Beninger, J. | bioRxiv Science

bioRxiv2026

Functional Continuum of GABAergic Synaptic Dynamics Reflects Genetic Identities

The language of the brain is articulated by temporal patterns of neuronal activity, which individual synapses interpret through distinct forms of synaptic dynamics. While glutamatergic synapses have been proposed to use a handful of functionally distinct types of short-term plasticity (STP) that loosely align with genetic identities, the functional organization of GABAergic synaptic dynamics remains ambiguous. Here, we ask whether the diversity of GABAergic dynamics also clusters into discrete functional types or instead forms a continuum, and whether this functional organization also aligns with genetic identities. Using a combination of machine learning and synaptic modeling on the Allen Institute Synaptic Physiology Dataset, we present evidence that inhibitory dynamics organize into a functional continuum with overlapping modes corresponding to different genetic identities. This continuum spans Parvalbumin (Pvalb) to Vasoactive Intestinal Peptide (VIP) presynaptic neurons, with Somatostatin (Sst) neurons forming an intermediate class. Across the three classes, most inhibitory synapses showed either strong depression or a biphasic form of plasticity that is facilitating at higher stimulation frequencies and depressing at lower ones. However, a higher proportion of Sst and VIP synapses, compared to Pvalb synapses, exhibited either depression restricted to high frequencies or consistent facilitation. When paired with functional subtypes of excitatory synaptic dynamics, this inhibitory continuum completes a unified framework for understanding information flow in the cortical microcircuit, advancing long-standing efforts to explain circuit function by investigating the organization of its components.

neuroscience↗

bioRxiv2023

Functional Subtypes of Synaptic Dynamics in Mouse and Human

Synapses show preferential responses to particular temporal patterns of activity. Across individual synapses, there is a large degree of response heterogeneity that is informally or tacitly separated into classes, and typically only two: facilitating and depressing short-term plasticity. Here we combined a kernel-based model and machine learning techniques to infer the number and the characteristics of functionally distinct subtypes of short-term synaptic dynamics in a large dataset of glutamatergic cortical connections. To this end, we took two independent approaches. First, we used unsupervised techniques to group similar synapses into clusters. Second, we used supervised prediction of cell subclasses to reveal features of synaptic dynamics that characterized cellular genetic profiles. In rodent data, we found five clusters with a remarkable degree of convergence with the transgenic-associated subtypes. Two of these clusters corresponded to different degrees of facilitation, two corresponded to depression with different degrees of variability and one corresponded to depression-then-facilitation. Strikingly, the application of the same clustering method in human data inferred highly similar clusters to those observed in rodents, supportive of a stable clustering procedure and suggesting a homology of functional subtypes across species. This nuanced dictionary of functional subtypes shapes the heterogeneity of cortical synaptic dynamics and provides a lens into the basic motifs of information transmission in the brain.

neuroscience↗