Softky, W. R., & Koch, C. (1993). The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. The Journal of Neuroscience, 13(1), 334-350.
cortical pyramidal cells show irregular firing (\(C_v \approx 1\))
irregularity can be explained by strong EPSPs, or many synchronized weak EPSPs without non-sync. input
integrator of many inputs yields regular firing (passive dendrites, AMPA)
coincidence detector shows irregular firing rates (active dendrites, NMDA)
Irregular firing suggests existence of spike code over rate code
Mainen, Z. F., & Sejnowski, T. J. (1995). Reliability of spike timing in neocortical neurons. Science, 268(5216), 1503-1506.
strong fluctuating input to cortical neurons lead to precise timing of spikes
slow inputs lead to regular spiking in neurons
unresolved: role of unreliable synapses
no proof for spike time carries information, but precise code would be possible
Shadlen, M. N., & Newsome, W. T. (1998). The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. The Journal of neuroscience, 18(10), 3870-3896.
statistics of input and output should be the same
variability in activity consists of variability in mean firing rate and variance in ISIs
common input causes variability, but necessary for fast signal transmission
Nicely elaborated relationship between \(C_v\) and spike counts variance
suggests rate code over temporal code
van Vreeswijk, C., & Sompolinsky, H. (1996). Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science, 274(5293), 1724-1726.
very simple model for balanced networks
network responds faster than membrane time constant
Brunel, N. (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of computational neuroscience, 8(3), 183-208.
Okun, M., & Lampl, I. (2008). Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities. Nature neuroscience, 11(5), 535-537.
neuronal input is strongly synchronized
excitatory and inhibitory input is balanced and correlated in time and strength
excitatory input is followed by inhibitory input with time lag of few ms
found in anesthetized and awake behaving rat
indication that temporal precise coincidence detection in neurons is possible
Poulet, J. F., & Petersen, C. C. (2008). Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice. Nature, 454(7206), 881-885.
mice pyramidal cell barrel cortex layer 2/3
membrane potential correlated during quiet wakefulness and decorrelated during whisking (internally modulated states)
APs are not synchronized, but SNR is higher during whisking (speaking for strong specific input to subpopulations)
In general: During whisking more decorrelated state of subthreshold potentials of populations, during behavior APs might be more reliable
Renart, A., de la Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A., & Harris, K. D. (2010). The asynchronous state in cortical circuits. science, 327(5965), 587-590.
Isaacson, J. S., & Scanziani, M. (2011). How inhibition shapes cortical activity. Neuron, 72(2), 231-243.
Tan, A. Y., Chen, Y., Scholl, B., Seidemann, E., & Priebe, N. J. (2014). Sensory stimulation shifts visual cortex from synchronous to asynchronous states. Nature.
Xue, Mingshan, Bassam V. Atallah, and Massimo Scanziani. “Equalizing excitation-inhibition ratios across visual cortical neurons.” Nature 511.7511 (2014): 596-600.
Gerstein, G., Bedenbaugh, P., & Aertsen, A. M. (1989). Neuronal assemblies. Biomedical Engineering, IEEE Transactions on, 36(1), 4-14.
Salinas, E., & Sejnowski, T. J. (2001). Correlated neuronal activity and the flow of neural information. Nature reviews neuroscience, 2(8), 539-550.
balanced networks are more sensitive to correlated input
correlations might act as mechanism to control the flow of information
Engel, A. K., Fries, P., & Singer, W. (2001). Dynamic predictions: oscillations and synchrony in top–down processing. Nature Reviews Neuroscience, 2(10), 704-716. This view may be obsolete.
Brown, E. N., Kass, R. E., & Mitra, P. P. (2004). Multiple neural spike train data analysis: state-of-the-art and future challenges. Nature neuroscience, 7(5), 456-461.
Harris, K. D. (2005). Neural signatures of cell assembly organization. Nature Reviews Neuroscience, 6(5), 399-407.
Averbeck, B. B., Latham, P. E., & Pouget, A. (2006). Neural correlations, population coding and computation. Nature Reviews Neuroscience, 7(5), 358-366.
Okun, M., Steinmetz, N. A., Cossell, L., Iacaruso, M. F., Ko, H., Barthó, P., ... & Harris, K. D. (2015). Diverse coupling of neurons to populations in sensory cortex. Nature, 521(7553), 511-515.
just looking at how neurons are coupled to the population activity explains a lot of the data
firing rate of neurons is stronger modulated to unspecific population rate fluctuations if they have strong coupling
in mouse V1 and monkey V4
Aertsen, A. M., Gerstein, G. L., Habib, M. K., & Palm, G. (1989). Dynamics