but also lengthens the membrane time 43 constant. We replaced gKL with a static leak in silico to recover the short membrane time constant and found that 44 silicon neurons could mimic the spike-time precision of their biological counterparts, but only over a narrow range 45 of stimulus intensities and biophysical parameters. GKL’s dynamics were required for precise spike timing robust to 46 stimulus variation across a heterogeneous population of silicon neurons, thus explaining how neural and 47 neuromorphic systems may perform precise operations despite inherent variability. 48