Neural Encoding of Odors during Active Sampling and in Turbulent Plumes

Sensory inputs are often fluctuating and intermittent, yet animals reliably utilize them to direct behavior. Here we ask how natural stimulus fluctuations influence the dynamic neural encoding of odors. Using the locust olfactory system, we isolated two main causes of odor intermittency: chaotic odor plumes and active sampling behaviors. Despite their irregularity, chaotic odor plumes still drove dynamic neural response features including the synchronization, temporal patterning, and short-term plasticity of spiking in projection neurons, enabling classifier-based stimulus identification and activating downstream decoders (Kenyon cells). Locusts can also impose odor intermittency through active sampling movements with their unrestrained antennae. Odors triggered immediate, spatially targeted antennal scanning that, paradoxically, weakened individual neural responses. However, these frequent but weaker responses were highly informative about stimulus location. Thus, not only are odor-elicited dynamic neural responses compatible with natural stimulus fluctuations and important for stimulus identification, but locusts actively increase intermittency, possibly to improve stimulus localization

Self-organization in the olfactory system: one shot odor recognition in insects

We show in a model of spiking neurons that synaptic plasticity in the mushroom bodies in combination with the general fan-in, fan-out properties of the early processing layers of the olfactory system might be sufficient to account for its efficient recognition of odors. For a large variety of initial conditions the model system consistently finds a working solution without any fine-tuning, and is, therefore, inherently robust. We demonstrate that gain control through the known feedforward inhibition of lateral horn interneurons increases the capacity of the system but is not essential for its general function. We also predict an upper limit for the number of odor classes Drosophila can discriminate based on the number and connectivity of its olfactory neurons

A phase I dose escalation study of BIBW 2992, an irreversible dual inhibitor of epidermal growth factor receptor 1 (EGFR) and 2 (HER2) tyrosine kinase in a 2-week on, 2-week off schedule in patients with advanced solid tumours

To assess tolerability, pharmacokinetics (PK), pharmacodynamics (PD) and clinical activity of the dual epidermal growth factor receptor (EGFR) 1 and 2 (HER2) tyrosine kinase inhibitor BIBW 2992. An escalating schedule of once-daily (OD) BIBW 2992 for 14 days followed by 14 days off medication was explored. Thirty-eight patients were enrolled. Dose levels were 10, 20, 30, 45, 70, 85, and 100 mg. At 100 mg dose-limiting toxicity (DLT) (common toxicity criteria grade 3 skin rash and grade 3 diarrhoea despite treatment with loperamide) occurred in two patients. In the next-lower dose of 70 mg, DLT (grade 3 fatigue and ALAT elevation) occurred in one of six patients. An intermediate dose level of 85 mg was studied. Here DLT occurred in two patients (grade 3 diarrhoea despite treatment and grade 2 diarrhoea lasting more than 7 days despite treatment). An additional 12 patients were treated at 70 mg. BIBW 2992 PK after single and multiple doses revealed moderately fast absorption, and no deviation from dose proportionality. Pharmacodynamics analysis in skin biopsies did not show significant changes in EGFR-associated biomarkers. However, a significant inhibitory effect on the proliferation index of epidermal keratinocytes was observed. No partial or complete responses were observed, stable disease lasting more than four cycles was seen in seven patients. The recommended dose for studies with BIBW 2992 for 14 days followed by 14 days off medication is 70 mg OD

Stimulus-dependent maximum entropy models of neural population codes

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. To be able to infer a model for this distribution from large-scale neural recordings, we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. The model is able to capture the single-cell response properties as well as the correlations in neural spiking due to shared stimulus and due to effective neuron-to-neuron connections. Here we show that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. As a result, the SDME model gives a more accurate account of single cell responses and in particular outperforms uncoupled models in reproducing the distributions of codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like surprise and information transmission in a neural population.Comment: 11 pages, 7 figure

Information transmission in oscillatory neural activity

Periodic neural activity not locked to the stimulus or to motor responses is usually ignored. Here, we present new tools for modeling and quantifying the information transmission based on periodic neural activity that occurs with quasi-random phase relative to the stimulus. We propose a model to reproduce characteristic features of oscillatory spike trains, such as histograms of inter-spike intervals and phase locking of spikes to an oscillatory influence. The proposed model is based on an inhomogeneous Gamma process governed by a density function that is a product of the usual stimulus-dependent rate and a quasi-periodic function. Further, we present an analysis method generalizing the direct method (Rieke et al, 1999; Brenner et al, 2000) to assess the information content in such data. We demonstrate these tools on recordings from relay cells in the lateral geniculate nucleus of the cat.Comment: 18 pages, 8 figures, to appear in Biological Cybernetic

Gain control network conditions in early sensory coding

Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models

Encoding Odorant Identity by Spiking Packets of Rate-Invariant Neurons in Awake Mice

Background: How do neural networks encode sensory information? Following sensory stimulation, neural coding is commonly assumed to be based on neurons changing their firing rate. In contrast, both theoretical works and experiments in several sensory systems showed that neurons could encode information as coordinated cell assemblies by adjusting their spike timing and without changing their firing rate. Nevertheless, in the olfactory system, there is little experimental evidence supporting such model. Methodology/Principal Findings: To study these issues, we implanted tetrodes in the olfactory bulb of awake mice to record the odorant-evoked activity of mitral/tufted (M/T) cells. We showed that following odorant presentation, most M/T neurons do not significantly change their firing rate over a breathing cycle but rather respond to odorant stimulation by redistributing their firing activity within respiratory cycles. In addition, we showed that sensory information can be encoded by cell assemblies composed of such neurons, thus supporting the idea that coordinated populations of globally rateinvariant neurons could be efficiently used to convey information about the odorant identity. We showed that different coding schemes can convey high amount of odorant information for specific read-out time window. Finally we showed that the optimal readout time window corresponds to the duration of gamma oscillations cycles. Conclusion: We propose that odorant can be encoded by population of cells that exhibit fine temporal tuning of spiking activity while displaying weak or no firing rate change. These cell assemblies may transfer sensory information in spikin

Generalization Mediates Sensitivity to Complex Odor Features in the Honeybee

Animals use odors as signals for mate, kin, and food recognition, a strategy which appears ubiquitous and successful despite the high intrinsic variability of naturally-occurring odor quantities. Stimulus generalization, or the ability to decide that two objects, though readily distinguishable, are similar enough to afford the same consequence [1], could help animals adjust to variation in odor signals without losing sensitivity to key inter-stimulus differences. The present study was designed to investigate whether an animal's ability to generalize learned associations to novel odors can be influenced by the nature of the associated outcome. We use a classical conditioning paradigm for studying olfactory learning in honeybees [2] to show that honeybees conditioned on either a fixed- or variable-proportion binary odor mixture generalize learned responses to novel proportions of the same mixture even when inter-odor differences are substantial. We also show that the resulting olfactory generalization gradients depend critically on both the nature of the stimulus-reward paradigm and the intrinsic variability of the conditioned stimulus. The reward dependency we observe must be cognitive rather than perceptual in nature, and we argue that outcome-dependent generalization is necessary for maintaining sensitivity to inter-odor differences in complex olfactory scenes