Sense of Smell: Single Giant Interneuron in Locusts Controls Activity in 50,000 Neurons, Enabling Sparse Codes for Odours

Sense of Smell: Single Giant Interneuron in Locusts Controls Activity in 50,000 Neurons, Enabling Sparse Codes for Odours

  8:09:00 PM    Science Lover

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The brain is a coding machine: it translates physical inputs from the world into visual, olfactory, auditory, tactile perceptions via the mysterious language of its nerve cells and the networks which they form. Neural codes could in principle take many forms, but in regions forming bottlenecks for information flow (e.g., the optic nerve) or in areas important for memory, sparse codes are highly desirable.

A single "giant", non-spiking, GABAergic interneuron
(right, labelled by intracellular injection of fluorescent dye)
forms an all-to-all negative feedback loop with a population
of about 50,000 Kenyon cells, principal neurons of the
mushroom bodies, a structure involved in olfactory
memory in the insect brain. This normalizing feedback
loop serves to ensure relatively constant sparseness
of mushroom body output across varying input
strengths. Sparseness is an important feature of
sensory representations in areas involved in
memory formation. (Credit: © MPI for Brain Research)

Scientists at the Max Planck Institute for Brain Research in Frankfurt have now discovered a single neuron in the brain of locusts that enables the adaptive regulation of sparseness in olfactory codes. This single giant interneuron tracks in real time the activity of several tens of thousands of neurons in an olfactory centre and feeds inhibition back onto all of them, so as to maintain their collective output within an appropriately sparse regime. In this way, representation sparseness remains steady as input intensity or complexity varies.

Signals from the world (electromagnetic waves, pressure, chemicals etc) are converted to electrical activity in sensory neurons and processed by neuronal networks in the brain. Insects sense smells via their antennae. Odours are detected by sensory neurons there, and olfactory data are then sent to and processed by the antennal lobes and a region of the brain known as the mushroom bodies. Neurons in the antennal lobes tend to be "promiscuous": odours are thus represented by specific combinations of neuronal activity. Neurons in the mushroom bodies -- they are called Kenyon cells -- , however, respond with great specificity and thus extremely rarely. In addition, they generally respond with fewer than three electrical impulses when stimulated with the right odour. This "sparse coding" strategy has the advantage that it simplifies the task of storing odour representations in memory.

Surprisingly, each Kenyon cell is connected on average to half of all possible presynaptic neurons in the antennal lobes. So how do the Kenyon cells manage to respond only extremely rarely, and with a sparseness that varies little over large ranges of stimulation conditions? Gilles Laurent of the Max Planck Institute for Brain Research and his group found that a single giant interneuron plays a key role. Along with colleagues in his lab (formerly at Caltech) and Great Britain, he has discovered that this neuron, with its extensive arbour, is activated by the entire Kenyon cell population and in turn inhibits them all back. "The giant interneuron and the Kenyon cells form a simple negative feed-back loop: the more strongly it is activated by the Kenyon cell population, the more strongly it curtails their activity in return," explains Laurent. The interneuron itself does not generate any action potentials, but inhibits Kenyon cells via nonspiking and graded release of the neurotransmitter GABA (gamma aminobutyric acid). This smooth, graded property enables this giant interneuron to do a kind of real-time, population averaging, thus carrying out an operation that might otherwise require the involvement of hundreds or thousands of individual spiking neurons.

The effectiveness of the giant interneuron is such that it can actually turn off the Kenyon cell population completely. But the research team also discovered that the giant interneuron is, in turn, controlled by another inhibitory neuron. "This allows the network activity to be potentiated or attenuated, and the sensitivity of this feedback loop to be adjusted," says Gilles Laurent. This is an important feature for brain regions such as the mushroom bodies, which are responsible not only for olfactory processing, but also for learning and memory. Mushroom bodies are where smells can be associated with other sensory modalities, enabling the formation of complex representations.

The scientists' findings show how massive negative feed-back loops can be formed in neuronal networks and what roles they can play. In vertebrates, the piriform cortex, part of the olfactory cortical complex, sits in a position equivalent to the mushroom bodies. "It is very likely that mammals have similar all-to-all control mechanisms in cortical and other circuits. They might not consist of single interneurons, however, but rather of populations of inhibitory neurons with means to couple their responses and actions," surmises Laurent. "Insect brains never cease to give us insights about neural computation, and to put elegant solutions right on our laps, if we know where to look and are a bit lucky."

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Butterfly Effect Makes Brain Unreliable

Butterfly Effect Makes Brain Unreliable

  11:07:00 PM    Science Lover

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Next time your brain plays tricks on you, you have an excuse: according to new research by UCL scientists published June 30 in the journal Nature, the brain is intrinsically unreliable.

Researchers introduced a small perturbation into the brain, the neural equivalent of butterfly wings, and ask what would happen to the activity in the circuit. Would the perturbation grow and have a knock-on effect, thus affecting the rest of the brain, or immediately die out? (Credit: Image courtesy of University College London)

This may not seem surprising to most of us, but it has puzzled neuroscientists for decades. Given that the brain is the most powerful computing device known, how can it perform so well even though the behaviour of its circuits is variable?

A long-standing hypothesis is that the brain's circuitry actually is reliable -- and the apparently high variability is because your brain is engaged in many tasks simultaneously, which affect each other.

It is this hypothesis that the researchers at UCL tested directly. The team -- a collaboration between experimentalists at the Wolfson Institute for Biomedical Research and a theorist, Peter Latham, at the Gatsby Computational Neuroscience Unit -- took inspiration from the celebrated butterfly effect -- from the fact that the flap of a butterfly's wings in Brazil could set off a tornado in Texas. Their idea was to introduce a small perturbation into the brain, the neural equivalent of butterfly wings, and ask what would happen to the activity in the circuit. Would the perturbation grow and have a knock-on effect, thus affecting the rest of the brain, or immediately die out?

It turned out to have a huge knock-on effect. The perturbation was a single extra 'spike', or nerve impulse, introduced to a single neuron in the brain of a rat. That single extra spike caused about thirty new extra spikes in nearby neurons in the brain, most of which caused another thirty extra spikes, and so on. This may not seem like much, given that the brain produces millions of spikes every second. However, the researchers estimated that eventually, that one extra spike affected millions of neurons in the brain.

"This result indicates that the variability we see in the brain may actually be due to noise, and represents a fundamental feature of normal brain function," said lead author Dr. Mickey London, of the Wolfson Institute for Biomedical Research, UCL.

This rapid amplification of spikes means that the brain is extremely 'noisy' -- much, much noisier than computers. Nevertheless, the brain can perform very complicated tasks with enormous speed and accuracy, far faster and more accurately than the most powerful computer ever built (and likely to be built in the foreseeable future). The UCL researchers suggest that for the brain to perform so well in the face of high levels of noise, it must be using a strategy called a rate code. In a rate code, neurons consider the activity of an ensemble of many neurons, and ignore the individual variability, or noise, produced by each of them.

So now we know that the brain is truly noisy, but we still don't know why. The UCL researchers suggest that one possibility is that it's the price the brain pays for high connectivity among neurons (each neuron connects to about 10,000 others, resulting in over 8 million kilometres of wiring in the human brain). Presumably, that high connectivity is at least in part responsible for the brain's computational power. However, as the research shows, the higher the connectivity, the noisier the brain. Therefore, while noise may not be a useful feature, it is at least a by-product of a useful feature.

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