Dendritic integration of sensory and reward information facilitates learning

Learning goal-directed behaviours requires integration of separate information streams representing context, relevant stimuli and reward. Dendrites of pyramidal neurons are suitable sites for such integration, but it remains elusive how their responses adapt when an animal learns a new task. Here, we identify two distinct classes of dendritic responses that represent either contextual/sensory information or reward information and that differ in their task- and learning-related dynamics. Using longitudinal calcium imaging of apical dendritic tufts of L5 pyramidal neurons in mouse barrel cortex, we tracked dendritic activity across learning and analyzed both local dendritic branch signals and global apical tuft activity. During texture discrimination learning, sensory representations (including contextual and touch information) strengthened and converged on the reward-predicting tactile stimulus when mice became experts. In contrast, reward-associated responses were particularly strong in the naive condition and became less pronounced upon learning. When we blocked the representation of unexpected reward in naive animals with optogenetic inhibition, animals failed to learn until we released the block and learning proceeded normally. Our results suggest that reward signals in dendrites are essential for adjusting neuronal integration of converging inputs to facilitate adaptive behaviour.

The Spatial Scale of Synaptic Protein Allocation during Homeostatic Plasticity

An individual neuron hosts up to 10,000 individual synapses that can be made stronger or weaker by local and cell-wide plasticity mechanisms, both of which require protein synthesis. To address over what spatial scale a neuron allocates synaptic resources, we quantified the distribution of newly synthesized proteins after global homeostatic upscaling using metabolic labeling and single-molecule localization (DNA-PAINT). Following upscaling, we observed a global increase in locally synthesized nascent protein in synapses and at dendrites, with a high degree of variability between individual synapses. We determined the smallest spatial scale over which nascent proteins were evenly distributed and found that it is best described by synaptic neighborhoods (~ 10 microns in length)-smaller than a dendritic branch and larger than an individual synapse. Protein allocation at the level of neighborhoods thus represents a solution to the problem of protein allocation within a neuron that balances local autonomy and global homeostasis.

Inhibition enhances spatially-specific calcium encoding of synaptic input patterns in a biologically constrained model

Synaptic plasticity, which underlies learning and memory, depends on calcium elevation in neurons, but the precise relationship between calcium and spatiotemporal patterns of synaptic inputs is unclear. Here, we develop a biologically realistic computational model of striatal spiny projection neurons with sophisticated calcium dynamics, based on data from rodents of both sexes, to investigate how spatiotemporally clustered and distributed excitatory and inhibitory inputs affect spine calcium. We demonstrate that coordinated excitatory synaptic inputs evoke enhanced calcium elevation specific to stimulated spines, with lower but physiologically relevant calcium elevation in nearby non-stimulated spines. Results further show a novel and important function of inhibition—to enhance the difference in calcium between stimulated and non-stimulated spines. These findings suggest that spine calcium dynamics encode synaptic input patterns and may serve as a signal for both stimulus-specific potentiation and heterosynaptic depression, maintaining balanced activity in a dendritic branch while inducing pattern-specific plasticity.

Detailed dendritic excitatory/inhibitory balance through heterosynaptic spike-timing-dependent plasticity

Balance between excitatory and inhibitory inputs is a key feature of cortical dynamics. Such balance is arguably preserved in dendritic branches, yet its underlying mechanism and functional roles remain unknown. Here, by considering computational models of heterosynaptic spike-timing-dependent plasticity (STDP), we show that the detailed excitatory/inhibitory balance on dendritic branch is robustly achieved through heterosynaptic interaction between excitatory and inhibitory synapses. The model well reproduces experimental results on heterosynaptic STDP, and provides analytical insights. Furthermore, heterosynaptic STDP explains how maturation of inhibitory neurons modulates selectivity of excitatory neurons in critical period plasticity of binocular matching. Our results propose heterosynaptic STDP as a critical factor in synaptic organization and resultant dendritic computation.Significance statementRecent experimental studies have revealed that relative spike timings among neighboring Glutamatergic and GABAergic synapses on a dendritic branch significantly influences changes in synaptic efficiency of these synapses. This heterosynaptic form of spike-timing-dependent plasticity (STDP) is potentially important for shaping the synaptic organization and computation of neurons, but its functional role remains elusive. Here, through computational modeling, we show that heterosynaptic plasticity causes the detailed balance between excitatory and inhibitory inputs on the dendrite, at the parameter regime where previous experimental results are well reproduced. Our result reveals a potential principle of GABA-driven neural circuit formation.