Abstract
From memorizing a musical tune to navigating a well known route, many of our underlying behaviors have a strong temporal component. While the mechanisms behind the sequential nature of the underlying brain activity are likely multifarious and multi-scale, in this work we attempt to characterize to what degree some of this properties can be explained as a consequence of simple associative learning. To this end, we employ a parsimonious firing-rate attractor network equipped with the Hebbian-like Bayesian Confidence Propagating Neural Network (BCPNN) learning rule relying on synaptic traces with asymmetric temporal characteristics. The proposed network model is able to encode and reproduce temporal aspects of the input, and offers internal control of the recall dynamics by gain modulation. We provide an analytical characterisation of the relationship between the structure of the weight matrix, the dynamical network parameters and the temporal aspects of sequence recall. We also present a computational study of the performance of the system under the effects of noise for an extensive region of the parameter space. Finally, we show how the inclusion of modularity in our network structure facilitates the learning and recall of multiple overlapping sequences even in a noisy regime.
Highlights
From throwing spears in the savanna to the performance of a well rehearsed dance, human behavior reflects an intrinsic sequential structure
Inspired by our previous modelling efforts to study sequence [39] and word list learning phenomena [45] we propose here a modular attractor memory neural network model that learns sequential representations by means of the combination of the Bayesian Confidence Propagating Neural Network (BCPNN) learning mechanism [46] and asymmetrical temporal synaptic traces
We describe how learning is accomplished in the network through the use of synaptic traces and study how the temporal structure of the input is accounted for in the recall dynamics by means of the BCPNN learning rule
Summary
From throwing spears in the savanna to the performance of a well rehearsed dance, human behavior reflects an intrinsic sequential structure. In this light, is not surprising that sequential activity has been found in the neural dynamics across different anatomical brain areas such as the cortex [1,2,3,4], the basal ganglia [2, 5,6,7,8,9,10], the hippocampus [11,12,13,14,15] and the HVC area in songbirds [16, 17]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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