UH Biocomputation Group - Network motifshttp://biocomputation.herts.ac.uk/2021-04-28T16:53:00+01:00Learning Compositional Sequences with Multiple Time Scales through a Hierarchical Network of Spiking Neurons2021-04-28T16:53:00+01:002021-04-28T16:53:00+01:00Muhammad Yaqoobtag:biocomputation.herts.ac.uk,2021-04-28:/2021/04/28/learning-compositional-sequences-with-multiple-time-scales-through-a-hierarchical-network-of-spiking-neurons.html<p class="first last">Muhammad Yaqoob's Journal Club session where he will talk about a paper "Learning Compositional Sequences with Multiple Time Scales through a Hierarchical Network of Spiking Neurons"</p>
<p>This week on Journal Club session Muhammad Yaqoob will talk about a paper "Learning Compositional Sequences with Multiple Time Scales through a Hierarchical Network of Spiking Neurons".</p>
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<p>Sequential behaviour is often compositional and organised across
multiple time scales: a set of individual elements developing on short
time scales (motifs) are combined to form longer functional sequences
(syntax). Such organisation leads to a natural hierarchy that can be
used advantageously for learning, since the motifs and the syntax can
be acquired independently. Despite mounting experimental evidence for
hierarchical structures in neuroscience, models for temporal learning
based on neuronal networks have mostly focused on serial methods.
Here, we introduce a network model of spiking neurons with a
hierarchical organisation aimed at sequence learning on multiple time
scales. Using biophysically motivated neuron dynamics and local
plasticity rules, the model can learn motifs and syntax independently.
Furthermore, the model can relearn sequences efficiently and store
multiple sequences. Compared to serial learning, the hierarchical
model displays faster learning, more flexible relearning, increased
capacity, and higher robustness to perturbations. The hierarchical
model redistributes the variability: it achieves high motif fidelity
at the cost of higher variability in the between-motif timings.</p>
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<p>Papers:</p>
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<li>A. Maes, M. Barahona, C. Clopath, <a class="reference external" href="https://doi.org/10.1371/journal.pcbi.1008866">"Learning Compositional Sequences with Multiple Time Scales through a Hierarchical Network of Spiking Neurons"</a>, 2021, PLOS Computational Biology, 17, e1008866</li>
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<p><strong>Date:</strong> 2021/04/30 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>