---
_id: '9362'
abstract:
- lang: eng
  text: A central goal in systems neuroscience is to understand the functions performed
    by neural circuits. Previous top-down models addressed this question by comparing
    the behaviour of an ideal model circuit, optimised to perform a given function,
    with neural recordings. However, this requires guessing in advance what function
    is being performed, which may not be possible for many neural systems. To address
    this, we propose an inverse reinforcement learning (RL) framework for inferring
    the function performed by a neural network from data. We assume that the responses
    of each neuron in a network are optimised so as to drive the network towards ‘rewarded’
    states, that are desirable for performing a given function. We then show how one
    can use inverse RL to infer the reward function optimised by the network from
    observing its responses. This inferred reward function can be used to predict
    how the neural network should adapt its dynamics to perform the same function
    when the external environment or network structure changes. This could lead to
    theoretical predictions about how neural network dynamics adapt to deal with cell
    death and/or varying sensory stimulus statistics.
acknowledgement: The authors would like to thank Ulisse Ferrari for useful discussions
  and feedback.
article_number: e0248940
article_processing_charge: No
article_type: original
author:
- first_name: Matthew J
  full_name: Chalk, Matthew J
  id: 2BAAC544-F248-11E8-B48F-1D18A9856A87
  last_name: Chalk
  orcid: 0000-0001-7782-4436
- first_name: Gašper
  full_name: Tkačik, Gašper
  id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
  last_name: Tkačik
  orcid: 0000-0002-6699-1455
- first_name: Olivier
  full_name: Marre, Olivier
  last_name: Marre
citation:
  ama: Chalk MJ, Tkačik G, Marre O. Inferring the function performed by a recurrent
    neural network. <i>PLoS ONE</i>. 2021;16(4). doi:<a href="https://doi.org/10.1371/journal.pone.0248940">10.1371/journal.pone.0248940</a>
  apa: Chalk, M. J., Tkačik, G., &#38; Marre, O. (2021). Inferring the function performed
    by a recurrent neural network. <i>PLoS ONE</i>. Public Library of Science. <a
    href="https://doi.org/10.1371/journal.pone.0248940">https://doi.org/10.1371/journal.pone.0248940</a>
  chicago: Chalk, Matthew J, Gašper Tkačik, and Olivier Marre. “Inferring the Function
    Performed by a Recurrent Neural Network.” <i>PLoS ONE</i>. Public Library of Science,
    2021. <a href="https://doi.org/10.1371/journal.pone.0248940">https://doi.org/10.1371/journal.pone.0248940</a>.
  ieee: M. J. Chalk, G. Tkačik, and O. Marre, “Inferring the function performed by
    a recurrent neural network,” <i>PLoS ONE</i>, vol. 16, no. 4. Public Library of
    Science, 2021.
  ista: Chalk MJ, Tkačik G, Marre O. 2021. Inferring the function performed by a recurrent
    neural network. PLoS ONE. 16(4), e0248940.
  mla: Chalk, Matthew J., et al. “Inferring the Function Performed by a Recurrent
    Neural Network.” <i>PLoS ONE</i>, vol. 16, no. 4, e0248940, Public Library of
    Science, 2021, doi:<a href="https://doi.org/10.1371/journal.pone.0248940">10.1371/journal.pone.0248940</a>.
  short: M.J. Chalk, G. Tkačik, O. Marre, PLoS ONE 16 (2021).
date_created: 2021-05-02T22:01:28Z
date_published: 2021-04-15T00:00:00Z
date_updated: 2023-10-18T08:17:42Z
day: '15'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0248940
external_id:
  isi:
  - '000641474900072'
  pmid:
  - '33857170'
file:
- access_level: open_access
  checksum: c52da133850307d2031f552d998f00e8
  content_type: application/pdf
  creator: kschuh
  date_created: 2021-05-04T13:22:19Z
  date_updated: 2021-05-04T13:22:19Z
  file_id: '9371'
  file_name: 2021_pone_Chalk.pdf
  file_size: 2768282
  relation: main_file
  success: 1
file_date_updated: 2021-05-04T13:22:19Z
has_accepted_license: '1'
intvolume: '        16'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLoS ONE
publication_identifier:
  eissn:
  - '19326203'
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Inferring the function performed by a recurrent neural network
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 16
year: '2021'
...
---
_id: '9821'
abstract:
- lang: eng
  text: Heart rate variability (hrv) is a physiological phenomenon of the variation
    in the length of the time interval between consecutive heartbeats. In many cases
    it could be an indicator of the development of pathological states. The classical
    approach to the analysis of hrv includes time domain methods and frequency domain
    methods. However, attempts are still being made to define new and more effective
    hrv assessment tools. Persistent homology is a novel data analysis tool developed
    in the recent decades that is rooted at algebraic topology. The Topological Data
    Analysis (TDA) approach focuses on examining the shape of the data in terms of
    connectedness and holes, and has recently proved to be very effective in various
    fields of research. In this paper we propose the use of persistent homology to
    the hrv analysis. We recall selected topological descriptors used in the literature
    and we introduce some new topological descriptors that reflect the specificity
    of hrv, and we discuss their relation to the standard hrv measures. In particular,
    we show that this novel approach provides a collection of indices that might be
    at least as useful as the classical parameters in differentiating between series
    of beat-to-beat intervals (RR-intervals) in healthy subjects and patients suffering
    from a stroke episode.
acknowledgement: We express our gratitude to the anonymous referees who provided constructive
  comments that helped us improve the quality of the paper.
article_number: e0253851
article_processing_charge: Yes
article_type: original
author:
- first_name: Grzegorz
  full_name: Graff, Grzegorz
  last_name: Graff
- first_name: Beata
  full_name: Graff, Beata
  last_name: Graff
- first_name: Pawel
  full_name: Pilarczyk, Pawel
  id: 3768D56A-F248-11E8-B48F-1D18A9856A87
  last_name: Pilarczyk
- first_name: Grzegorz
  full_name: Jablonski, Grzegorz
  id: 4483EF78-F248-11E8-B48F-1D18A9856A87
  last_name: Jablonski
  orcid: 0000-0002-3536-9866
- first_name: Dariusz
  full_name: Gąsecki, Dariusz
  last_name: Gąsecki
- first_name: Krzysztof
  full_name: Narkiewicz, Krzysztof
  last_name: Narkiewicz
citation:
  ama: Graff G, Graff B, Pilarczyk P, Jablonski G, Gąsecki D, Narkiewicz K. Persistent
    homology as a new method of the assessment of heart rate variability. <i>PLoS
    ONE</i>. 2021;16(7). doi:<a href="https://doi.org/10.1371/journal.pone.0253851">10.1371/journal.pone.0253851</a>
  apa: Graff, G., Graff, B., Pilarczyk, P., Jablonski, G., Gąsecki, D., &#38; Narkiewicz,
    K. (2021). Persistent homology as a new method of the assessment of heart rate
    variability. <i>PLoS ONE</i>. Public Library of Science. <a href="https://doi.org/10.1371/journal.pone.0253851">https://doi.org/10.1371/journal.pone.0253851</a>
  chicago: Graff, Grzegorz, Beata Graff, Pawel Pilarczyk, Grzegorz Jablonski, Dariusz
    Gąsecki, and Krzysztof Narkiewicz. “Persistent Homology as a New Method of the
    Assessment of Heart Rate Variability.” <i>PLoS ONE</i>. Public Library of Science,
    2021. <a href="https://doi.org/10.1371/journal.pone.0253851">https://doi.org/10.1371/journal.pone.0253851</a>.
  ieee: G. Graff, B. Graff, P. Pilarczyk, G. Jablonski, D. Gąsecki, and K. Narkiewicz,
    “Persistent homology as a new method of the assessment of heart rate variability,”
    <i>PLoS ONE</i>, vol. 16, no. 7. Public Library of Science, 2021.
  ista: Graff G, Graff B, Pilarczyk P, Jablonski G, Gąsecki D, Narkiewicz K. 2021.
    Persistent homology as a new method of the assessment of heart rate variability.
    PLoS ONE. 16(7), e0253851.
  mla: Graff, Grzegorz, et al. “Persistent Homology as a New Method of the Assessment
    of Heart Rate Variability.” <i>PLoS ONE</i>, vol. 16, no. 7, e0253851, Public
    Library of Science, 2021, doi:<a href="https://doi.org/10.1371/journal.pone.0253851">10.1371/journal.pone.0253851</a>.
  short: G. Graff, B. Graff, P. Pilarczyk, G. Jablonski, D. Gąsecki, K. Narkiewicz,
    PLoS ONE 16 (2021).
date_created: 2021-08-08T22:01:28Z
date_published: 2021-07-01T00:00:00Z
date_updated: 2023-08-10T14:21:42Z
day: '01'
ddc:
- '006'
department:
- _id: HeEd
doi: 10.1371/journal.pone.0253851
external_id:
  isi:
  - '000678124900050'
  pmid:
  - '34292957'
file:
- access_level: open_access
  checksum: 0277aa155d5db1febd2cb384768bba5f
  content_type: application/pdf
  creator: asandaue
  date_created: 2021-08-09T09:25:41Z
  date_updated: 2021-08-09T09:25:41Z
  file_id: '9832'
  file_name: 2021_PLoSONE_Graff.pdf
  file_size: 2706919
  relation: main_file
  success: 1
file_date_updated: 2021-08-09T09:25:41Z
has_accepted_license: '1'
intvolume: '        16'
isi: 1
issue: '7'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLoS ONE
publication_identifier:
  eissn:
  - '19326203'
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Persistent homology as a new method of the assessment of heart rate variability
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 16
year: '2021'
...