What the fly’s nose tells the fly’s brain

Stevens, Charles F.

doi:10.1073/pnas.1510103112

Cited by 103 publications

(153 citation statements)

References 35 publications

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“…This model separates the odor composition, encoded in the activity a of the projection neurons, from the odor intensity, which could be encoded by the total excitation e tot or the threshold level γ [60]. For significant inhibition the representation a is sparse and the set of active projection neurons provides a natural odor ‘tag’ that could be used for identification and memorization in the downstream processing [35]. …”

Section: Discussionmentioning

confidence: 99%

“…This choice is motivated by experimental measurements, which also suggest that λ ≈ 1 for flies and humans [27]. The random sensing implied by these sensitivities has been discussed in terms of compressed sensing [35, 36] and we showed previously that it typically decorrelates stimuli, thus leading to near-optimal odor representations on the level of glomeruli [27]. …”

Section: Simple Model Of the Olfactory Systemmentioning

confidence: 99%

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Normalized Neural Representations of Complex Odors

Zwicker

2016

PLoS ONE

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The olfactory system removes correlations in natural odors using a network of inhibitory neurons in the olfactory bulb. It has been proposed that this network integrates the response from all olfactory receptors and inhibits them equally. However, how such global inhibition influences the neural representations of odors is unclear. Here, we study a simple statistical model of the processing in the olfactory bulb, which leads to concentration-invariant, sparse representations of the odor composition. We show that the inhibition strength can be tuned to obtain sparse representations that are still useful to discriminate odors that vary in relative concentration, size, and composition. The model reveals two generic consequences of global inhibition: (i) odors with many molecular species are more difficult to discriminate and (ii) receptor arrays with heterogeneous sensitivities perform badly. Comparing these predictions to experiments will help us to understand the role of global inhibition in shaping normalized odor representations in the olfactory bulb.

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Section: Discussionmentioning

confidence: 99%

Section: Simple Model Of the Olfactory Systemmentioning

confidence: 99%

Normalized Neural Representations of Complex Odors

Zwicker

2016

PLoS ONE

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“…fly | olfaction | theory | odor code T he projection neurons of the fly antennal lobe present odor information to the Kenyon cells of the mushroom body in the form of a combinatorial code-each odor is specified by a particular pattern of firing rates across the population of projection neurons-and a recent paper (1), using data published in ref. 2, provided preliminary evidence that this odor code is what information theorists call maximum entropy (3).…”

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confidence: 99%

A statistical property of fly odor responses is conserved across odors

Stevens

2016

Proc. Natl. Acad. Sci. U.S.A.

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I have reanalyzed the data presented by Hallem and Carlson [Hallem EA, Carlson JR (2006) Cell 125(1):143-160] and shown that the combinatorial odor code supplied by the fruit fly antenna is a very simple one in which nearly all odors produce, statistically, the same neuronal response; i.e., the probability distribution of sensory neuron firing rates across the population of odorant sensory neurons is an exponential for nearly all odors and odor mixtures, with the mean rate dependent on the odor concentration. Between odors, then, the response differs according to which sensory neurons are firing at what individual rates and with what mean population rate, but not in the probability distribution of firing rates. This conclusion is independent of adjustable parameters, and holds both for monomolecular odors and complex mixtures. Because the circuitry in the antennal lobe constrains the mean firing rate to be the same for all odors and concentrations, the odor code is what is known as maximum entropy.fly | olfaction | theory | odor code T he projection neurons of the fly antennal lobe present odor information to the Kenyon cells of the mushroom body in the form of a combinatorial code-each odor is specified by a particular pattern of firing rates across the population of projection neurons-and a recent paper (1), using data published in ref. 2, provided preliminary evidence that this odor code is what information theorists call maximum entropy (3). To understand what a maximum entropy code is, suppose that we record the firing rates from, say, 10 different projection neurons, each presented with, say, 10 different odors to give a total of 100 firing rates. Now make a histogram of these firing rates. If the odor code is maximum entropy, this histogram would have nearly the same shape no matter which projection neurons and which odors were chosen. Also, the larger the sample of projection neurons and/or odors, the closer the shapes of histograms would be. Remarkably, if only a single odor and many projection neurons, or a single projection neuron and many odors, are used to generate the rates, the histogram shape is always nearly the same.Many different maximum entropy codes have been studied, and the type of code is defined by the shape of the histogram that results from a sample of rates. The histogram in each case is an approximation of a probability distribution of rates. For example, if the mean rate is always the same, the maximum entropy code is known to be associated with an exponential distribution of rates, and if both the mean and variance are always the same, a Gaussian distribution of rates is associated (3). For the fly projection neurons, the associated probability distribution of rates is proposed to be an exponential (1) (with always the same mean).As pointed out earlier (1), a maximum entropy code would be advantageous to the fly because it would permit the most odors to be discriminated with the available number of odorant receptors.This characterization of the odor code used by antennal lobe proje...

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“…It is invoked in a wide range of tasks including inference, optimization, decision 1 making, action selection, consensus, and foraging [18,23,9,63]. In inference and decoding, finding the best-supported alternative involves identifying the largest likelihood (max), then finding the model corresponding to that likelihood (argmax); decision making, action selection and foraging involve determining and selecting the most desirable alternative (option, move, or food source, respectively) according to some metric, again requiring max, argmax operations.…”

Section: Introductionmentioning

confidence: 99%

“…An example of this large-N max, argmax computation in the brain is the dynamics that lead to the sparsification of Kenyon cell activity within the fly mushroom bodies [63]. It is possible that many more areas with strong recurrent inhibition and gap-junction coupled interneurons display similar dynamics, including the vertebrate olfactory bulb [60,52], hippocampal area CA1 [1,19,62], and basal ganglia [50,55].…”

Section: Introductionmentioning

confidence: 99%

Robust parallel decision-making in neural circuits with nonlinear inhibition

Kriener

Chaudhuri

Fiete

2017

Preprint

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Identifying the maximal element (max, argmax) in a set is a core computational element in inference, decision making, optimization, action selection, consensus, and foraging. We show that running sequentially through a list of N fluctuating items takes N log(N ) time to accurately find the max, prohibitively slow for large N . The power of computation in the brain is ascribed to its parallelism, yet it is theoretically unclear whether, even on an elemental task like the max operation, leaky and noisy neurons can perform a distributed computation that cuts the required time by a factor of N , a benchmark for parallel computation. We show that conventional winner-take-all circuit models fail to realize the parallelism benchmark and worse, in the presence of noise altogether fail to produce a winner when N is large. If, however, neurons are equipped with a second nonlinearity so that weakly active neurons cannot contribute inhibition to the circuit, the network matches the accuracy of the serial strategy but does so N times faster, partially self-adjusting integration time for task difficulty and number of options and saturating the parallelism benchmark without parameter fine-tuning. Finally, in the regime of few choices (small N ), the same circuit predicts Hick's law of decision making; thus Hick's law behavior is a symptom of efficient parallel computation. Our work shows that distributed computation that saturates the parallelism benchmark is possible in networks of noisy and finite-memory neurons.

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What the fly’s nose tells the fly’s brain

Cited by 103 publications

References 35 publications

Normalized Neural Representations of Complex Odors

Normalized Neural Representations of Complex Odors

A statistical property of fly odor responses is conserved across odors

Robust parallel decision-making in neural circuits with nonlinear inhibition

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