Regulating the information in spikes: a useful bias

The bias/variance tradeoff is fundamental to learning: increasing a model’s complexity can improve its fit on training data, but potentially worsens performance on future samples [1]. Remarkably, however, the human brain effortlessly handles a wide-range of complex pattern recognition tasks. On the basis of these conflicting observations, it has been argued that useful biases in the form of “generic mechanisms for representation” must be hardwired into cortex [2].