6533b822fe1ef96bd127cd57
RESEARCH PRODUCT
Sequence Learning in a Single Trial: A Spiking Neurons Model Based on Hippocampal Circuitry.
Giuseppe GiacopelliMichele MiglioreSimone Coppolinosubject
Computer Networks and CommunicationsComputer scienceModels NeurologicalHippocampusAction PotentialsBrain modeling; Computer architecture; Hippocampus; Learning systems; Microprocessors; Navigation; Neurons; Persistent firing (PF); robot navigation; spike-timing-dependent-plasticity synapse; spiking neurons.Hippocampal formationHippocampus03 medical and health sciences0302 clinical medicineArtificial IntelligenceBiological neural network030304 developmental biologyNeurons0303 health sciencesSequenceSeries (mathematics)business.industryBasic cognitive functionsContrast (statistics)CognitionComputer Science ApplicationsSequence learningArtificial intelligenceNeural Networks ComputerbusinessSoftware030217 neurology & neurosurgerydescription
ABSTRACTIn contrast with our everyday experience using brain circuits, it can take a prohibitively long time to train a computational system to produce the correct sequence of outputs in the presence of a series of inputs. This suggests that something important is missing in the way in which models are trying to reproduce basic cognitive functions. In this work, we introduce a new neuronal network architecture that is able to learn, in a single trial, an arbitrary long sequence of any known objects. The key point of the model is the explicit use of mechanisms and circuitry observed in the hippocampus, which allow the model to reach a level of efficiency and accuracy that, to the best of our knowledge, is not possible with abstract network implementations. By directly following the natural system’s layout and circuitry, this type of implementation has the additional advantage that the results can be more easily compared to experimental data, allowing a deeper and more direct understanding of the mechanisms underlying cognitive functions and dysfunctions, and opening the way to a new generation of learning architectures.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-01-10 | IEEE transactions on neural networks and learning systems |