Search results for "Hebbian theory"
showing 4 items of 4 documents
Modulation of information processing by AMPA receptor auxiliary subunits
2020
AMPA-type glutamate receptors (AMPARs) are key molecules of neuronal communication in our brain. The discovery of AMPAR auxiliary subunits, such as proteins of the TARP, CKAMP and CNIH families, fundamentally changed our understanding of how AMPAR function is regulated. Auxiliary subunits control almost all aspects of AMPAR function in the brain. They influence AMPAR assembly, composition, structure, trafficking, subcellular localization and gating. This influence has important implications for synapse function. In the present review, we first discuss how auxiliary subunits affect the strength of synapses by modulating number and localization of AMPARs in synapses as well as their glutamate…
Synaptic scaling generically stabilizes circuit connectivity
2011
Neural systems regulate synaptic plasticity avoiding overly strong growth or shrinkage of the connections, thereby keeping the circuit architecture operational. Accordingly, several experimental studies have shown that synaptic weights increase only in direct relation to their current value, resulting in reduced growth for stronger synapses [1]. It is, however, difficult to extract from these studies unequivocal evidence about the underlying biophysical mechanisms that control weight growth. The theoretical neurosciences have addressed this problem by exploring mechanisms for synaptic weight change that contain limiting factors to regulate growth [2]. The effectiveness of these mechanisms i…
A Hebbian approach to complex-network generation
2011
Through a redefinition of patterns in an Hopfield-like model, we introduce and develop an approach to model discrete systems made up of many, interacting components with inner degrees of freedom. Our approach clarifies the intrinsic connection between the kind of interactions among components and the emergent topology describing the system itself; also, it allows to effectively address the statistical mechanics on the resulting networks. Indeed, a wide class of analytically treatable, weighted random graphs with a tunable level of correlation can be recovered and controlled. We especially focus on the case of imitative couplings among components endowed with similar patterns (i.e. attribute…
A Neurocomputational Approach to Trained and Transitive Relations in Equivalence Classes
2017
A stimulus class can be composed of perceptually different but functionally equivalent stimuli. The relations between the stimuli that are grouped in a class can be learned or derived from other stimulus relations. If stimulus A is equivalent to B, and B is equivalent to C, then the equivalence between A and C can be derived without explicit training. In this work we propose, with a neurocomputational model, a basic learning mechanism for the formation of equivalence. We also describe how the relatedness between the members of an equivalence class is developed for both trained and derived stimulus relations. Three classic studies on stimulus equivalence are simulated covering typical and at…