Is there a signal in the noise?

Shadlen, Michael N.; Newsome, William T.

doi:10.1016/0959-4388(95)80033-6

Cited by 137 publications

(52 citation statements)

References 5 publications

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“…It is still debated how spike train characteristics represent content in the cerebral cortex [97][98][99][100]. The rate-coding hypothesis emphasizes the mean firing rate in carrying information [101].…”

Section: Effects Of Parametric Variation Of Spike Patterns On Movemenmentioning

confidence: 99%

What single-cell stimulation has told us about neural coding

Doron

Brecht

2015

Phil. Trans. R. Soc. B

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One contribution of 15 to a theme issue 'Controlling brain activity to alter perception, behaviour and society'. In recent years, single-cell stimulation experiments have resulted in substantial progress towards directly linking single-cell activity to movement and sensation. Recent advances in electrical recording and stimulation techniques have enabled control of single neuron spiking in vivo and have contributed to our understanding of neuronal coding schemes in the brain. Here, we review single neuron stimulation effects in different brain structures and how they vary with artificially inserted spike patterns. We briefly compare single neuron stimulation with other brain stimulation techniques. A key advantage of single neuron stimulation is the precise control of the evoked spiking patterns. Systematically varying spike patterns and measuring evoked movements and sensations enables 'decoding' of the single-cell spike patterns and provides insights into the readout mechanisms of sensory and motor cortical spikes.

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Section: Effects Of Parametric Variation Of Spike Patterns On Movemenmentioning

confidence: 99%

What single-cell stimulation has told us about neural coding

Doron

Brecht

2015

Phil. Trans. R. Soc. B

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“…The ⌬t of the model has a functional interpretation: it represents the refractory period, because only one spike is allowed per ⌬t. In this mode the neuron fires because there are fluctuations in the numbers of excitatory and inhibitory input spikes that arrive per ⌬t, even though on average excitatory and inhibitory contributions balance each other out (Smith, 1992;Shadlen and Newsome, 1995;Bell et al, 1995). If the fluctuations are large, the average drive may even be negative, and this will not prevent the neuron from firing.…”

Section: Two Output Modes: Mean Excitatory Drive Versus Fluctuationsmentioning

confidence: 99%

Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models

2000

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Cortical neurons are typically driven by thousands of synaptic inputs. The arrival of a spike from one input may or may not be correlated with the arrival of other spikes from different inputs. How does this interdependence alter the probability that the postsynaptic neuron will fire? We constructed a simple random walk model in which the membrane potential of a target neuron fluctuates stochastically, driven by excitatory and inhibitory spikes arriving at random times. An analytic expression was derived for the mean output firing rate as a function of the firing rates and pairwise correlations of the inputs. This stochastic model made three quantitative predictions. (1) Correlations between pairs of excitatory or inhibitory inputs increase the fluctuations in synaptic drive, whereas correlations between excitatory-inhibitory pairs decrease them. (2) When excitation and inhibition are fully balanced (the mean net synaptic drive is zero), firing is caused by the fluctuations only. (3) In the balanced case, firing is irregular. These theoretical predictions were in excellent agreement with simulations of an integrate-and-fire neuron that included multiple conductances and received hundreds of synaptic inputs. The results show that, in the balanced regime, weak correlations caused by signals shared among inputs may have a multiplicative effect on the input-output rate curve of a postsynaptic neuron, i.e. they may regulate its gain; in the unbalanced regime, correlations may increase firing probability mainly around threshold, when output rate is low; and in all cases correlations are expected to increase the variability of the output spike train. Key words: random-walk; integrate-and-fire; computer simulation; spike synchrony; oscillations; cross-correlation; balanced inhibition; cerebral cortexThe output of a typical cortical neuron depends on the activity of a large number of synaptic inputs-several thousands of them, as estimated by anatomical techniques (White, 1989; Braitenberg and Shüz, 1997). What kind of response should be expected from a postsynaptic neuron driven by so many inputs? Answering this question in detail requires a deep understanding of dendritic integration, synaptic function, and spike generation mechanisms; but, given the large numbers commonly involved, as a first approximation it is natural to cast the problem in statistical terms. The strategy then is to compute the output responses of a model neuron (or their statistics), given a set of driving inputs with known statistical properties. These inputs may be either independent or temporally correlated. In the latter case, spikes from different input neurons arrive close together in time more often or less often than expected by chance.In general, the situation with independent inputs is easier to analyze, and for many applications it is probably a good approximation. However, there are at least three reasons why the effects of correlations on single cells should be fully characterized. First, correlations in spike counts have indeed been o...

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“…At one extreme, the input to the neuron consists of many small uncorrelated postsynaptic potentials (PSPs) summed together; in this case, the membrane potential is Gaussian distributed and follows a "random walk." Such models have been widely used to describe the inputs to cortical neurons in both theoretical (Gerstein and Mandelbrot, 1964;Calvin and Stevens, 1967;Softky and Koch, 1993;Shadlen and Newsome, 1994;Shadlen and Newsome, 1995;van Vreeswijk and Sompolinsky, 1998;Song et al, 2000;Fellous et al, 2003;Rudolph and Destexhe, 2003) and experimental Carandini, 2004) studies but have yet to be experimentally tested in auditory cortex. At the other extreme, the presynaptic population is highly correlated; neurons might be silent most of the time, except during brief moments when large groups of them fire in a concerted manner, which would elicit rare large excursions (or "bumps") of the membrane potential of the postsynaptic neuron.…”

Section: Introductionmentioning

confidence: 99%

Non-Gaussian Membrane Potential Dynamics Imply Sparse, Synchronous Activity in Auditory Cortex

DeWeese¹,

Zador²

2006

J. Neurosci.

148

134

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Many models of cortical dynamics have focused on the high-firing regime, in which neurons are driven near their maximal rate. Here we consider the responses of neurons in auditory cortex under typical low-firing rate conditions, when stimuli have not been optimized to drive neurons maximally. We used whole-cell patch-clamp recording in vivo to measure subthreshold membrane potential fluctuations in rat primary auditory cortex in both the anesthetized and awake preparations. By analyzing the subthreshold membrane potential dynamics on single trials, we made inferences about the underlying population activity. We found that, during both spontaneous and evoked responses, membrane potential was highly non-Gaussian, with dynamics consisting of occasional large excursions (sometimes tens of millivolts), much larger than the small fluctuations predicted by most random walk models that predict a Gaussian distribution of membrane potential. Thus, presynaptic inputs under these conditions are organized into quiescent periods punctuated by brief highly synchronous volleys, or "bumps." These bumps were typically so brief that they could not be well characterized as "up states" or "down states." We estimate that hundreds, perhaps thousands, of presynaptic neurons participate in the largest volleys. These dynamics suggest a computational scheme in which spike timing is controlled by concerted firing among input neurons rather than by small fluctuations in a sea of background activity.

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Is there a signal in the noise?

Cited by 137 publications

References 5 publications

What single-cell stimulation has told us about neural coding

What single-cell stimulation has told us about neural coding

Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models

Non-Gaussian Membrane Potential Dynamics Imply Sparse, Synchronous Activity in Auditory Cortex

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