In 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. 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.
Model Type: Realistic Network
Cell Type(s): Abstract integrate-and-fire leaky neuron
Model Concept(s): Place cell/field; Direction Selectivity; Persistent activity
Simulation Environment: PyNN; Python
References:
Coppolino S, Giacopelli G, Migliore M. (2021). Sequence learning in a single trial: a spiking neurons model based on hippocampal circuitry IEEE Transactions on Neural Networks and Learning Systems (in press).