Synapsisirakko

Synapsisirakko is a theoretical construct in neuroscience and cognitive science that describes a puzzle-like organization of synaptic connectivity. In this framework, the structure of synapses is shaped not only by activity-dependent potentiation and depression but also by solving a local- and global-constraint puzzle intended to optimize information transfer and metabolic efficiency.

The model envisions synaptic changes as the outcome of a constraint-satisfaction process. Each neuron adjusts its incoming weights to satisfy a set of local constraints representing features of inputs, while a higher-level objective encourages modularity and coherent representation across the network. Plasticity rules combine Hebbian-like learning with infrequent reconfiguration events that resemble solving a jigsaw puzzle, allowing the network to reorganize its structure in response to changing demands.

Mathematically, the approach can be described as minimizing a composite cost function that balances reconstruction accuracy, sparsity, energy consumption, and modularity. The optimization is performed through asynchronous updates and local computations, with the “pieces” of the puzzle corresponding to functional assemblies of neurons. When pieces fit together, the resulting representations are intended to be robust, reusable, and scalable across tasks.

Origins and status: Synapsisirakko emerged in theoretical discussions during the 2010s as a way to capture how anatomical and energetic constraints might shape learning. It has been explored primarily in computer simulations to study the emergence of modularity and efficient coding under constraint-based learning. There is no broad consensus on its empirical applicability.

See also: Hebbian learning, synaptic plasticity, modularity in neural networks, constraint satisfaction problems, energy-based models.