Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex

London, Michael; Roth, Arnd; Beeren, Lisa; Häusser, Michael; Latham, Peter E.

doi:10.1038/nature09086

Cited by 421 publications

(469 citation statements)

References 38 publications

(35 reference statements)

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“…While the function of this variability is still being debated (Stein et al 2005), it is generally agreed that at least part of this variability arises from noise, which is due to the random opening of ion channels as well as the large synaptic bombardment that neurons are subjected to in vivo (Destexhe et al 2003;London et al 2010). It is possible to simply average away this variability in order to obtain an estimate of the mean response to a given stimulus, and this is frequently done when computing measures such as the peristimulus time histogram.…”

Section: Neural Codingmentioning

confidence: 99%

Nonrenewal spike train statistics: causes and functional consequences on neural coding

Ávila-Ǻkerberg

Chacron

2011

Exp Brain Res

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Many neurons display significant patterning in their spike trains (e.g. oscillations, bursting), and there is accumulating evidence that information is contained in these patterns. In many cases, this patterning is caused by intrinsic mechanisms rather than external signals. In this review, we focus on spiking activity that displays nonrenewal statistics (i.e. memory that persists from one firing to the next). Such statistics are seen in both peripheral and central neurons and appear to be ubiquitous in the CNS. We review the principal mechanisms that can give rise to nonrenewal spike train statistics. These are separated into intrinsic mechanisms such as relative refractoriness and network mechanisms such as coupling with delayed inhibitory feedback. Next, we focus on the functional roles for nonrenewal spike train statistics. These can either increase or decrease information transmission. We also focus on how such statistics can give rise to an optimal integration timescale at which spike train variability is minimal and how this might be exploited by sensory systems to maximize the detection of weak signals. We finish by pointing out some interesting future directions for research in this area. In particular, we explore the interesting possibility that synaptic dynamics might be matched with the nonrenewal spiking statistics of presynaptic spike trains in order to further improve information transmission.

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Section: Neural Codingmentioning

confidence: 99%

Nonrenewal spike train statistics: causes and functional consequences on neural coding

Ávila-Ǻkerberg

Chacron

2011

Exp Brain Res

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“…In this way, classic notions, such that cortical minicolumns may constitute the fundamental units of brain function (31), or that the cortex works by population coding in space (32) or rate coding in time (33) in the face of high intertrial variability (34), could then be tested rigorously using a measure of effectiveness. Examining small motifs that are overrepresented in complex networks [such as brains (35)] could determine whether the network as a whole is biased toward emergence or reduction.…”

Section: Discussionmentioning

confidence: 99%

Quantifying causal emergence shows that macro can beat micro

Hoel

Albantakis

Tononi

2013

Proc. Natl. Acad. Sci. U.S.A.

226

307

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Causal interactions within complex systems can be analyzed at multiple spatial and temporal scales. For example, the brain can be analyzed at the level of neurons, neuronal groups, and areas, over tens, hundreds, or thousands of milliseconds. It is widely assumed that, once a micro level is fixed, macro levels are fixed too, a relation called supervenience. It is also assumed that, although macro descriptions may be convenient, only the micro level is causally complete, because it includes every detail, thus leaving no room for causation at the macro level. However, this assumption can only be evaluated under a proper measure of causation. Here, we use a measure [effective information (EI)] that depends on both the effectiveness of a system's mechanisms and the size of its state space: EI is higher the more the mechanisms constrain the system's possible past and future states. By measuring EI at micro and macro levels in simple systems whose micro mechanisms are fixed, we show that for certain causal architectures EI can peak at a macro level in space and/or time. This happens when coarse-grained macro mechanisms are more effective (more deterministic and/or less degenerate) than the underlying micro mechanisms, to an extent that overcomes the smaller state space. Thus, although the macro level supervenes upon the micro, it can supersede it causally, leading to genuine causal emergence-the gain in EI when moving from a micro to a macro level of analysis.I n science, it is usually assumed that, the better one can characterize the detailed causal mechanisms of a complex system, the more one can understand how the system works. At times, it may be convenient to resort to a "macro"-level description, either because not all of the "micro"-level data are available, or because a rough model may suffice for one's purposes. However, a complete understanding of how a system functions, and the ability to predict its behavior precisely, would seem to require the full knowledge of causal interactions at the micro level. For example, the brain can be characterized at a macro scale of brain regions and pathways, a meso scale of local populations of neurons such as minicolumns and their connectivity, and a micro scale of neurons and their synapses (1). With the goal of a complete mechanistic understanding of the brain, ambitious programs have been launched with the aim of modeling its micro scale (2).The reductionist approach common in science has been successful not only in practice, but has also been supported by strong theoretical arguments. The chief argument starts from the intuitive notion that, when the properties of micro-level physical mechanisms of a system are fixed, so are the properties of all its macro levels-a relation called "supervenience" (3). In turn, this relation is usually taken to imply that the micro mechanisms do all of the causal work, i.e., the micro level is causally complete. This leaves no room for any causal contribution at the macro level; otherwise, there would be "multiple causation" (4). This ...

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“…embedded in a network of other neurons. In particular, spontaneous neural activity is largely caused by the massive quasi-random input from surrounding cells (Destexhe et al 2003), which also strongly affects a cell's computational properties (Brunel et al 2001;London et al 2010). Hence, when modeling single neuron activity, it is vital to properly account for the characteristics of the spike trains that constitute this input.…”

Section: Introductionmentioning

confidence: 99%

Statistical structure of neural spiking under non-Poissonian or other non-white stimulation

2015

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Nerve cells in the brain generate sequences of action potentials with a complex statistics. Theoretical attempts to understand this statistics were largely limited to the case of a temporally uncorrelated input (Poissonian shot noise) from the neurons in the surrounding network. However, the stimulation from thousands of other neurons has various sorts of temporal structure. Firstly, input spike trains are temporally correlated because their firing rates can carry complex signals and because of cell-intrinsic properties like neural refractoriness, bursting, or adaptation. Secondly, at the connections between neurons, the synapses, usagedependent changes in the synaptic weight (short-term plasticity) further shape the correlation structure of the effective input to the cell. From the theoretical side, it is poorly understood how these correlated stimuli, socalled colored noise, affect the spike train statistics. In particular, no standard method exists to solve the associated first-passage-time problem for the interspikeinterval statistics with an arbitrarily colored noise. Assuming that input fluctuations are weaker than the mean neuronal drive, we derive simple formulas for the essential interspike-interval statistics for a canon- ical model of a tonically firing neuron subjected to arbitrarily correlated input from the network. We verify our theory by numerical simulations for three paradigmatic situations that lead to input correlations: (i) ratecoded naturalistic stimuli in presynaptic spike trains; (ii) presynaptic refractoriness or bursting; (iii) synaptic short-term plasticity. In all cases, we find severe effects on interval statistics. Our results provide a framework for the interpretation of firing statistics measured in vivo in the brain.

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Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex

Cited by 421 publications

References 38 publications

Nonrenewal spike train statistics: causes and functional consequences on neural coding

Nonrenewal spike train statistics: causes and functional consequences on neural coding

Quantifying causal emergence shows that macro can beat micro

Statistical structure of neural spiking under non-Poissonian or other non-white stimulation

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