The atoms of neural computation

Marcus, Gary; Marblestone, Adam; Dean, Thomas

doi:10.1126/science.1261661

Cited by 80 publications

(77 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The power-of-two permutation-based computational logic can be executed in the cortex whether it has three or six layers (Barbas, 2015; Fournier et al, 2015), in the service of the fundamental cortical “processing unit” that has been long sought-after (Van Hemmen and Sejnowski, 2005; Rabinovich et al, 2008; Hill et al, 2012; Marcus et al, 2014; Adolphs, 2015; Lisman, 2015). In both the mouse RSC and hamster PrL, we showed that specific cells were concentrated in the superficial layers (L2/3), whereas general cells were enriched in the deep layers (L5/6).…”

Section: Discussionmentioning

confidence: 99%

“…This pace is likely to accelerate further with recent BRAIN initiatives. However, it has long been recognized that there is a need to establish the basic computational frameworks that may underlie the brain’s functions (Grillner, 2006; Tsien, 2007; Brenner and Sejnowski, 2011; Shanahan, 2012; Budd and Kisvarday, 2013; Marcus et al, 2014; Geman and Geman, 2016). Imagine if all component parts were made available; what should be the unifying mathematical principles that evolution has adhered to in constructing brains in such a way as to be capable of intelligent cognition and adaptive behavior be?…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Brain Computation Is Organized via Power-of-Two-Based Permutation Logic

Xie

Fox

Liu

et al. 2016

Front. Syst. Neurosci.

View full text Add to dashboard Cite

There is considerable scientific interest in understanding how cell assemblies—the long-presumed computational motif—are organized so that the brain can generate intelligent cognition and flexible behavior. The Theory of Connectivity proposes that the origin of intelligence is rooted in a power-of-two-based permutation logic (N = 2i–1), producing specific-to-general cell-assembly architecture capable of generating specific perceptions and memories, as well as generalized knowledge and flexible actions. We show that this power-of-two-based permutation logic is widely used in cortical and subcortical circuits across animal species and is conserved for the processing of a variety of cognitive modalities including appetitive, emotional and social information. However, modulatory neurons, such as dopaminergic (DA) neurons, use a simpler logic despite their distinct subtypes. Interestingly, this specific-to-general permutation logic remained largely intact although NMDA receptors—the synaptic switch for learning and memory—were deleted throughout adulthood, suggesting that the logic is developmentally pre-configured. Moreover, this computational logic is implemented in the cortex via combining a random-connectivity strategy in superficial layers 2/3 with nonrandom organizations in deep layers 5/6. This randomness of layers 2/3 cliques—which preferentially encode specific and low-combinatorial features and project inter-cortically—is ideal for maximizing cross-modality novel pattern-extraction, pattern-discrimination and pattern-categorization using sparse code, consequently explaining why it requires hippocampal offline-consolidation. In contrast, the nonrandomness in layers 5/6—which consists of few specific cliques but a higher portion of more general cliques projecting mostly to subcortical systems—is ideal for feedback-control of motivation, emotion, consciousness and behaviors. These observations suggest that the brain’s basic computational algorithm is indeed organized by the power-of-two-based permutation logic. This simple mathematical logic can account for brain computation across the entire evolutionary spectrum, ranging from the simplest neural networks to the most complex.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain Computation Is Organized via Power-of-Two-Based Permutation Logic

Xie

Fox

Liu

et al. 2016

Front. Syst. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Kovelman, Baker, & Petitto, 2007;PalomarGarcía et al, 2015). Thus, NPSFs are hypothesized to be implemented at the level of the "language of thought" (Fodor, & Pylyshyn, 1988;Marcus, Marblestone, & Dean, 2014). We used these 42 NPSFs, developed to code the semantic properties of English words, testing their ability to generate accurate predictions concerning the neural representations of words in Portuguese.…”

Section: A Generative Mapping Between Word Concepts and Fmri Activatimentioning

confidence: 99%

Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function

et al. 2017

View full text Add to dashboard Cite

Keywords:Cross-language commonality, sentence representations in bilinguals, Predictive modeling of sentence representations, Meta-language brain locations in sentence processing ABSTRACTThe aim of the study was to test the cross-language generative capability of a model that predicts neural activation patterns evoked by sentence reading, based on a semantic characterization of the sentence. In a previous study on English monolingual speakers (Wang, Cherkassky, & Just, submitted), a computational model performed a mapping from a set of 42 concept-level semantic features (Neurally Plausible Semantic Features, NPSFs) as well as 6 thematic role markers to neural activation patterns (assessed with fMRI), to predict activation levels in a network of brain locations. The model used two types of information gained from the English-based fMRI data to predict the activation for individual sentences in Portuguese. First, it used the mapping weights from NPSFs to voxel activation levels derived from the model for English reading. Second, the brain locations for which the activation levels were predicted were derived from a factor analysis of the brain activation patterns during English reading. These meta-language locations were defined by the clusters of voxels with high loadings on each of the four main dimensions (factors), namely people, places, actions and feelings, underlying the neural representations of the stimulus sentences.This cross-language model succeeded in predicting the brain activation patterns associated with the reading of 60 individual Portuguese sentences that were entirely new to the model, attaining accuracies reliably above chance level. The prediction accuracy was not affected by whether the Portuguese speaker was monolingual or Portuguese-English bilingual. The model's confusion errors indicated an accurate capture of the events or states described in the sentence at a conceptual level. Overall, the cross-language predictive capability of the model demonstrates the neural commonality between speakers of different languages in the representations of everyday events and states, and provides an initial characterization of the common meta-language neural basis.

show abstract

“…Although model-based analyses reviewed above can reveal what information content during comprehension makes a difference in terms of neural signals, this type of correlational "bridging" represents an initial step towards a more ambitious goal of describing the plausible neural computational principles that explain the mapping to hypothesized linguistic/cognitive computations and taxonomies (Dehaene et al, 2015; see also Marcus et al, 2014). If probabilistic computations at some level represent a valid cognitive hypothesis underlying the behaviour, this should provide constraints on the target neural computations, mechanisms and algorithmic descriptions.…”

Section: Explanatory Status: Maps or Mapping?mentioning

confidence: 99%

Probabilistic language models in cognitive neuroscience: Promises and pitfalls

Armeni

Willems

Frank

2017

Neuroscience & Biobehavioral Reviews

View full text Add to dashboard Cite

A R T I C L E I N F O A B S T R A C TCognitive neuroscientists of language comprehension study how neural computations relate to cognitive computations during comprehension. On the cognitive part of the equation, it is important that the computations and processing complexity are explicitly defined. Probabilistic language models can be used to give a computationally explicit account of language complexity during comprehension. Whereas such models have so far predominantly been evaluated against behavioral data, only recently have the models been used to explain neurobiological signals. Measures obtained from these models emphasize the probabilistic, information-processing view of language understanding and provide a set of tools that can be used for testing neural hypotheses about language comprehension. Here, we provide a cursory review of the theoretical foundations and example neuroimaging studies employing probabilistic language models. We highlight the advantages and potential pitfalls of this approach and indicate avenues for future research.

show abstract

The atoms of neural computation

Abstract: Does the brain depend on a set of elementary, reusable computations?

Cited by 80 publications

References 78 publications

Brain Computation Is Organized via Power-of-Two-Based Permutation Logic

Brain Computation Is Organized via Power-of-Two-Based Permutation Logic

Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function

Probabilistic language models in cognitive neuroscience: Promises and pitfalls

Contact Info

Product

Resources

About