List of Neuroscience papers

Neuroscience

Variability of systems / balanced network

  1. 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

  2. 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

  3. 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

  4. 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

  5. Brunel, N. (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of computational neuroscience, 8(3), 183-208.

  6. 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

  7. Poule