Optical interrogation of multi-scale neuronal plasticity underlying behavioral learning

Distributing task-related neural activity across a cortical network through task-independent connections

Task-related neural activity is widespread across populations of neurons during goal-directed behaviors. However, little is known about the synaptic reorganization and circuit mechanisms that lead to broad activity changes. Here we trained a subset of neurons in a spiking network with strong synaptic interactions to reproduce the activity of neurons in the motor cortex during a decision-making task. Task-related activity, resembling the neural data, emerged across the network, even in the untrained neurons. Analysis of trained networks showed that strong untrained synapses, which were independent of the task and determined the dynamical state of the network, mediated the spread of task-related activity. Optogenetic perturbations suggest that the motor cortex is strongly-coupled, supporting the applicability of the mechanism to cortical networks. Our results reveal a cortical mechanism that facilitates distributed representations of task-variables by spreading the activity from a subset of plastic neurons to the entire network through task-independent strong synapses.

Efficient inference of synaptic plasticity rule with Gaussian process regression

Finding the form of synaptic plasticity is critical to understanding its functions underlying learning and memory. We investigated an efficient method to infer synaptic plasticity rules in various experimental settings. We considered biologically plausible models fitting a wide range of in-vitro studies and examined the recovery of their firing-rate dependence from sparse and noisy data. Among the methods assuming low-rankness or smoothness of plasticity rules, Gaussian process regression (GPR), a nonparametric Bayesian approach, performs the best. Under the conditions measuring changes in synaptic weights directly or measuring changes in neural activities as indirect observables of synaptic plasticity, which leads to different inference problems, GPR performs well. Also, GPR could simultaneously recover multiple plasticity rules and robustly perform under various plasticity rules and noise levels. Such flexibility and efficiency, particularly at the low sampling regime, make GPR suitable for recent experimental developments and inferring a broader class of plasticity models.

Dendritic Spines in Learning and Memory: From First Discoveries to Current Insights

The central nervous system is composed of neural ensembles, and their activity patterns are neural correlates of cognitive functions. Those ensembles are networks of neurons connected to each other by synapses. Most neurons integrate synaptic signal through a remarkable subcellular structure called spine. Dendritic spines are protrusions whose diverse shapes make them appear as a specific neuronal compartment, and they have been the focus of studies for more than a century. Soon after their first description by Ramón y Cajal, it has been hypothesized that spine morphological changes could modify neuronal connectivity and sustain cognitive abilities. Later studies demonstrated that changes in spine density and morphology occurred in experience-dependent plasticity during development, and in clinical cases of mental retardation. This gave ground for the assumption that dendritic spines are the particular locus of cerebral plasticity. With the discovery of synaptic long-term potentiation, a research program emerged with the aim to establish whether dendritic spine plasticity could explain learning and memory. The development of live imaging methods revealed on the one hand that dendritic spine remodeling is compatible with learning process and, on the other hand, that their long-term stability is compatible with lifelong memories. Furthermore, the study of the mechanisms of spine growth and maintenance shed new light on the rules of plasticity. In behavioral paradigms of memory, spine formation or elimination and morphological changes were found to correlate with learning. In a last critical step, recent experiments have provided evidence that dendritic spines play a causal role in learning and memory.

Stable continual learning through structured multiscale plasticity manifolds

Biological plasticity is ubiquitous. How does the brain navigate this complex plasticity space, where any component can seemingly change, in adapting to an ever-changing environment? We build a systematic case that stable continuous learning is achieved by structured rules that enforce multiple, but not all, components to change together in specific directions. This rule-based low-dimensional plasticity manifold of permitted plasticity combinations emerges from cell type-specific molecular signaling and triggers cascading impacts that span multiple scales. These multiscale plasticity manifolds form the basis for behavioral learning and are dynamic entities that are altered by neuromodulation, metaplasticity, and pathology. We explore the strong links between heterogeneities, degeneracy, and plasticity manifolds and emphasize the need to incorporate plasticity manifolds into learning-theoretical frameworks and experimental designs.

Multi-Scale Understanding of NMDA Receptor Function in Schizophrenia

Schizophrenia is a chronic and disabling psychiatric disorder characterized by disturbances of thought, cognition, and behavior. Despite massive research efforts to date, the etiology and pathophysiology of schizophrenia remain largely unknown. The difficulty of brain research is largely a result of complex interactions between contributory factors at different scales: susceptible gene variants (molecular scale), synaptopathies (synaptic, dendritic, and cell scales), and alterations in neuronal circuits (circuit scale), which together result in behavioral manifestations (individual scale). It is likely that each scale affects the others, from the microscale to the mesoscale to the macroscale, and vice versa. Thus, to consider the intricate complexity of schizophrenia across multiple layers, we introduce a multi-scale, hierarchical view of the nature of this disorder, focusing especially on N-methyl-D-aspartate-type glutamate receptors (NMDARs). The reason for placing emphasis on NMDAR is its clinical relevance to schizophrenia, as well as its diverse functions in neurons, including the robust supralinear synaptic integration provided by N-methyl-D-aspartate-type glutamate (NMDA) spikes and the Ca²⁺ permeability of the NMDAR, which facilitates synaptic plasticity via various calcium-dependent proteins. Here, we review recent evidence implicating NMDARs in the pathophysiology of schizophrenia from the multi-scale perspective. We also discuss recent advances from optical techniques, which provide a powerful tool for uncovering the mechanisms of NMDAR synaptic pathology and their relationships, with subsequent behavioral manifestations.