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@INPROCEEDINGS{vanderVlag:1033646,
      author       = {van der Vlag, Michiel and Yegenoglu, Alper and
                      Jimenez-Romero, Cristian and Diaz, Sandra and Goldman,
                      Jennifer},
      title        = {{BIO}: {L}everaging {C}onscious {D}ynamics {E}nhancing {ML}
                      {P}erformance},
      reportid     = {FZJ-2024-06518},
      year         = {2024},
      abstract     = {Exploring the Influence of Spontaneous Dynamics on
                      Reservoir Computing PerformanceInsights emerging from the
                      study of brain state-dependent neural dynamics have been
                      slow to influence the optimization and design of Machine
                      Learning (ML) and Artificial Intelligence (AI) algorithms.
                      Analysis of time signals recorded in neuroimaging studies
                      has demonstrated intricate dynamical patterns inherent to
                      conscious brain states, with complexity and proximity to
                      criticality associated with optimal cognitive performance.
                      While substantial investments are made to enhance the
                      performance of ML and AI by designing new hardware resources
                      like GPUs, supercomputers, quantum, and neuromorphic
                      machines, valuable lessons derived from studying the
                      state-dependence of the most efficient computer that we
                      know, the brain, can potentially be better incorporated into
                      ML workflows to ameliorate performance. ML research has
                      begun to achieve improved performance and enhanced learning
                      capabilities using brain-inspired classification algorithms,
                      e.g. programmed ”wake” or ”sleep” states. However,
                      to our knowledge, the implications of neuronal dynamics
                      observed specifically during conscious brain states has not
                      yet been thoroughly examined nor integrated into the design
                      or optimization of ML/AI algorithms. Reservoir Computing
                      (RC) is particularly well-suited for processing time series
                      data, due to its inherent capacity to capture temporal
                      dependencies and dynamics, as its echo state properties
                      amplify and propagate input information over time. To
                      explore the capacity of various dynamical regimes to
                      influence the performance of computations, we utilize RC to
                      solve various cognitive learning tasks implemented in
                      Neurogym (github.com/neurogym). In order to analyze time
                      series data from RC, we implement the Peturbational
                      Complexity Index (PCI) a clinical metric for discriminating
                      various levels of consciousness, and assess the fractal-like
                      dimensions making use of Detrended Fluctuation Analysis
                      (DFA). Furthermore, we analyze the dynamics of the reservoir
                      on exhibiting scale-invariance, sensitivity to
                      perturbations, and operating at a critical state, by
                      computing the Lyapunov exponents. These metrics contribute
                      to our understanding of how reservoir systems adapt and
                      organize information during the acquisition of new tasks. In
                      order to train the RC we make use of high performance
                      computing (HPC) resources and the L2L framework
                      (github.com/Meta-optimization/L2L), a Python implementation
                      of the well known ”Learning to Learn” ML paradigm. Using
                      RC enables the assessment of the complexity and criticality
                      of the signals, and the results can be applied in a
                      reversible manner to guide the network to maintain a
                      specific complexity and operate near criticality. Our
                      preliminary results indicate that, in analogy to enhanced
                      computational properties afforded to the brain during
                      conscious states, ML algorithms parameterized to support
                      near-critical and highly complex operational regimes
                      outperform the same algorithms parameterized with less
                      ”conscious-like” dynamics. This work supports the
                      hypothesis regarding the utility of reverse engineering
                      neural principles derived from the study of brain
                      state-specific dynamics to optimize the computational
                      prowess of current ML/AI algorithms.* 16. Why would
                      attending ASSC benefit your career and the ASSC community?
                      Attending the Association for the Scientific Study of
                      Consciousness (ASSC) conference presents a valuable
                      opportunity to enrich both one's career and the ASSC
                      community. Professionally, participation in ASSC facilitates
                      networking with leading researchers and practitioners in the
                      field of consciousness studies, fostering collaborations and
                      opening avenues for future research endeavors. Engaging with
                      diverse perspectives and cutting-edge research presented at
                      the conference broadens one's knowledge and understanding of
                      consciousness science, contributing to professional growth
                      and development. Furthermore, active involvement in the ASSC
                      community through conference participation, presentations,
                      and discussions allows individuals to contribute their own
                      insights, research findings, and perspectives, thereby
                      enriching the collective knowledge base of the community. By
                      actively participating in ASSC, attendees not only benefit
                      personally and professionally but also contribute to the
                      advancement of consciousness research and the broader
                      scientific community.* 17. How would receipt of the travel
                      award facilitate your attending ASSC? How are you hoping to
                      use the travel award? Receiving the travel award would
                      significantly support my attendance at ASSC by alleviating
                      financial constraints associated with travel expenses and
                      childcare arrangements. The award would enable me to cover
                      the costs of transportation, accommodation, and childcare,
                      making it feasible for me to participate fully in the
                      conference activities. A very short justification on how
                      this work benefits or is linked to the ebrain-Health project
                      The tools used and advocated on this poster presentation are
                      the Peturbational Complexity Index (PCI) a measure used in
                      neuroscience to quantify the complexity (richness and
                      diversity) of brain responses to perturbations or stimuli,
                      and assess the long-range connections via fractal-like
                      dimensions making use of Detrended Fluctuation Analysis
                      (DFA). Furthermore, the dynamics are analyzed on the
                      exhibition of scale-invariance, sensitivity to
                      perturbations, and operating at a critical state, by
                      computing the Lyapunov exponents. The development and
                      utilization of these tools within the project are crucial
                      for enhancing the project's efficacy and achieving its
                      objectives. By enabling the analysis of simulated outputs
                      from The Virtual Brain, these tools provide valuable
                      insights into brain dynamics and their relation to health
                      conditions in the context of the Digital Twin. They
                      facilitate a more thorough examination of the relationships
                      within the simulated functional traces derived from the
                      output of various instances of the model. Additionally,
                      their integration within the workflow of Work Package 8
                      demonstrates the project's commitment to leveraging advanced
                      computational techniques to advance our understanding of
                      brain health. This strategic use of tools underscores the
                      project's broader goal of improving diagnostics and
                      treatment strategies for neurological disorders. Therefore,
                      their inclusion serves as a vital component in realizing the
                      project's overarching objectives. This work benefits
                      significantly from the discourse at a conference focused on
                      dynamics by offering valuable insights into the
                      effectiveness of various analytical methods as tools for
                      understanding complex dynamic systems. Moreover, it fosters
                      networking opportunities among researchers with shared
                      interests, laying the groundwork for future collaborations
                      and enhancing the project's impact within the scientific
                      community.},
      month         = {Jul},
      date          = {2024-07-01},
      organization  = {Association of Scientific Studies of
                       Consciousness: theASSC.org, Tokyo
                       (Japan), 1 Jul 2024 - 6 Jul 2024},
      subtyp        = {After Call},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / eBRAIN-Health -
                      eBRAIN-Health - Actionable Multilevel Health Data
                      (101058516) / SLNS - SimLab Neuroscience (Helmholtz-SLNS) /
                      JL SMHB - Joint Lab Supercomputing and Modeling for the
                      Human Brain (JL SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)101058516 /
                      G:(DE-Juel1)Helmholtz-SLNS / G:(DE-Juel1)JL SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2024-06518},
      url          = {https://juser.fz-juelich.de/record/1033646},
}