Transient dynamics between displaced fixed points: an alternate nonlinear dynamical framework for olfaction

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns.

Population coding is essential for rapid information processing in the moth antennal lobe

We investigated how odorant information is transmitted by neurons in the moth antennal lobe (AL). The neurons were repeatedly stimulated by three different odorants and their activity was intracellularly recorded. First, the response properties of single neurons were analyzed. The neurons exhibited highly reliable responses to the odorants and 43% of AL neurons responded to two or three odorants. The population distribution of firing rates in response to odorant stimulation was relatively broad in moth AL neurons, which is consistent across insects. Second, we attempted to decode the odorant identity from the activity of the recorded neurons using the maximum likelihood method. The decoding performance rapidly improves with increasing the number of neurons. Notably, an increase in the size of neural population results in faster transfer of information and increased the duration to retain odorant information. In conclusion, the AL neurons encode odorant information reliably and the population coding can transmit odorant information to olfactory centers. Population coding allows AL to encode and transmit olfactory information faster than the discrimination latency demonstrated in behavioral experiments. This article is part of a Special Issue entitled Neural Coding 2012.