Schematic illustration of neuronal plasticity across scales. (a) Schematic representation of key cellular elements (neuronal and glial cells) involved in the neuroplasticity (NP) process (Neuronal and Glia plasticity) as well as subcellular compartments (Synaptic, Dendritic, Axonal, Neuromuscular Plasticity). (b) Schematic diagram representing how repetitive synaptic stimulations repetitive LTPs are linked to molecular changes (Doted circle in (a)), and the generation of dendrite and spine remodeling. (c) Schematic illustration showing some of the changes in dendrite formations (dotted dendrite spines) following learning (synaptic re-wiring) and memory formation (synaptic stabilization). (d) Schematic illustration showing axonal mechanism of neuroplasticity and repair (rerouting and sprouting) following brain injury. Abbreviations: LTP, long-term potential. Source: Wikimediaꜛ (license: CC BY-SA 4.0; original figure from Gatto, 2020ꜛ).

Neural plasticity and learning are fundamental adaptive processes that enable the brain to modify its structure and function in response to experience. Shown is a simplified schematic illustration of plastic changes induced by intensive practice of a specific skill at multiple organizational levels. Top (behavior): Repeated practice improves behavioral performance, illustrated here by increased accuracy and reduced variability when practicing a motor task such as throwing darts. Middle (cortex): At the cortical level, practice leads to a reorganization of functional representations, with task relevant neural populations becoming more strongly engaged and more precisely tuned. Bottom (neuron): At the cellular level, these changes are supported by synaptic and structural plasticity, including modifications of synaptic strength and local connectivity at individual neurons. Together, these coordinated changes across scales illustrate how learning emerges from local plastic mechanisms to produce stable improvements in behavior. Source: Wikimedia Commonsꜛ (license: CC BY 3.0).

Neural population dynamics as an embedding of task structure into neural state space. (A) Behavioral paradigm illustrating a reaching movement to a single target. The monkey initiates a movement from a fixed starting position and reaches toward a specified goal. In this simple condition, the task is fully parameterized by time t along the movement trajectory. (B) Trial-averaged population firing rates of many simultaneously recorded neurons during the reach. Each trace represents the activity of one neuron as a function of time, illustrating how complex, heterogeneous single-neuron responses unfold during a structured behavior. (C) The same population activity represented as a trajectory in neural state space. At each time point, the joint activity of all recorded neurons defines a single point in a high-dimensional space, with neural dynamics corresponding to a continuous trajectory through this space. Temporal evolution of behavior is thus mapped onto a geometric path in population activity space. (D) Extension of the task to reaching movements in multiple directions. In this case, behavior is no longer described by time alone but by two task variables: Time within the movement and reach angle. (E) The resulting task manifold, here shown schematically as a low-dimensional cylinder parameterized by time and reach direction. Each point on this manifold corresponds to a specific behavioral state of the task. (F) The neural data manifold obtained from population activity.