We present a novel technique for performing current-mode analog-to-digital conversion using two integrate-and-fire spiking neurons. The conversion is performed in two steps. The first step is an overranging step that extracts the integer part of the ratio between the input current and a reference current; the second step is a subranging step that extracts the fractional part of this ratio. The conversion is performed by having analog spike-time information in one neuron, the timing neuron,

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... ate digital spike-count information in another neuron, the counting neuron. The roles of the timing neuron and the counting neuron are reversed as we transition between the steps. Operations in the converter are coordinated via a spike-triggered asynchronous finite state machine, obviating the need for a clock to synchronize the internal operations of the converter. We present experimental data from a proof-of-concept VLSI implementation. Because spike number is discrete while interspike intervals are analog, spikes are naturally suited for hybrid computation, i.e., computation that is not purely analog or purely digital but that is a mixture of both forms. Our converter provides an example for the use of such spike-based hybrid computation. The converter is suited for compact, lowpower neuromorphic applications.