A category-free neural population supports evolving demands during decision-making

By David Raposo, Matthew T. Kaufman, Anne K. Churchland. 2014

481 citations recorded by the source corpus.

The posterior parietal cortex (PPC) receives diverse inputs and is involved in a dizzying array of behaviors. These many behaviors could rely on distinct categories of neurons specialized to represent particular variables or could rely on a single population of PPC neurons that is leveraged in different ways. To distinguish these possibilities, we evaluated rat PPC neurons recorded during multisensory decisions. Newly designed tests revealed that task parameters and temporal response features were distributed randomly across neurons, without evidence of categories. This suggests that PPC neurons constitute a dynamic network that is decoded according to the animal's present needs. To test for an additional signature of a dynamic network, we compared moments when behavioral demands differed: decision and movement. Our new state-space analysis revealed that the network explored different dimensions during decision and movement. These observations suggest that a single network of neurons can support the evolving behavioral demands of decision-making.

Subfields: Cognitive Neuroscience

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Citations in the corpus

Orthogonal representations for robust context-dependent task performance in brains and neural networks (2022, neutral): “Indeed many neurons, especially in prefrontal and parietal cortex, exhibit nonlinear mixed selectivity, multiplexing information over several potentially relevant task variables (Rigotti et al., 2013; Raposo et al., 2014; Tang et al., 2019) with errors heralded by a collapse in dimensionality (Rigot”
On the importance of single directions for generalization (2018, background): “wn that task information can be decoded with high accuracy from populations of these neurons with low individual class selectivity (Averbeck et al. (2006), Rigotti et al. (2013), Mante et al. (2013), Raposo et al. (2014), Morcos & Harvey (2016), Zylberberg (2017)). Perturbation analyses have bee”
Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex. (2017, unknown): “Harvey et al., 2012; Licata et al., 2017; Raposo et al., 2014).”
Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework (2016, methodology): “…present the results of applying the training framework to well-known experimental paradigms in systems neuroscience: perceptual decision-making [61 – 63], context-dependent integration [5], multisensory integration [64], parametric working memory [34, 65], and eye-movement sequence generation [66].”
The Role of Prefrontal Mixed Selectivity in Cognitive Control (2016, background): “In light of these findings, one should keep in mind the limited scope of conclusions obtained from a functionally defined subpopulation (Raposo et al., 2014) like the switch neurons.”
Projections of non-invasive human recordings into state space show unfolding of spontaneous and over-trained choice (2020, methodology): “More precisely, within a given trial, the order of presentation 396 was as follows (Figure 1A): (1) an instruction cue presented for 150ms showed a green and red dot to 397 signal that color was relevant or a left and right arrow to signal that motion was the relevant dimension 398 to attend to for ”
If deep learning is the answer, what is the question? (2020, background): “From this perspective, single neurons code for multiple experimental variables and their interactions 38–41 , exhibiting non-linear mixed selectivity.”
Demixed principal component analysis of neural population data (2014, methodology): “Second, even though dPCA, unlike PCA, does not enforce orthogonality between encoding axes corresponding to different task parameters, most of them turned out to be close to orthogonal to each other (Figures 3–6e, upper triangle), as has been observed before (Brendel et al., 2011; Rishel et al., 201”
Navigating the Neural Space in Search of the Neural Code. (2017, unknown): “…et al., 2012; Jazayeri and Movshon, 2006; Laurent et al., 2001; Lo and Wang, 2006; Mante et al., 2013; Ma et al., 2006; Moore and Armstrong, 2003; Raposo et al., 2014; Rigotti et al., 2013; Siegel et al., 2015; Znamenskiy and Zador, 2013), and they are thus difficult to explore by searching…”