Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
Martin, K.A.C.: Microcircuits in visual cortex. Current Opinion in Neurobiology 12, 418–425 (2002)
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)
Zhang, K., Sejnowski, T.: Neuronal tuning: to sharpen or to broaden? Neural Computation 11, 75–84 (1999)
Albright, T.D.: Direction and orientation selectivity of neurons in visual area MT of the macaque. J. Neurophysiol. 52, 1106–1130 (1984)
Brunel, N., Nadal, J.P.: Mutual information, fisher information and population coding. Neural Computation 10, 1731–1757 (1998)
Nevado, A., Young, M., Panzeri, S.: Functional imaging and neural information coding. Neuroimage 21, 1095–1095 (2004)
Petersen, R.S., Panzeri, S., Diamond, M.E.: Population coding of stimulus location in rat somatosensory cortex. Neuron 32, 503–514 (2001)
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)
Cover, T., Thomas, J.: Elements of information theory. John Wiley, Chichester (1991)
Dayan, P., Abbott, L.F.: Theoretical Neuroscience. MIT Press, Cambridge (2001)
Panzeri, S., Treves, A.: Analytical estimates of limited sampling biases in different information measures. Network 7, 87–107 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Montemurro, M.A., Panzeri, S. (2005). Optimal Information Transmission Through Cortico-Cortical Synapses. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_7
Download citation
DOI: https://doi.org/10.1007/11550822_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28752-0
Online ISBN: 978-3-540-28754-4
eBook Packages: Computer ScienceComputer Science (R0)