Neural computations support stable behavior despite relying on many dynamically changing biological processes. One such process is representational drift (RD), which describes changes in neurons' response profile over the timescale of minutes to weeks. Across many brain areas, neurons change their tuning or even stop/start being active, while population encoding and behavior stays intact. Generally, RD is believed to be caused by changes in synaptic weights. These changes impact the population readout and consequently require adaptation of downstream areas to maintain stable function, a costly and non-local problem. Here we propose that much of the observed drift phenomenon can be explained by a simpler mechanism: changes in the excitability of cells without changes in synaptic weights. Fluctuations in excitability due to intrinsic homeostatic properties or neuromodulation can occur at different timescales and change individual neuron’s response gain. Here we show that given recurrent connections, such excitability changes can also change the apparent tuning of neurons while leaving population readouts in downstream areas intact. We use spike coding networks (SCN) to show that the extent of these tuning shifts matches experimentally observed changes and that a general decoder can perform near-optimal across excitability changes. This suggests that experimentally observed decline in decoder accuracy across sessions may be due to overfitting of the decoder to one particular population configuration (i.e. the experimental session it was trained on), while downstream brain areas could maintain accurate behavior through a general decoder.