| Literature DB >> 28484375 |
Brandon L Warren1, Nobuyoshi Suto2, Bruce T Hope1.
Abstract
Many learned behaviors are directed by complex sets of highly specific stimuli or cues. The neural mechanisms mediating learned associations in these behaviors must be capable of storing complex cue information and distinguishing among different learned associations-we call this general concept "mechanistic resolution". For many years, our understanding of the circuitry of these learned behaviors has been based primarily on inactivation of specific cell types or whole brain areas regardless of which neurons were activated during the cue-specific behaviors. However, activation of all cells or specific cell types in a brain area do not have enough mechanistic resolution to encode or distinguish high-resolution learned associations in these behaviors. Instead, these learned associations are likely encoded within specific patterns of sparsely distributed neurons called neuronal ensembles that are selectively activated by the cues. This review article focuses on studies of neuronal ensembles in operant learned responding to obtain food or drug rewards. These studies suggest that the circuitry of operant learned behaviors may need to be re-examined using ensemble-specific manipulations that have the requisite level of mechanistic resolution.Entities:
Keywords: learned associations; mechanistic resolution; neuronal ensembles; operant learning
Mesh:
Year: 2017 PMID: 28484375 PMCID: PMC5401897 DOI: 10.3389/fncir.2017.00028
Source DB: PubMed Journal: Front Neural Circuits ISSN: 1662-5110 Impact factor: 3.492
Figure 1Formula for calculating number of possible combinations of activated neurons. A hypothetical brain region containing 300 neurons is shown on the left, if 1% of the neurons are active, 4,455,100 possible combinations are possible. With more neurons available in real brain areas, the number of possible combinations is much greater.
Figure 2Comparing global to ensemble-specific inactivation. The left column shows hypothetical neuronal ensembles. The middle column illustrates that global inactivation inactivates all neurons (both ensemble A and B). The right column shows ensemble-specific inactivation of ensemble A, leaving ensemble B unaffected.