Time and space are complementary encoding dimensions in the moth antennal lobe.

The contribution of time to the encoding of information by the nervous system is still controversial. The olfactory system is one of the standard preparations where this issue is empirically investigated. For instance, output neurons of the antennal lobe or the olfactory bulb display odor stimulus induced temporal modulations of their firing rate at a scale of hundreds of milliseconds. The role of these temporal patterns in the encoding of odor stimuli, however, is not yet known. Here, we use optical imaging of the projection neurons of the moth antennal lobe to address this question. First, we present a biophysically derived model that provides an accurate description of the calcium response of projection neurons. On the basis of this model, we subsequently show that the calcium response of the projection neurons displays a stimulus specific temporal structure. Finally, we demonstrate that an encoding scheme that includes this temporal information boosts classification performance by 60% as compared to a purely spatial encoding. Although the putative role of combinatorial spatio-temporal encoding strategies has been the subject of debate, our results for the first time establish quantitatively that such an encoding strategy is used by the insect brain.

Spatio-temporal Ca2+ dynamics of moth olfactory projection neurones.

We studied the Ca2+ dynamics of odour-evoked glomerular patterns in the antennal lobe of the moth Spodoptera littoralis using optical imaging. Here we selectively stained a large population of antennal lobe output neurones, projection neurones, by retrograde filling with FURA-dextran from the inner antennocerebral tract in the protocerebrum. Different plant-associated odorants evoked distributed patterns of activated glomeruli that were odour dependent and repeatable. These patterns were, however, dynamic during the period of odour exposure. Temporal responses differed across glomeruli and were stimulus dependent. Next we examined how the correlations between patterns evoked by different odorants changed with time. Initially, responses to structurally similar compounds were highly correlated, whereas responses to structurally different compounds differed. Within the period of odour exposure (1 s) we found a significant reduction in similarity of responses evoked by different odours, irrespective of initial similarity, whereas trial-to-trial correlations remained high. Our results suggested an ability for coarse classification at the initial encounter with an odour source. With time, however, the discrimination ability increases and structurally similar odours can be distinguished.

Decoding a temporal population code.

Encoding of sensory events in internal states of the brain requires that this information can be decoded by other neural structures. The encoding of sensory events can involve both the spatial organization of neuronal activity and its temporal dynamics. Here we investigate the issue of decoding in the context of a recently proposed encoding scheme: the temporal population code. In this code, the geometric properties of visual stimuli become encoded into the temporal response characteristics of the summed activities of a population of cortical neurons. For its decoding, we evaluate a model based on the structure and dynamics of cortical microcircuits that is proposed for computations on continuous temporal streams: the liquid state machine. Employing the original proposal of the decoding network results in a moderate performance. Our analysis shows that the temporal mixing of subsequent stimuli results in a joint representation that compromises their classification. To overcome this problem, we investigate a number of initialization strategies. Whereas we observe that a deterministically initialized network results in the best performance, we find that in case the network is never reset, that is, it continuously processes the sequence of stimuli, the classification performance is greatly hampered by the mixing of information from past and present stimuli. We conclude that this problem of the mixing of temporally segregated information is not specific to this particular decoding model but relates to a general problem that any circuit that processes continuous streams of temporal information needs to solve. Furthermore, as both the encoding and decoding components of our network have been independently proposed as models of the cerebral cortex, our results suggest that the brain could solve the problem of temporal mixing by applying reset signals at stimulus onset, leading to a temporal segmentation of a continuous input stream.