Optimal Information Transmission Through Cortico-Cortical Synapses

  • Marcelo A. Montemurro
  • Stefano Panzeri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3696)


Neurons in visual cortex receive a large fraction of their inputs from other cortical neurons with a similar stimulus preference. Here we use models of neuronal population activity and information theoretic tools to investigate whether this arrangement of synapses allows efficient information transmission. We find that efficient information transmission requires that the tuning curve of the afferent neurons is approximately as wide as the spread of stimulus preferences of the afferent neurons reaching a target neuron. This is compatible with present neurophysiological evidence from visual cortex. We thus suggest that the organization of V1 cortico-cortical synaptic inputs allows optimal information transmission.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Roerig, B., Kao, J.P.Y.: Organization of intracortical circuits in relation to direction preference maps in ferret visual cortex. J. Neurosci. 19(24), RC44 (1999)Google Scholar
  2. 2.
    Yousef, T., Bonhoeffer, T., Kim, D.-S., Eysel, U.T., Tóth, É., Kisvárday, Z.F.: Orientation topography of layer 4 lateral networks revealed by optical imaging in cat visual cortex (area 18). European J. Neurosci. 11, 4291–4308 (1999)CrossRefGoogle Scholar
  3. 3.
    Roerig, B., Chen, B.: Relations of local inhibitory and excitatory circuits to orientation preference maps in ferret visual cortex. Cerebral Cortex 12, 187–198 (2002)CrossRefGoogle Scholar
  4. 4.
    Martin, K.A.C.: Microcircuits in visual cortex. Current Opinion in Neurobiology 12, 418–425 (2002)CrossRefGoogle Scholar
  5. 5.
    Montemurro, M.A., Panzeri, S.: Optimal information decoding from neuronal populations with specific stimulus selectivity. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems 17. MIT Press, Cambridge (2005)Google Scholar
  6. 6.
    Zhang, K., Sejnowski, T.: Neuronal tuning: to sharpen or to broaden? Neural Computation 11, 75–84 (1999)CrossRefGoogle Scholar
  7. 7.
    Albright, T.D.: Direction and orientation selectivity of neurons in visual area MT of the macaque. J. Neurophysiol. 52, 1106–1130 (1984)Google Scholar
  8. 8.
    Brunel, N., Nadal, J.P.: Mutual information, fisher information and population coding. Neural Computation 10, 1731–1757 (1998)CrossRefGoogle Scholar
  9. 9.
    Nevado, A., Young, M., Panzeri, S.: Functional imaging and neural information coding. Neuroimage 21, 1095–1095 (2004)CrossRefGoogle Scholar
  10. 10.
    Petersen, R.S., Panzeri, S., Diamond, M.E.: Population coding of stimulus location in rat somatosensory cortex. Neuron 32, 503–514 (2001)CrossRefGoogle Scholar
  11. 11.
    Usrey, W.M., Sceniak, M.P., Chapman, B.: Receptive fields and response properties of neurons in layer 4 of ferret visual cortex. J. Neurophysiol. 89, 1003–1015 (2003)CrossRefGoogle Scholar
  12. 12.
    Cover, T., Thomas, J.: Elements of information theory. John Wiley, Chichester (1991)zbMATHCrossRefGoogle Scholar
  13. 13.
    Dayan, P., Abbott, L.F.: Theoretical Neuroscience. MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  14. 14.
    Panzeri, S., Treves, A.: Analytical estimates of limited sampling biases in different information measures. Network 7, 87–107 (1996)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Marcelo A. Montemurro
    • 1
  • Stefano Panzeri
    • 1
  1. 1.Faculty of Life SciencesThe University of ManchesterManchesterUK

Personalised recommendations