Modulation of the combinatorial code of odorant receptor response patterns in odorant mixtures

The perception of odors relies on combinatorial codes consisting of odorant receptor (OR) response patterns to encode odor identity. The modulation of these patterns by odorant interactions at ORs potentially explains several olfactory phenomena: mixture suppression, unpredictable sensory outcomes, and the perception of odorant mixtures as unique objects. We determined OR response patterns to 4 odorants and 3 binary mixtures in vivo in mice, identifying 30 responsive ORs. These patterns typically had a few strongly responsive ORs and a greater number of weakly responsive ORs. The ORs responsive to an odorant were often unrelated sequences distributed across several OR subfamilies. Mixture responses predicted pharmacological interactions between odorants, which were tested in vitro by heterologous expression of ORs in cultured cells. These tests provided independent evidence confirming odorant agonists for 13 ORs and identified both suppressive and additive effects of mixing odorants. This included 11 instances of antagonism of ORs by an odorant, 1 instance of additive responses to a binary mixture, 1 instance of suppression of a strong agonist by a weak agonist, and the discovery of an inverse agonist for an OR. These findings demonstrate that interactions between odorants at ORs are common.

Building the knowledge base for non-combinable codes according to the Korean Standard Classification of Diseases

The purpose of this study is to develop a knowledge base for non-combinable combinatorial codes to improve the accuracy of disease classification. We defined the rules related to non-combinable codes according to the list of code pairs proposed by the HIRA and the KCD-7 classification rules. A knowledge base was created according to defined rules and verified. To validate the knowledge base, inpatients who were billed for diabetes mellitus in December 2016 were selected as the subject of the study. As a result, the number of combinatorial codes proposed by the HIRA was 1,195, but the number of code pairs generated in the knowledge base was 25,439. Non-combinable codes by confirming with an indication of the HIRA have discovered 1,391 cases. As a result of verification with the code pair of the proposed knowledge base, 100 combinations were found. Non-combinable codes by confirming with an indication of the HIRA have discovered 1,391 cases. As a result of verification with the code pair of the proposed knowledge base, 3,525 combinations were found. It is meaningful that a convenient authoring tool that can automatically catch combinatorial codes was developed to build a knowledge base.

Isometry groups of combinatorial codes

Two isometry groups of combinatorial codes are described: the group of isometries, that is, the group of Hamming isometries from a code to itself and the group of monomial isometries, which is the group of those isometries of a code to itself that extend to monomial maps. Unlike the case of classical linear codes, where these groups are the same, it is shown that for combinatorial codes the groups can be arbitrarily different. In particular, there exist codes with the richest possible group of isometries and the trivial group of monomial isometries. In the paper, the two groups are characterized and codes with predefined isometry groups are constructed.

Asymmetry of Neuronal Combinatorial Codes Arises from Minimizing Synaptic Weight Change

Synaptic change is a costly resource, particularly for brain structures that have a high demand of synaptic plasticity. For example, building memories of object positions requires efficient use of plasticity resources since objects can easily change their location in space and yet we can memorize object locations. But how should a neural circuit ideally be set up to integrate two input streams (object location and identity) in case the overall synaptic changes should be minimized during ongoing learning? This letter provides a theoretical framework on how the two input pathways should ideally be specified. Generally the model predicts that the information-rich pathway should be plastic and encoded sparsely, whereas the pathway conveying less information should be encoded densely and undergo learning only if a neuronal representation of a novel object has to be established. As an example, we consider hippocampal area CA1, which combines place and object information. The model thereby provides a normative account of hippocampal rate remapping, that is, modulations of place field activity by changes of local cues. It may as well be applicable to other brain areas (such as neocortical layer V) that learn combinatorial codes from multiple input streams.