A stable proportion of Purkinje cell inputs from parallel fibers are silent during cerebellar maturation

Cerebellar Purkinje neurons integrate information transmitted at excitatory synapses formed by granule cells. Although these synapses are considered essential sites for learning, most of them appear not to transmit any detectable electrical information and have been defined as silent. It has been proposed that silent synapses are required to maximize information storage capacity and ensure its reliability, and hence to optimize cerebellar operation. Such optimization is expected to occur once the cerebellar circuitry is in place, during its maturation and the natural and steady improvement of animal agility. We therefore investigated whether the proportion of silent synapses varies over this period, from the third to the sixth postnatal week in mice. Selective expression of a calcium indicator in granule cells enabled quantitative mapping of presynaptic activity, while postsynaptic responses were recorded by patch clamp in acute slices. Through this approach and the assessment of two anatomical features (the distance that separates adjacent planar Purkinje dendritic trees and the synapse density), we determined the average excitatory postsynaptic potential per synapse. Its value was four to eight times smaller than responses from paired recorded detectable connections, consistent with over 70% of synapses being silent. These figures remained remarkably stable across maturation stages. According to the proposed role for silent synapses, our results suggest that information storage capacity and reliability are optimized early during cerebellar maturation. Alternatively, silent synapses may have roles other than adjusting the information storage capacity and reliability.

Role of NMDAR plasticity in a computational model of synaptic memory

A largely unexplored question in neuronal plasticity is whether synapses are capable of encoding and learning the timing of synaptic inputs. We address this question in a computational model of synaptic input time difference learning (SITDL), where N‐methyl‐d‐aspartate receptor (NMDAR) isoform expression in silent synapses is affected by time differences between glutamate and voltage signals. We suggest that differences between NMDARs’ glutamate and voltage gate conductances induce modifications of the synapse’s NMDAR isoform population, consequently changing the timing of synaptic response. NMDAR expression at individual synapses can encode the precise time difference between signals. Thus, SITDL enables the learning and reconstruction of signals across multiple synapses of a single neuron. In addition to plausibly predicting the roles of NMDARs in synaptic plasticity, SITDL can be usefully applied in artificial neural network models.

Regulation of hippocampal excitatory synapses by the Zdhhc5 palmitoyl acyl transferase

Palmitoylation is the most common post-translational lipid modification in the brain; however, the role of palmitoylation and palmitoylating enzymes in the nervous system remains elusive. One of these enzymes, Zdhhc5, has previously been shown to regulate synapse plasticity. Here, we report that Zdhhc5 is also essential for the formation of excitatory, but not inhibitory synapses both in vitro and in vivo. We demonstrate in vitro that this is dependent on Zdhhc5's enzymatic activity, its localization at the plasma membrane, and its C-terminal domain which has been shown to be truncated in a patient with schizophrenia. Loss of Zdhhc5 in mice results in a decrease in the density of excitatory hippocampal synapses accompanied by alterations in membrane capacitance and synaptic currents, consistent with an overall decrease in spine number and silent synapses. These findings reveal an important role for Zdhhc5 in the formation and/or maintenance of excitatory synapses.

Spine dynamics of PSD-95-deficient neurons in the visual cortex link silent synapses to structural cortical plasticity

Critical periods (CPs) are time windows of heightened brain plasticity during which experience refines synaptic connections to achieve mature functionality. At glutamatergic synapses on dendritic spines of principal cortical neurons, the maturation is largely governed by postsynaptic density protein-95 (PSD-95)-dependent synaptic incorporation of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors into nascent AMPA-receptor silent synapses. Consequently, in mouse primary visual cortex (V1), impaired silent synapse maturation in PSD-95-deficient neurons prevents the closure of the CP for juvenile ocular dominance plasticity (jODP). A structural hallmark of jODP is increased spine elimination, induced by brief monocular deprivation (MD). However, it is unknown whether impaired silent synapse maturation facilitates spine elimination and also preserves juvenile structural plasticity. Using two-photon microscopy, we assessed spine dynamics in apical dendrites of layer 2/3 pyramidal neurons (PNs) in binocular V1 during ODP in awake adult mice. Under basal conditions, spine formation and elimination ratios were similar between PSD-95 knockout (KO) and wild-type (WT) mice. However, a brief MD affected spine dynamics only in KO mice, where MD doubled spine elimination, primarily affecting newly formed spines, and caused a net reduction in spine density similar to what has been observed during jODP in WT mice. A similar increase in spine elimination after MD occurred if PSD-95 was knocked down in single PNs of layer 2/3. Thus, structural plasticity is dictated cell autonomously by PSD-95 in vivo in awake mice. Loss of PSD-95 preserves hallmark features of spine dynamics in jODP into adulthood, revealing a functional link of PSD-95 for experience-dependent synapse maturation and stabilization during CPs.

Optimal learning with excitatory and inhibitory synapses

Characterizing the relation between weight structure and input/output statistics is fundamental for understanding the computational capabilities of neural circuits. In this work, I study the problem of storing associations between analog signals in the presence of correlations, using methods from statistical mechanics. I characterize the typical learning performance in terms of the power spectrum of random input and output processes. I show that optimal synaptic weight configurations reach a capacity of 0.5 for any fraction of excitatory to inhibitory weights and have a peculiar synaptic distribution with a finite fraction of silent synapses. I further provide a link between typical learning performance and principal components analysis in single cases. These results may shed light on the synaptic profile of brain circuits, such as cerebellar structures, that are thought to engage in processing time-dependent signals and performing on-line prediction.