Biomedical subjects

Antonino Raffone

Publications and source records attributed to Antonino Raffone.

4 recordsLinked to original sources

PubMed2006

Synthetic computational models of selective attention.

Computational modeling plays an important role to understand the mechanisms of attention. In this framework, synthetic computational models can uniquely contribute to integrate different explanatory levels and neurocognitive findings, with special reference to the integration of attention and awareness processes. Novel combined experimental and computational investigations can lead to important insights, as in the revived domain of neural correlates of attention- and awareness-related meditation states and traits.

Attention↗

PubMed2006

What do we learn from binding features? Evidence for multilevel feature integration.

Four experiments were conducted to investigate the relationship between the binding of visual features (as measured by their aftereffects on subsequent binding) and the learning of feature-conjunction probabilities. Both binding and learning effects were obtained, but they did not interact. Interestingly, (shape-color) binding effects disappeared with increasing practice, presumably because of the fact that only 1 of the features involved was relevant to the task. However, this instability was only observed for arbitrary, not highly overlearned combinations of simple geometric features and not for real objects (colored pictures of a banana and strawberry), where binding effects were strong and resistant to practice. These findings suggest that learning has no direct impact on the strength or resistance of bindings or on speed with which features are bound; however, learning does affect the amount of attention particular feature dimensions attract, which again can influence which features are considered in binding.

Analysis of Variance↗

PubMed2005

Recall of memory sequences by interaction of the dentate and CA3: a revised model of the phase precession.

Behavioral and electrophysiological evidence indicates that the hippocampus has a special role in the encoding and recall of memory sequences. Importantly, the hippocampal phase precession, a phenomenon recorded as a rat moves through place fields, can be interpreted as cued recall of the sequence of upcoming places. The phase precession can be recorded in all hippocampal regions, but the role of each region has been unclear. Here, we suggest how the dentate and CA3 regions can work together to learn sequences, recall sequences, and generate the phase precession. Our proposal is constrained by information regarding synaptic plasticity rules, network connectivity, timing delays and theta/gamma oscillations.

Animals↗

PubMed2003

Dynamic synchronization and chaos in an associative neural network with multiple active memories.

Associative memory dynamics in neural networks are generally based on attractors. Retrieval based on fixed-point attractors works if only one memory pattern is retrieved at the time, but cannot enable the simultaneous retrieval of more than one pattern. Stable phase-locking of periodic oscillations or limit cycle attractors leads to incorrect feature bindings if the simultaneously retrieved patterns share some of their features. We investigate retrieval dynamics of multiple active patterns in a network of chaotic model neurons. Several memory patterns are kept simultaneously active and separated from each other by a dynamic itinerant synchronization between neurons. Neurons representing shared features alternate their synchronization between patterns, thus multiplexing their binding relationships. Our model includes a mechanism for self-organized readout or decoding of memory pattern coherence in terms of short-term potentiation and short-term depression of synaptic weights.

Animals↗