Why neurons mix: high dimensionality for higher cognition

Fusi, Stefano; Miller, Earl K.; Rigotti, Mattia

doi:10.1016/j.conb.2016.01.010

Cited by 648 publications

(750 citation statements)

References 55 publications

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“…This prediction is also supported by recent findings from entorhinal-cortex recordings in mice 31 . We therefore proposed here a novel role for conjunctive neurons as a neural substrate for encoding behaviorally relevant variables at fine temporal scales—although we note that conjunctive coding also likely has other important functions, such as representing multidimensional information in complex cognitive and working-memory tasks 18,32,33 .…”

Section: Discussionmentioning

confidence: 97%

Optimal dynamic coding by mixed-dimensionality neurons in the head-direction system of bats

et al. 2018

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Ethologically relevant stimuli are often multidimensional. In many brain systems, neurons with “pure” tuning to one stimulus dimension are found along with “conjunctive” neurons that encode several dimensions, forming an apparently redundant representation. Here we show using theoretical analysis that a mixed-dimensionality code can efficiently represent a stimulus in different behavioral regimes: encoding by conjunctive cells is more robust when the stimulus changes quickly, whereas on long timescales pure cells represent the stimulus more efficiently with fewer neurons. We tested our predictions experimentally in the bat head-direction system and found that many head-direction cells switched their tuning dynamically from pure to conjunctive representation as a function of angular velocity—confirming our theoretical prediction. More broadly, our results suggest that optimal dimensionality depends on population size and on the time available for decoding—which might explain why mixed-dimensionality representations are common in sensory, motor, and higher cognitive systems across species.

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Section: Discussionmentioning

confidence: 97%

Optimal dynamic coding by mixed-dimensionality neurons in the head-direction system of bats

et al. 2018

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“…Evidence for this hypothesis comes from the strong multiplexing and even mixed selectivity observed in many reward regions (Ganguli and Sompolinsky, 2012; Rigotti et al, 2013; Fusi et al, 2016). The reason is that mixed-selectivity allows linear readout more readily (Fusi et al, 2016; Rigotti et al, 2013). That is, the non-linearity observed in representations directly contributes to linearity in the readout.…”

Section: Implications Of the Choice Microarchitecturementioning

confidence: 99%

Economic Choice as an Untangling of Options into Actions

Yoo

Hayden

2018

Neuron

115

123

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SUMMARY We propose that economic choice can be understood as a gradual transformation from domain of options to one of the actions. We draw an analogy with the idea of untangling information in the form vision system and propose that form vision and economic choice may be two aspects of a larger process that sculpts actions based on sensory inputs. From this viewpoint, choice results from the accumulated effect of repetitions of simple computations. These may consist primarily of relative valuations (evaluations relative to the value of rejection, perhaps in a manner akin to divisive normalization) applied to individual offers. With regard to economic choice, cortical brain regions differ primarily in their position and in what information they prioritize, and do not—with a few exceptions—have categorically distinct roles. Each region’s specific contribution is determined largely by its inputs; thus, understanding connectivity is crucial for understanding choice. This view suggests that there is no single site of choice, that there is no meaningful distinction between pre- and post-decisionality, and that there is no explicit representation of value in the brain.

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“…In LIP, many of the neurons were better described by multiplicative interactions between different sensory and motor components than by linear combinations (Park et al 2014). These nonlinear interactions between task and stimulus variables, combined with substantial encoding heterogeneity across the population of LIP neurons, reflects an instance of mixed selectivity, for which theoretical treatments have highlighted computational benefits (Fusi et al 2016, Pagan & Rust 2014, Raposo et al 2014, Rigotti et al 2013). Thus, the multiplexed nature of LIP responses may be purely accidental but could also carry information-processing benefits, wherein the heterogeneous population can still be read out with a simple mechanism (Park et al 2014).…”

Section: Dissection Of Lip’s Correlations With Decision-making and Idmentioning

confidence: 99%

The Role of the Lateral Intraparietal Area in (the Study of) Decision Making

Huk

Katz

Yates

2017

Annu. Rev. Neurosci.

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Over the past two decades, neurophysiological responses in the lateral intraparietal area (LIP) have received extensive study for insight into decision-making. In a parallel manner, inferred cognitive processes have enriched interpretations of LIP activity. Because of this bidirectional interplay between physiology and cognition, LIP has served as fertile ground for developing quantitative models that link neural activity with decision-making. These models stand as some of the most important frameworks for linking brain and mind, and they are now mature enough to be evaluated in finer detail and integrated with other lines of investigation of LIP function. Here, we focus on the relationship between LIP responses and known sensory and motor events in perceptual decision-making tasks, as assessed by correlative and causal methods. The resulting sensorimotor-focused approach offers an account of LIP activity as a multiplexed amalgam of sensory, cognitive, and motor-related activity, with a complex and often indirect relationship to decision processes. Our data-driven focus on multiplexing (and de-multiplexing) of various response components can complement decision-focused models and provides more detailed insight into how neural signals might relate to cognitive processes such as decision-making.

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Why neurons mix: high dimensionality for higher cognition

Cited by 648 publications

References 55 publications

Optimal dynamic coding by mixed-dimensionality neurons in the head-direction system of bats

Optimal dynamic coding by mixed-dimensionality neurons in the head-direction system of bats

Economic Choice as an Untangling of Options into Actions

The Role of the Lateral Intraparietal Area in (the Study of) Decision Making

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