Synchrony and surround modulation
(A neural correlate of conscious experience)
All three center gratings in this figure have identical physical contrast (i.e., the difference in brightness between the white and dark stripes is the same). Nevertheless, the center grating in the middle of the figure (b) appears to have least contrast. The reason for this loss of contrast is the influence produced by the larger grating that surrounds the center. Interestingly, only some surround gratings affect strongly the perception of the contrast in the center. We investigated the neuronal correlates of the perceptual changes in the contrast of grating stimuli.
It has been long known that the case in (a) correlates well with changes (or modulation) of firing rate responses to the centre stimulus. However, the most interesting case for us was the one in (c) because in this case neuronal firing rates remain suppressed strongly by the surround stimulus (i.e., the same as for (b)), but nevertheless, we perceive strong contrast in this stimulus due to the offset between the centre and surround. We discovered that the strong contrast in this case is due to increase synchronization of neuronal discharges between the neurons that are located in early visual areas and that are stimulated by the center gratings (Biederlack et al., 2006)(see the picture).
This result can be considered a neural correlated of the conscious experience of stimulus brightness (qualia). Therefore, while the absolute firing rates and synchrony in the primary visual areas do not correlate with conscious experience, the degree of modulation of these responses seems to accurately correlate with that experience.
Neural synchrony and spiking delays
For long time, the wisdom was that neuronal synchrony occurs with zero-delay precision. However, now we know that this is not true (e.g., Schneider and Nikolić, 2006). Precise neuronal synchrony occurs most commonly with small (< ~15 ms) but precise and reliable time delays detectable already from cross-correlation (Nikolić, 2007; Fries et al, 2007). More accurately, one neuron tends to fire action potentials earlier than the other one. For a given stimulus, each neuron has its own firing time relative to other neurons and to the ongoing gamma oscillations in e.g., LFP.
A number of computational functions could be achieved if i) these small time delays contain stimulus-related information and ii) this information can be decoded by cortical neurons at later stages of processing. We could demonstrate that, in cat visual cortex, these delays are stimulus dependent and can carry about as much information about stimuli as firing rates (Havenith et al., 2011).
Nikolić D., P. Fries, and W. Singer (2013)
Gamma oscillations: precise temporal coordination without a metronome.
Trends in Cognitive Sciences, 17: 54-55. doi:10.1016/j.tics.2012.12.003
Folias, S.E., S. Yu, A. Snyder, D. Nikolić, and J.E. Rubin (2013)
Synchronisation hubs in the visual cortex may arise from strong rhythmic inhibition
during gamma oscillations.
European Journal of Neuroscience, 38(6): 2864–2883.
Havenith, M. N., S. Yu, J. Biederlack, N-H. Chen, W. Singer, D. Nikolić (2011)
Synchrony makes neurons fire in sequence – and stimulus properties determine who is ahead.
Journal of Neuroscience, 31(23): 8570-8584. Supp. Mat.
Fries, P., D. Nikolić and W. Singer (2007)
The gamma cycle
Trends in Neurosciences, 30(7):309-316
Nikolić, D. (2007)
Non-parametric detection of temporal order across pairwise measurements of time delays.
Journal of Computational Neuroscience, 22 (1); 5-19
Biederlack, J., M. Castelo-Branco, S. Neuenschwander, D.W. Wheeler, W. Singer and D. Nikolić (2006)
Brightness induction: Rate enhancement and neuronal synchronization as complementary codes.
Neuron 52, 1073-1083
Schneider, G., M.N. Havenith and D. Nikolić (2006)
Spatio-temporal structure in large neuronal networks detected from cross correlation.
Neural Computation, 18(10):2387-2413
Schneider, G. and D. Nikolić (2006)
Detection and assessment of near-zero delays in neuronal spiking activity.
Journal of Neuroscience Methods, 2005, 152(1-2):97-106