Literature DB >> 35803736

Monkey Prefrontal Single-Unit Activity Reflecting Category-Based Logical Thinking Process and Its Neural Network Model.

Takayuki Hosokawa1,2, Muyuan Xu3, Yuichi Katori3,4, Munekazu Yamada1, Kazuyuki Aihara5, Ken-Ichiro Tsutsui6.   

Abstract

Category-based thinking is a fundamental form of logical thinking. Here, we aimed to investigate its neural process at the local circuit level in the prefrontal cortex (PFC). We recorded single-unit PFC activity while male monkeys (Macaca fuscata) performed a task in which the category and rule were prerequisites of logical thinking and the outcome contingency was its consequence. Different groups of neurons coded a single type of information discretely or multiple types in a transitional form. Results of time-by-time analysis of neuronal activity suggest an information flow from category-coding and rule-coding neurons to transitional intermediate neurons, and then to contingency-coding neurons. Category-coding, rule-coding, and contingency-coding neurons showed stable coding of information, whereas intermediate neurons showed dynamic coding, as if it integrated category and rule to derive contingency. A similar process was confirmed by using a spiking neural network model that consisted of subnetworks coding category and rule on the input layer and those coding contingency on the output layer, with a subnetwork for integration in the intermediate layer. These results suggest that category-based logical thinking is realized in the PFC by separated neural populations organized for working in a feedforward manner.SIGNIFICANCE STATEMENT To elucidate the neural process for logical thinking, we combined an in-depth analysis of single-unit activity data with a biologically plausible computational model. Results of time-by-time analysis of prefrontal neuronal activity suggest an information flow from category-coding and rule-coding neurons to transitional intermediate neurons, and then to contingency-coding neurons. Category-coding, rule-coding, and contingency-coding neurons showed stable coding, whereas intermediate neurons showed dynamic coding, as if they integrated category and rule to derive contingency. A spiking neural network model reproduced similar temporal changes of information as the recorded neuronal data. Our results suggest that the prefrontal cortex (PFC) is critically involved in category-based thought process, and this process may be produced by separated neural populations organized for working in a feedforward manner.
Copyright © 2022 the authors.

Entities:  

Keywords:  category; logical thinking process; monkey; neural network model; prefrontal; single unit

Mesh:

Year:  2022        PMID: 35803736      PMCID: PMC9398542          DOI: 10.1523/JNEUROSCI.2286-21.2022

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.709


  34 in total

Review 1.  An integrative theory of prefrontal cortex function.

Authors:  E K Miller; J D Cohen
Journal:  Annu Rev Neurosci       Date:  2001       Impact factor: 12.449

2.  Prospective coding for objects in primate prefrontal cortex.

Authors:  G Rainer; S C Rao; E K Miller
Journal:  J Neurosci       Date:  1999-07-01       Impact factor: 6.167

3.  Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory.

Authors:  X J Wang
Journal:  J Neurosci       Date:  1999-11-01       Impact factor: 6.167

4.  Object category structure in response patterns of neuronal population in monkey inferior temporal cortex.

Authors:  Roozbeh Kiani; Hossein Esteky; Koorosh Mirpour; Keiji Tanaka
Journal:  J Neurophysiol       Date:  2007-04-11       Impact factor: 2.714

5.  PFC neurons reflect categorical decisions about ambiguous stimuli.

Authors:  Jefferson E Roy; Timothy J Buschman; Earl K Miller
Journal:  J Cogn Neurosci       Date:  2014-01-09       Impact factor: 3.225

6.  Integration of what and where in the primate prefrontal cortex.

Authors:  S C Rao; G Rainer; E K Miller
Journal:  Science       Date:  1997-05-02       Impact factor: 47.728

7.  Representation of abstract quantitative rules applied to spatial and numerical magnitudes in primate prefrontal cortex.

Authors:  Anne-Kathrin Eiselt; Andreas Nieder
Journal:  J Neurosci       Date:  2013-04-24       Impact factor: 6.167

8.  Prefrontal Cortex Predicts State Switches during Reversal Learning.

Authors:  Ramon Bartolo; Bruno B Averbeck
Journal:  Neuron       Date:  2020-04-20       Impact factor: 17.173

Review 9.  Quantitative assessment of CA1 local circuits: knowledge base for interneuron-pyramidal cell connectivity.

Authors:  Marianne J Bezaire; Ivan Soltesz
Journal:  Hippocampus       Date:  2013-07-10       Impact factor: 3.899

10.  Choosing the rules: distinct and overlapping frontoparietal representations of task rules for perceptual decisions.

Authors:  Jiaxiang Zhang; Nikolaus Kriegeskorte; Johan D Carlin; James B Rowe
Journal:  J Neurosci       Date:  2013-07-17       Impact factor: 6.167

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.