Information coding in a laminar computational model of cat primary visual cortex
- 517 Downloads
Neural populations across cortical layers perform different computational tasks. However, it is not known whether information in different layers is encoded using a common neural code or whether it depends on the specific layer. Here we studied the laminar distribution of information in a large-scale computational model of cat primary visual cortex. We analyzed the amount of information about the input stimulus conveyed by the different representations of the cortical responses. In particular, we compared the information encoded in four possible neural codes: (1) the information carried by the firing rate of individual neurons; (2) the information carried by spike patterns within a time window; (3) the rate-and-phase information carried by the firing rate labelled by the phase of the Local Field Potentials (LFP); (4) the pattern-and-phase information carried by the spike patterns tagged with the LFP phase. We found that there is substantially more information in the rate-and-phase code compared with the firing rate alone for low LFP frequency bands (less than 30 Hz). When comparing how information is encoded across layers, we found that the extra information contained in a rate-and-phase code may reach 90 % in Layer 4, while in other layers it reaches only 60 %, compared to the information carried by the firing rate alone. These results suggest that information processing in primary sensory cortices could rely on different coding strategies across different layers.
KeywordsInformation coding Primary visual cortex Cortical microcircuit Phase-of-firing information Phase coding
This work was supported by EPSRC research grant EP/C010841/1.
- Adrian, E. (1928). The basis of sensations. New York: Norton.Google Scholar
- Basalyga, G., & Wennekers, T. (2009). Large-scale computational model of cat primary visual cortex. BMC Neuroscience, 10(Suppl 1), p358.Google Scholar
- Carnevale, N.T., & Hines, M.L. (2006). The NEURON book. Cambridge, UK: Cambridge University Press.Google Scholar
- Miikkulainen, R., et al. (2005). Computational maps in the visual cortex. Berlin, New York: Springer.Google Scholar
- Protopapas, A.D., et al. (1999). Simulating large networks of neurons. In C. Koch, & I. Sefev (Eds.), Methods in neuronal modeling from ions to networks (chapter 12, pp. 461–498). Cambridge, MA: MIT Press.Google Scholar
- Shannon, C.E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379–423, 623–656.Google Scholar
- Tsodyks, M., et al. (2000). Synchrony generation in recurrent networks with frequency-dependent synapses. Journal of Neuroscience, 20(1), 1–5.Google Scholar