publications

2025

1. preprint

   Fast Dynamical Similarity Analysis

   Arman Behrad, Mitchell Ostrow, Mohammad Taha Fakharian, and 3 more authors

   arXiv preprint arXiv:2511.22828, Nov 2025

   Abs arXiv Bib HTML Code

   

   Understanding how nonlinear dynamical systems (e.g., artificial neural networks and neural circuits) process information requires comparing their underlying dynamics at scale, across diverse architectures and large neural recordings. While many similarity metrics exist, current approaches fall short for large-scale comparisons. Geometric methods are computationally efficient but fail to capture governing dynamics, limiting their accuracy. In contrast, traditional dynamical similarity methods are faithful to system dynamics but are often computationally prohibitive. We bridge this gap by combining the efficiency of geometric approaches with the fidelity of dynamical methods. We introduce fast dynamical similarity analysis (fastDSA), a computationally efficient and accurate metric for measuring (dis)similarity between nonlinear dynamical systems. FastDSA leverages modern computational tools, including random matrix theory to determine optimal system rank, novel optimization pipelines for aligning system flow fields, and Koopman embeddings. Across benchmark nonlinear systems and recurrent network models, fastDSA is robust to arbitrary coordinate choices while remaining sensitive to meaningful dynamical differences, capturing variations in system evolution that geometric methods may miss and traditional methods detect only at high computational cost. To our knowledge, fastDSA is the fastest method that retains accuracy in comparing nonlinear dynamical systems. It enables scalable, statistical analyses across diverse systems, significantly expanding the practical applicability of dynamical similarity analysis.

   @article{behrad2025fastdsa,
     title = {Fast Dynamical Similarity Analysis},
     author = {Behrad, Arman and Ostrow, Mitchell and Fakharian, Mohammad Taha and Fiete, Ila and Beste, Christian and Safavi, Shervin},
     journal = {arXiv preprint arXiv:2511.22828},
     year = {2025},
     month = nov,
     url = {https://arxiv.org/abs/2511.22828},
   }

2. CCN

   Heterogeneous Effect of Input and Task-optimization on the Dynamics of RNNs

   Mohammad Taha Fakharian^*, Alireza Ghalambor^*, Arman Behrad, and 2 more authors

   In Cognitive Computational Neuroscience (CCN), 2025

   Travel Award Abs Bib HTML

   

   true

   Reverse-engineering task-optimized recurrent neural networks (RNNs) has become a key framework in cognitive tasks to uncover mechanisms of brain computation. Tasks are often constructed as a set of inputs. Then, RNNs are optimized to achieve a set of computational sub-goals given the inputs. Then, neural dynamics in RNNs can be shaped by two major factors: the effect of input structure (defined by task) and task-based optimization or training. The former better reflects the attributes of the input to the network, while the latter better reflects the connectivity that is shaped by task-based optimization. Although both are major factors shaping the network dynamics in a task-specific fashion, how exactly these factors affect network dynamics remains elusive. Here, we investigate the effect of both factors on discriminating the neural dynamics across tasks. We systematically vary the network architecture and the input conditions, using three distinct recurrent architectures: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and vanilla RNN (V-RNN), trained on cognitive tasks from the NeuroGym library. While we observed a vast range of heterogeneity across architectures and choices of task on task-specific dynamics, we observed that task structure (rather than task-based optimization of the connectivity) almost dominantly informs about task-specific dynamics.

   @inproceedings{fakharian2025heterogeneous,
     title = {Heterogeneous Effect of Input and Task-optimization on the Dynamics of RNNs},
     author = {Fakharian, Mohammad Taha and Ghalambor, Alireza and Behrad, Arman and Zeraati, Roxana and Safavi, Shervin},
     booktitle = {Cognitive Computational Neuroscience (CCN)},
     address = {Amsterdam, Netherlands},
     year = {2025},
     url = {https://2025.ccneuro.org/abstract_pdf/Fakharian_2025_Heterogeneous_Effect_Input_Task-optimization_Dynamics_Recurrent.pdf},
   }