ArticleNeural computation2021
Stimulus-Driven and Spontaneous Dynamics in Excitatory-Inhibitory Recurrent Neural Networks for Sequence Representation.
Article in Neural computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Mediodorsal thalamus regulates task uncertainty to enable cognitive flexibility.Nature communications · 2025Article
- Effect in the spectra of eigenvalues and dynamics of RNNs trained with excitatory-inhibitory constraint.Cognitive neurodynamics · 2024Article
- Neuronal travelling waves explain rotational dynamics in experimental datasets and modelling.Scientific reports · 2024Article
- A neural basis for learning sequential memory in brain loop structures.Frontiers in computational neuroscience · 2024Article
- Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies.bioRxiv : the preprint server for biology · 2023Article
- Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation.Cognitive computation · 2023Article
- Excitatory-inhibitory recurrent dynamics produce robust visual grids and stable attractors.Cell reports · 2022Article
- Retinal Processing: Insights from Mathematical Modelling.Journal of imaging · 2022Article
- A geometric framework for understanding dynamic information integration in context-dependent computation.iScience · 2021Article
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3 authors.
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Abstract
Recurrent neural networks (RNNs) have been widely used to model sequential neural dynamics ("neural sequences") of cortical circuits in cognitive and motor tasks. Efforts to incorporate biological constraints and Dale's principle will help elucidate the neural representations and mechanisms of underlying circuits. We trained an excitatory-inhibitory RNN to learn neural sequences in a supervised manner and studied the representations and dynamic attractors of the trained network. The trained RNN was robust to trigger the sequence in response to various input signals and interpolated a time-warped input for sequence representation. Interestingly, a learned sequence can repeat periodically when the RNN evolved beyond the duration of a single sequence. The eigenspectrum of the learned recurrent connectivity matrix with growing or damping modes, together with the RNN's nonlinearity, were adequate to generate a limit cycle attractor. We further examined the stability of dynamic attractors while training the RNN to learn two sequences. Together, our results provide a general framework for understanding neural sequence representation in the excitatory-inhibitory RNN.
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