Volume 14 Supplement 1

Abstracts from the Twenty Second Annual Computational Neuroscience Meeting: CNS*2013

Open Access

Neuronal coding in the rodent prefrontal cortex

  • Olga Kornienko1Email author,
  • Liya Ma2,
  • James M Hyman2,
  • Jeremy K Seamans2 and
  • Daniel Durstewitz1
BMC Neuroscience201314(Suppl 1):P117

DOI: 10.1186/1471-2202-14-S1-P117

Published: 8 July 2013

Apart from being associated with working memory, neurons in the rodent medial prefrontal cortex (mPFC) are known to be involved in encoding of spatial and temporal contexts [1], the deduction of rules [2], and decision making [3]. The context-dependent organization of neural assemblies encoding for different task events, stimuli or decisions [3, 4], may account for the great flexibility required during the performance of higher cognitive tasks. The way in which single neurons and their interactions code for different entities may play a huge role in this flexibility [5], but has rarely been systematically investigated in the PFC.

Here, we employ various multivariate statistical techniques and time series bootstraps to analyze the way in which neurons, neural interactions, and temporal patterns of activity within ensembles of simultaneously recorded rat PFC neurons contribute to the neural population code during the performance of different tasks comprised of multiple stimuli, task events, and responses.

To examine the neural population representation of a given set of stimuli and task events, in a first step kernel density stimates of spiking activity were obtained from all recorded neurons. Both multivariate/ multiple regression and classification approaches were then utilized to characterize neuronal coding properties. Using regression, the distributions of single neuron contributions to the explained variation in stimulus conditions were charted, both individually and after regressing out or taking into account the contribution of other neurons. The same was done including neuronal interaction terms of various orders as well as time-lagged versions of the neuronal activities (based on the idea of delay-embedding, thus taking temporal patterns into account). Significant contributions of single terms or sets of terms were identified by construction of null hypothesis distributions through block-permutation bootstraps. In a complementary decoding-type of analysis, a linear discriminant analysis (LDA) classifier was run on sets of single neuron activities, time-lagged versions of these, and their interaction terms, with performance evaluated through leave-one-out cross-validation. Results show that 1) contributions to explained variation in stimulus conditions follow monotonically falling, potentially power-law-like, distributions, and 2) both including temporal pattern information as well as neural interaction terms significantly improves prediction performance and strongly reduces the misclassification rate.

These findings indicate that a) there appears to be no highly specialized subpopulation of neurons encoding for specific events, and b) that precise temporal patterns, and to a lesser degree correlations among units, have a major contribution to the neural representation of specific stimuli and internal task stages in the rat mPFC.



This work was funded by grants from the German ministry for education and research (BMBF, 01GQ1003B) and the Deutsche Forschungsgemeinschaft to D.D. (Du 354/7-2, SFB-636 B08).

Authors’ Affiliations

Bernstein Center for Computational Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University
Brain Research Centre, Psychiatry, Faculty of Medicine, University of British Columbia


  1. Hyman JM, Ma L, Balaguer-Ballerster E, Durstewitz D, Seamans JK: Contextual encoding by ensembles of medial prefrontal cortex neurons. PNAS. 2012, 109: 5086-5091. 10.1073/pnas.1114415109.PubMed CentralView ArticlePubMedGoogle Scholar
  2. Durstewitz D, Vittoz NM, Floresco SB, Seamans JK: Abrupt transitions between prefrontal neural ensemble states accompany behavioral transitions during rule learning. Neuron. 2010, 66: 334-336. 10.1016/j.neuron.2010.04.042.View ArticleGoogle Scholar
  3. Lapish CC, Durstewitz D, Chandler LJ, Seamans JK: Successful choice behavior is associated with distinct and coherent network states in anterior cingulate cortex. PNAS. 2008, 105: 11963-11968. 10.1073/pnas.0804045105.PubMed CentralView ArticlePubMedGoogle Scholar
  4. Balaguer-Ballerster E, Lapish CC, Seamans JK, Durstewitz D: Attracting Dynamics of Frontal Cortex Ensembles during Memory-Guided Decision-Making. PLoS Comput Biol. 2011, 7: e1002057-10.1371/journal.pcbi.1002057.View ArticleGoogle Scholar
  5. Rigotti M, Ben Dayan Rubin D, Wang XJ, Fusi S: Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses. Front Comput Neurosci. 2010, 4: 24-PubMed CentralView ArticlePubMedGoogle Scholar


© Kornienko et al; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.