What is signal and what is noise in the brain?

Knoblauch, Andreas; Palm, Günther

doi:10.1016/j.biosystems.2004.09.007

Cited by 36 publications

(14 citation statements)

References 24 publications

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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.

240

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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Section: Discussionmentioning

confidence: 99%

Quantifying causal emergence shows that macro can beat micro

Hoel

Albantakis

Tononi

2013

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

240

307

View full text Add to dashboard Cite

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“…the PSTH. Doing so makes the implicit assumption that the rate function is the same in all trials, because only then the trial averaged estimate faithfully reflects the rate profile which underlies the generation of action potentials in individual trials (Aertsen et al, 1989;Knoblauch and Palm, 2005;Masuda and Aihara, 2003;Ventura et al, 2005). This criterion was clearly met in all our simulations where we imposed the same intensity function in all trials.…”

Section: Non-stationarity Across Trialsmentioning

confidence: 97%

Measurement of variability dynamics in cortical spike trains

Nawrot

Boucsein

Molina

et al. 2008

Journal of Neuroscience Methods

192

241

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“…In distributed codes, randomly removing n units does not lead to the total deletion of n representations, but rather to the gradual loss of information in all representations. This would seem especially useful considering the different sources of noise-environmental or neural-in spite of which the brain has to operate (Knoblauch & Palm, 2005). Redundant localist coding would indeed provide a similar advantage, were it not already disproved by the above mentioned estimates.…”

Section: The Localist Versus Distributed Debatementioning

confidence: 99%

Holographic String Encoding

2010

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In this article, we apply a special case of holographic representations to letter position coding. We translate different well-known schemes into this format, which uses distributed representations and supports constituent structure. We show that in addition to these brain-like characteristics, performances on a standard benchmark of behavioral effects are improved in the holographic format relative to the standard localist one. This notably occurs because of emerging properties in holographic codes, like transposition and edge effects, for which we give formal demonstrations. Finally, we outline the limits of the approach as well as its possible future extensions.

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What is signal and what is noise in the brain?

Cited by 36 publications

References 24 publications

Quantifying causal emergence shows that macro can beat micro

Quantifying causal emergence shows that macro can beat micro

Measurement of variability dynamics in cortical spike trains

Holographic String Encoding

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