Skip to main content
SpringerLink
Log in

Information Theory and Systems Neuroscience

  • Chapter
Analysis of Parallel Spike Trains

Part of the book series: Springer Series in Computational Neuroscience ((NEUROSCI,volume 7))

  • 2210 Accesses

Abstract

Information theory reveals the performance limits of communication and signal processing systems, the brain being an interesting example. However, applying this powerful theory to neural signals has many pitfalls. The problem areas are discussed and we describe how to resolve the issues. In addition, we describe modern information theoretic results pertinent to neuroscience.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Bialek W, Rieke F, de Ruyter van Steveninck RR, Warland D (1991) Reading a neural code. Science 252:1852–1856

    Article  Google Scholar 

  • Borst A, Theunissen FE (1999) Information theory and neural coding. Nature Neurosci 2:947–957

    Article  CAS  PubMed  Google Scholar 

  • Brémaud P (1981) Point processes and queues. Springer, New York

    Google Scholar 

  • Brenner N, Strong SP, Koberle R, Bialek W, de Ruyter van Steveninck RR (2000) Synergy in a neural code. Neural Comput 12:1531–1552

    Article  CAS  PubMed  Google Scholar 

  • Cover TM, Thomas JA (2006) Elements of information theory, 2nd edn. Wiley, New York

    Google Scholar 

  • Frey MR (1991) Information capacity of the Poisson channel. IEEE Trans Inform Theory 37:244–256

    Article  Google Scholar 

  • Gastpar M, Rimoldi B, Vetterli M (2003) To code, or not to code: lossy source-channel communication revisited. IEEE Trans Inform Theory 49:1147–1158

    Article  Google Scholar 

  • Johnson DH (1996) Point process models of single-neuron discharges. J Comp Neurosci 3:275–299

    Article  CAS  Google Scholar 

  • Johnson DH (2002) Four top reasons mutual information does not quantify neural information processing. In: Computational neuroscience ’02, Chicago

    Google Scholar 

  • Johnson DH (2004) Neural population structures and consequences for neural coding. J Comp Neurosci 16:69–80

    Article  Google Scholar 

  • Johnson DH, Goodman IN (2008) Inferring the capacity of the vector Poisson channel with a Bernoulli model. Network Comput Neural Syst 19:13–33

    Article  Google Scholar 

  • Johnson DH, Gruner CM, Baggerly K, Seshagiri C (2001) Information-theoretic analysis of neural coding. J Comp Neurosci 10:47–69

    Article  CAS  Google Scholar 

  • Kabanov YM (1978) The capacity of a channel of the Poisson type. Theory Probab Appl 23:143–147

    Article  Google Scholar 

  • Latham PE, Nirenberg S (2005) Synergy redundancy, and independence in population codes, revisited. J Neurosci 25:5195–5206

    Article  CAS  PubMed  Google Scholar 

  • Massey JL (1990) Causality feedback and directed information. In: Proc. 1990 intl. sym. on information theory and its applications, Waikiki

    Google Scholar 

  • McFadden JA (1965) The entropy of a point process. SIAM J Appl Math 13:988–994

    Article  Google Scholar 

  • Paninski L (2003) Estimation of entropy and mutual information. Neural Comput 15:1191–1253

    Article  Google Scholar 

  • Panzeri S, Schultz SR (2001) A unified approach to the study of temporal, correlational, and rate coding. Neural Comput 13:1311–1349

    Article  CAS  PubMed  Google Scholar 

  • Panzeri S, Schultz SR, Treves A, Rolls ET (1999) Correlations and the encoding of information in the nervous system. Proc R Soc Lond 266:1001–1012

    Article  CAS  Google Scholar 

  • Reich DS, Mechler F, Victor JD (2001) Independent and redundant information in nearby cortical neurons. Science 294:2566–2568

    Article  CAS  PubMed  Google Scholar 

  • Rozell CJ, Johnson DH, Glantz RM (2004) Measuring information transfer in crayfish sustaining fiber spike generators: methods and analysis. Biol Cybernet 90:89–97

    Article  Google Scholar 

  • Schneidman E, Bialek W, Berry MJ II (2003) Synergy, redundancy, and independence in population codes. J Neurosci 23:11539–11553

    CAS  PubMed  Google Scholar 

  • Shamai S, Verdú S (1997) Empirical distribution for good codes. IEEE Trans Inform Theory 43:836–846

    Article  Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423, 623–656

    Google Scholar 

  • Sinanović S, Johnson DH (2007) Toward a theory of information processing. Signal Process 87:1326–1344

    Article  Google Scholar 

  • Stein RB (1967) The information capacity of nerve cells using a frequency code. Biophys J 7:67–82

    Google Scholar 

  • Strong SP, Koberle R, de Ruyter van Steveninck RR, Bialek W (1998) Entropy and information in neural spike trains. Phys Rev Lett 80:197–200

    Article  CAS  Google Scholar 

  • Venkataramanan R, Pradhan SS (2005) Directed information for communication problems with common side information and delayed feedback/feedforward. In: Proc. Allerton conference on communication, control and computing

    Google Scholar 

  • Victor JD (2006) Approaches to information-theoretic analysis of neural activity. Biol Theory 1:302–316

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA

    Don H. Johnson & Ilan N. Goodman

  2. School of Electrical and Computer Engineering, Georgia Institute of Technology, 777 Atlantic Dr. NW, Atlanta, GA, 30332-0250, USA

    Christopher J. Rozell

Authors
  1. Don H. Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ilan N. Goodman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christopher J. Rozell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Don H. Johnson .

Editor information

Editors and Affiliations

  1. Computational Neuroscience Group, RIKEN Brain Science Institute, Hirosawa 2-1, Saitama, 351-0198, Japan

    Sonja Grün

  2. Bernstein-Zentrum für Computational Neur, Neuroscience, Universität Freiburg, Hansastrasse 9A 9a, Freiburg, 79104, Germany

    Stefan Rotter

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Johnson, D.H., Goodman, I.N., Rozell, C.J. (2010). Information Theory and Systems Neuroscience. In: Grün, S., Rotter, S. (eds) Analysis of Parallel Spike Trains. Springer Series in Computational Neuroscience, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5675-0_13

Download citation

Publish with us

Policies and ethics

Search

Navigation