Cortical response selectivity derives from strength in numbers of synapses

Abstract

Single neocortical neurons are driven by populations of excitatory inputs, forming the basis of neuronal selectivity to features of sensory input. Excitatory connections are thought to mature during development through activity-dependent Hebbian plasticity 1 , whereby similarity between presynaptic and postsynaptic activity selectively strengthens some synapses and weakens others 2 . Evidence in support of this process ranges from measurements of synaptic ultrastructure to in vitro and in vivo physiology and imaging studies 3 , 4 , 5 , 6 , 7 , 8 . These corroborating lines of evidence lead to the prediction that a small number of strong synaptic inputs drive neuronal selectivity, while weak synaptic inputs are less correlated with the somatic output and modulate activity overall 6 , 7 . Supporting evidence from cortical circuits, however, has been limited to measurements of neighboring, connected cell pairs, raising the question of whether this prediction holds for a broad range of synapses converging onto cortical neurons. Here we measure the strengths of functionally characterized excitatory inputs contacting single pyramidal neurons in ferret primary visual cortex (V1) by combining in vivo two-photon synaptic imaging and post hoc electron microscopy (EM). Using EM reconstruction of individual synapses as a metric of strength, we find no evidence that strong synapses play a predominant role in the selectivity of cortical neuron responses to visual stimuli. Instead, selectivity appears to arise from the total number of synapses activated by different stimuli. Moreover, spatial clustering of co-active inputs appears reserved for weaker synapses, enhancing the contribution of weak synapses to somatic responses. Our results challenge the role of Hebbian mechanisms in shaping neuronal selectivity in cortical circuits, and suggest that selectivity reflects the co-activation of large populations of presynaptic neurons with similar properties and a mixture of strengths.

Active dendrites enable strong but sparse inputs to determine orientation selectivity

“…Output Tuning Is Determined by Dendritic Spikes. The prevailing view of synaptic integration is that the preferred orientation, and the tuning width of the output of a neuron, are set by the sum of its synaptic inputs and the nonlinearity of AP generation (33,37,(71)(72)(73). Even in models where the distribution of synaptic weights is taken into account (18,74), a predominantly linear sum of those inputs drives somatic membrane potential fluctuations (75), and the sharpness of output tuning is effectively set by the somatic AP threshold.…”

“…Third, they could carry additional information about brain state and modulate postsynaptic response properties depending on the level of attention or motion of the animal (20,84,85). Finally, coactivation of local groups of weaker synapses could emulate the activation of a strong synapse (37), also in the recruitment of dendritic spikes (39,86,87).…”

“…We then calculated the difference in orientation preference across all synapses. To generate clusters of similarly tuned synapses, we split the dendritic tree into regions of interest (ROIs) of ∼20-μm length (mean ± SD = 19.9 ± 3.6 μm) within which experimental work has identified clustering or apparently random synaptic organization (33,36,37,53,54). We then calculated the circular dispersion (36) of the orientation preferences of synapses in each ROI.…”

Synaptic diversity naturally arises from neural decoding of heterogeneous populations

“…This creates a degeneracy where synapse count and size trade off. Because current spine imaging techniques typically capture large synapses and there is no relationship between strength and orientation preference (Scholl et al, 2021), we can assume size is fixed and convert the derived weights into a frequency distribution of 'synaptic inputs' (Fig. 4a).…”

“…5a). To characterize PSYN diversity, both for real dendritic spines and simulated inputs, we computed the Pearson correlation coefficient between the tuning curves of individual inputs and the corresponding somatic output (Scholl et al, 2021). For these comparisons, we sampled orientation space in the model spines to match our empirical measurements (22.5 deg increments) and the number of total excitatory inputs recovered for each simulated downstream neuron was set to 100, similar to the average number of visually-responsive spines recorded for each ferret V1 neuron (n = 45, n = 158.9 ± 73.2 spines/cell).…”