Synaptic weights that correlate with presynaptic selectivity increase decoding performance

Author summaryBrains are composed of complex networks, with communication taking place along synaptic connections between neurons. These connections can change and adapt, a process we call "plasticity". However, the specific rules that dictate these changes remain largely unknown. In our visual system, which is responsible for vision and perception, it is primarily thought that connections get stronger between neurons exhibiting similar activity, also known as 'Hebbian plasticity'. A recent study revealed results that seemed to contradict this idea, showing a strength in numbers of synapses rather than strength. Prompted by these findings, we developed a computational model with a hypothesis about how these changes could occur. We discovered that this new model, with a plasticity mechanism based on presynaptic activity, could capture experimental findings and lead to benefits in population decoding. Our model doesn't necessarily contradict a Hebbian model, but rather, likely co-exists with it. The activity of neurons in the visual cortex is often characterized by tuning curves, which are thought to be shaped by Hebbian plasticity during development and sensory experience. This leads to the prediction that neural circuits should be organized such that neurons with similar functional preference are connected with stronger weights. In support of this idea, previous experimental and theoretical work have provided evidence for a model of the visual cortex characterized by such functional subnetworks. A recent experimental study, however, have found that the postsynaptic preferred stimulus was defined by the total number of spines activated by a given stimulus and independent of their individual strength. While this result might seem to contradict previous literature, there are many factors that define how a given synaptic input influences postsynaptic selectivity. Here, we designed a computational model in which postsynaptic functional preference is defined by the number of inputs activated by a given stimulus. Using a plasticity rule where synaptic weights tend to correlate with presynaptic selectivity, and is independent of functional-similarity between pre- and postsynaptic activity, we find that this model can be used to decode presented stimuli in a manner that is comparable to maximum likelihood inference.