Principles for coding associative memories in a compact neural network.

A major goal in neuroscience is to elucidate the principles by which memories are stored in a neural network. Here, we have systematically studied how four types of associative memories (short- and long-term memories, each as positive and negative associations) are encoded within the compact neural network of Caenorhabditis elegans worms. Interestingly, sensory neurons were primarily involved in coding short-term, but not long-term, memories, and individual sensory neurons could be assigned to coding either the conditioned stimulus or the experience valence (or both). Moreover, when considering the collective activity of the sensory neurons, the specific training experiences could be decoded. Interneurons integrated the modulated sensory inputs and a simple linear combination model identified the experience-specific modulated communication routes. The widely distributed memory suggests that integrated network plasticity, rather than changes to individual neurons, underlies the fine behavioral plasticity. This comprehensive study reveals basic memory-coding principles and highlights the central roles of sensory neurons in memory formation.

The synaptic organization in the Caenorhabditis elegans neural network suggests significant local compartmentalized computations.

Neurons are characterized by elaborate tree-like dendritic structures that support local computations by integrating multiple inputs from upstream presynaptic neurons. It is less clear whether simple neurons, consisting of a few or even a single neurite, may perform local computations as well. To address this question, we focused on the compact neural network of Caenorhabditis elegans animals for which the full wiring diagram is available, including the coordinates of individual synapses. We find that the positions of the chemical synapses along the neurites are not randomly distributed nor can they be explained by anatomical constraints. Instead, synapses tend to form clusters, an organization that supports local compartmentalized computations. In mutually synapsing neurons, connections of opposite polarity cluster separately, suggesting that positive and negative feedback dynamics may be implemented in discrete compartmentalized regions along neurites. In triple-neuron circuits, the nonrandom synaptic organization may facilitate local functional roles, such as signal integration and coordinated activation of functionally related downstream neurons. These clustered synaptic topologies emerge as a guiding principle in the network, presumably to facilitate distinct parallel functions along a single neurite, which effectively increase the computational capacity of the neural network.

A negative feedback loop in the GPCR pathway underlies efficient coding of external stimuli.

Efficient navigation based on chemical cues is an essential feature shared by all animals. These cues may be encountered in complex spatiotemporal patterns and with orders of magnitude varying intensities. Nevertheless, sensory neurons accurately extract the relevant information from such perplexing signals. Here, we show how a single sensory neuron in Caenorhabditis elegans animals can cell-autonomously encode complex stimulus patterns composed of instantaneous sharp changes and of slowly changing continuous gradients. This encoding relies on a simple negative feedback in the G-protein-coupled receptor (GPCR) signaling pathway in which TAX-6/Calcineurin plays a key role in mediating the feedback inhibition. This negative feedback supports several important coding features that underlie an efficient navigation strategy, including exact adaptation and adaptation to the magnitude of the gradient's first derivative. A simple mathematical model explains the fine neural dynamics of both wild-type and tax-6 mutant animals, further highlighting how the calcium-dependent activity of TAX-6/Calcineurin dictates GPCR inhibition and response dynamics. As GPCRs are ubiquitously expressed in all sensory neurons, this mechanism may be a general solution for efficient cell-autonomous coding of external stimuli.

Author Correction: Concerted pulsatile and graded neural dynamics enables efficient chemotaxis in C. elegans.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Addendum: Irrational behavior in C. elegans arises from asymmetric modulatory effects within single sensory neurons.

We would like to make our readers aware of the publication by Cohen et al., which reports irrational behaviour in C. elegans olfactory preference[1] . These complementary studies establish C. elegans as a model system to explore the neural mechanisms of decision making.

Irrational behavior in C. elegans arises from asymmetric modulatory effects within single sensory neurons.

C. elegans worms exhibit a natural chemotaxis towards food cues. This provides a potential platform to study the interactions between stimulus valence and innate behavioral preferences. Here we perform a comprehensive set of choice assays to measure worms' relative preference towards various attractants. Surprisingly, we find that when facing a combination of choices, worms' preferences do not always follow value-based hierarchy. In fact, the innate chemotaxis behavior in worms robustly violates key rationality paradigms of transitivity, independence of irrelevant alternatives and regularity. These violations arise due to asymmetric modulatory effects between the presented options. Functional analysis of the entire chemosensory system at a single-neuron resolution, coupled with analyses of mutants, defective in individual neurons, reveals that these asymmetric effects originate in specific sensory neurons.

Concerted pulsatile and graded neural dynamics enables efficient chemotaxis in C. elegans.

The ability of animals to effectively locate and navigate toward food sources is central for survival. Here, using C. elegans nematodes, we reveal the neural mechanism underlying efficient navigation in chemical gradients. This mechanism relies on the activity of two types of chemosensory neurons: one (AWA) coding gradients via stochastic pulsatile dynamics, and the second (AWCON) coding the gradients deterministically in a graded manner. The pulsatile dynamics of the AWA neuron adapts to the magnitude of the gradient derivative, allowing animals to take trajectories better oriented toward the target. The robust response of AWCON to negative derivatives promotes immediate turns, thus alleviating the costs incurred by erroneous turns dictated by the AWA neuron. This mechanism empowers an efficient navigation strategy that outperforms the classical biased-random walk strategy. This general mechanism thus may be applicable to other sensory modalities for efficient gradient-based navigation.

An assumption-free quantification of neural responses to electrical stimulations.

BACKGROUND: Connectivity between brain regions provides the fundamental infrastructure for information processing. The standard way to characterize these interactions is to stimulate one site while recording the evoked response from a second site. The average stimulus-triggered response is usually compared to the pre-stimulus activity. This requires a set of prior assumptions regarding the amplitude and duration of the evoked response. NEW METHOD: We introduce an assumption-free method for detecting and clustering evoked responses. We used Independent Component Analysis to reduce the dimensions of the response vectors, and then clustered them according to a Gaussian mixture model. This enables both the detection and categorization of responsive sites into different subtypes. RESULTS: Our method is demonstrated on recordings obtained from the sensory-motor cortex of behaving primates in response to stimulation of the cerebello-thalamo-cortical tract. We detected and classified the evoked responses of local field potential (LFP) and local spiking activity (multiunit activity-MUA). We found a strong association between specific input (LFP) and output (MUA) patterns across cortical sites, further supporting the physiological relevance of the proposed method. COMPARISON WITH EXISTING METHODS: Our method detected the vast majority of sites found in the conventional, significant threshold-crossing method. However, we found a subgroup of sites with a robust response that were missed when using the conventional method. CONCLUSION: Our method provides a useful, assumption-free tool for detecting and classifying neural evoked responses in a physiologically-relevant manner.