Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin?

Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing. Author Summary: The inputs a neuron receives from its presynaptic partners strongly fluctuate as a result of either varying sensory information or ongoing intrinsic activity. To represent this wide range of signals effectively, neurons use various mechanisms that regulate the total input they receive. On the one hand, feedforward inhibition adjusts the relative contribution of individual inputs inversely proportional to the total number of active afferents, implementing a form of input normalization. On the other hand, synaptic scaling uniformly rescales the efficacy of incoming synapses to stabilize the neuron's firing rate after learning-induced changes in drive. Given that these mechanisms often act on the same neurons, we ask here if there are any benefits in combining the two. We show that the interaction between the two has important computational consequences, beyond their traditional role in maintaining network homeostasis. When combined with lateral inhibition, synaptic scaling and fast feedforward inhibition allow the circuit to learn efficiently from noisy, ambiguous inputs. For inputs not normalized by feed-forward inhibition, learning is less efficient. Given that feed-forward inhibition and synaptic scaling have been reported in various systems, our results suggest that they could generally facilitate learning in neural circuits. More broadly, our work emphasizes the importance of studying the interaction between different plasticity mechanisms for understanding circuit function.