Cortical circuits obey Dale’s principle: each neuron either excites or inhibits all its postsynaptic targets. There is no known principled justification for why this must be so; in fact, Dale’s principle is considered – if at all – a mere constraint in neural network models. Here we provide a novel rationale for Dale’s principle: networks with separate excitatory (E) and inhibitory (I) populations preserve the temporal relationships between their inputs, thus preventing spurious temporal correlations that could mislead spike timing-dependent plasticity (STDP). To show this, we study a recurrent firing rate network model with arbitrary nonlinear response functions. We assume that, in line with known Hebbian mechanisms at both excitatory and inhibitory synapses, the magnitudes of recurrent synaptic weights are proportional to the covariance of pre- and postsynaptic rates, while their sign is determined by the E/I identity of the presynaptic cell. We show that this connectivity pattern is both necessary and sufficient to ensure that neural circuit output will be non-sequential, if the input has no specific temporal ordering of its elements. Conversely, if there is some specific temporal ordering of inputs to different neurons, then the neural circuit output will also have sequences that reproduce those of the input. Our theory predicts the relative degree of sequentiality of V1 responses to visual stimuli with different statistics, which we confirmed in cortical recordings: stimuli that are similar in lacking temporal ordering evoke responses that differ in their sequentiality, depending on whether V1 has been adapted to them. Our results suggest a novel and unexpected connection between the ubiquitous Dale’s principle and STDP, namely that Dale’s principle acts as a control mechanism to guarantee that STDP will act only on input-driven temporal sequences, rather than on internally generated ones.

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