A major challenge in analyzing animal behavior is to discover some underlying simplicity in complex motor actions. Here, we show that the space of shapes adopted by the nematode Caenorhabditis elegans is low dimensional, with just four dimensions accounting for 95% of the shape variance. These dimensions provide a quantitative description of worm behavior, and we partially reconstruct “equations of motion” for the dynamics in this space. These dynamics have multiple attractors, and we find that the worm visits these in a rapid and almost completely deterministic response to weak thermal stimuli. Stimulus-dependent correlations among the different modes suggest that one can generate more reliable behaviors by synchronizing stimuli to the state of the worm in shape space. We confirm this prediction, effectively “steering” the worm in real time.

Verbal communication is a joint activity; however, speech production and comprehension have primarily been analyzed as independent processes within the boundaries of individual brains. Here, we applied fMRI to record brain activity from both speakers and listeners during natural verbal communication. We used the speaker's spatiotemporal brain activity to model listeners' brain activity and found that the speaker's activity is spatially and temporally coupled with the listener's activity. This coupling vanishes when participants fail to communicate. Moreover, though on average the listener's brain activity mirrors the speaker's activity with a delay, we also find areas that exhibit predictive anticipatory responses. We connected the extent of neural coupling to a quantitative measure of story comprehension and find that the greater the anticipatory speaker-listener coupling, the greater the understanding. We argue that the observed alignment of productionand comprehension-based processes serves as a mechanism by which brains convey information.functional MRI | intersubject correlation | language production | language comprehension V erbal communication is a joint activity by which interlocutors share information (1). However, little is known about the neural mechanisms underlying the transfer of linguistic information across brains. Communication between brains may be facilitated by a shared neural system dedicated to both the production and the perception/comprehension of speech (1-7). Existing neurolinguistic studies are mostly concerned with either speech production or speech comprehension, and focus on cognitive processes within the boundaries of individual brains (1). The ongoing interaction between the two systems during everyday communication thus remains largely unknown. In this study we directly examine the spatial and temporal coupling between production and comprehension across brains during natural verbal communication.Using fMRI, we recorded the brain activity of a speaker telling an unrehearsed real-life story and the brain activity of a listener listening to a recording of the story. In the past, recording speech during an fMRI scan has been problematic due to the high levels of acoustic noise produced by the MR scanner and the distortion of the signal by traditional microphones. Thus, we used a customized MR-compatible dual-channel optic microphone that cancels the acoustic noise in real time and achieves high levels of noise reduction with negligible loss of audibility (see SI Methods and Fig. 1A). To make the study as ecologically valid as possible, we instructed the speaker to speak as if telling the story to a friend (see SI Methods for a transcript of the story and Movie S1 for an actual sample of the recording). To minimize motion artifacts induced by vocalization during an fMRI scan, we trained the speaker to produce as little head movement as possible. Next, we measured the brain activity (n = 11) of a listener listening to the recorded audio of the spoken story, thereby capturing the time...

The ability to respond to chemical stimuli is fundamental to the survival of motile organisms, but the strategies underlying odour tracking remain poorly understood. Here we show that chemotaxis in Drosophila melanogaster larvae is an active sampling process analogous to sniffing in vertebrates. Combining computer-vision algorithms with reconstructed olfactory environments, we establish that larvae orient in odour gradients through a sequential organization of stereotypical behaviours, including runs, stops, lateral head casts and directed turns. Negative gradients, integrated during runs, control the timing of turns. Positive gradients detected through high-amplitude head casts determine the direction of individual turns. By genetically manipulating the peripheral olfactory circuit, we examine how orientation adapts to losses and gains of function in olfactory input. Our findings suggest that larval chemotaxis represents an intermediate navigation strategy between the biased random walks of Escherichia Coli and the stereo-olfaction observed in rats and humans.

We introduce optogenetic investigation of neurotransmission (OptIoN) for time-resolved and quantitative assessment of synaptic function via behavioral and electrophysiological analyses. We photo-triggered release of acetylcholine or gamma-aminobutyric acid at Caenorhabditis elegans neuromuscular junctions using targeted expression of Chlamydomonas reinhardtii Channelrhodopsin-2. In intact Channelrhodopsin-2 transgenic worms, photostimulation instantly induced body elongation (for gamma-aminobutyric acid) or contraction (for acetylcholine), which we analyzed acutely, or during sustained activation with automated image analysis, to assess synaptic efficacy. In dissected worms, photostimulation evoked neurotransmitter-specific postsynaptic currents that could be triggered repeatedly and at various frequencies. Light-evoked behaviors and postsynaptic currents were significantly (P

We apply an information theoretic treatment of action potential time series measured with microelectrode arrays to estimate the connectivity of mammalian neuronal cell assemblies grown in vitro. We infer connectivity between two neurons via the measurement of the mutual information between their spike trains. In addition we measure higher point multi-informations between any two spike trains conditional on the activity of a third cell, as a means to identify and distinguish classes of functional connectivity among three neurons. The use of a conditional three-cell measure removes some interpretational shortcomings of the pairwise mutual information and sheds light into the functional connectivity arrangements of any three cells. We analyze the resultant connectivity graphs in light of other complex networks and demonstrate that, despite their ex vivo development, the connectivity maps derived from cultured neural assemblies are similar to other biological networks and display nontrivial structure in clustering coefficient, network diameter and assortative mixing. Specifically we show that these networks are weakly disassortative small world graphs, which differ significantly in their structure from randomized graphs with the same degree. We expect our analysis to be useful in identifying the computational motifs of a wide variety of complex networks, derived from time series data. I. INTRODUCTIONUnderstanding and quantifying the dynamical mechanisms used by the nervous system to store and process information remains one of the greatest challenges to contemporary science. At present, broad outlines of the physical mechanisms that underpin the basic functioning of single neurons and synapses in the brain are understood [1]. However, this detailed knowledge of individual units sheds little light into the origin of the unmatched computational power of mammalian nervous systems, achieved despite characteristic operating times that are six orders of magnitude slower than those of modern digital computers.The computational nature of the brain lies therefore principally in the ensemble properties of neurons, synapses and their emergent complex, dynamical networks. Over the last few years the interaction structure of many complex systems has been mapped in terms of graphs, which can in turn be characterized using tools of statistical physics [2]. This approach has revealed broad classes of networks such as small world graphs [3] and scale free networks [4], which occur across fields of study, from technological networks, such as the internet, to various biological and social systems. The structural properties of these graphs, such as their degree distribution or their local transitivity, moreover, have been suggested to result from optimization constraints [5,6] or network growth dynamics [6], thus connecting graph structure to operational definitions of function, independent of a system's details. These lines of research provide new quantitative insights, connecting the interaction structure of a complex systems to nove...

We exploit the reduced space of C. elegans postures to develop a novel tracking algorithm which captures both simple shapes and also self-occluding coils, an important, yet unexplored, component of 2D worm behavior. We apply our algorithm to show that visually complex, coiled sequences are a superposition of two simpler patterns: the body wave dynamics and a head-curvature pulse. We demonstrate the precise true0normalΩ-turn dynamics of an escape response and uncover a surprising new dichotomy in spontaneous, large-amplitude coils; deep reorientations occur not only through classical normalΩ-shaped postures but also through larger postural excitations which we label here as δ-turns. We find that omega and delta turns occur independently, suggesting a distinct triggering mechanism, and are the serpentine analog of a random left-right step. Finally, we show that omega and delta turns occur with approximately equal rates and adapt to food-free conditions on a similar timescale, a simple strategy to avoid navigational bias.DOI: http://dx.doi.org/10.7554/eLife.17227.001

We use functional magnetic resonance imaging (fMRI) to analyze neural responses to natural auditory stimuli. We characterize the fMRI time series through the shape of the voxel power spectrum and find that the timescales of neural dynamics vary along a spatial gradient, with faster dynamics in early auditory cortex and slower dynamics in higher order brain regions. The timescale gradient is observed through the unsupervised clustering of the power spectra of individual brains, both in the presence and absence of a stimulus, and is enhanced in the stimulus-locked component that is shared across listeners. Moreover, intrinsically faster dynamics occur in areas that respond preferentially to momentary stimulus features, while the intrinsically slower dynamics occur in areas that integrate stimulus information over longer timescales. These observations connect the timescales of intrinsic neural dynamics to the timescales of information processing, suggesting a temporal organizing principle for neural computation across the cerebral cortex.

Animal behaviors often are decomposable into discrete, stereotyped elements, well separated in time. In one model, such behaviors are triggered by specific commands; in the extreme case, the discreteness of behavior is traced to the discreteness of action potentials in the individual command neurons. Here, we use the crawling behavior of the nematode Caenorhabditis elegans to demonstrate the opposite view, in which discreteness, stereotypy, and long timescales emerge from the collective dynamics of the behavior itself. In previous work, we found that as C. elegans crawls, its body moves through a "shape space" in which four dimensions capture approximately 95% of the variance in body shape. Here we show that stochastic dynamics within this shape space predicts transitions between attractors corresponding to abrupt reversals in crawling direction. With no free parameters, our inferred stochastic dynamical system generates reversal timescales and stereotyped trajectories in close agreement with experimental observations. We use the stochastic dynamics to show that the noise amplitude decreases systematically with increasing time away from food, resulting in longer bouts of forward crawling and suggesting that worms can use noise to modify their locomotory behavior.motor behavior | stochastic transitions | adaptation M any organisms, from bacteria to humans, exhibit discrete, stereotyped motor behaviors. A common model is that these behaviors are stereotyped because they are triggered by specific commands, and in some cases we can identify "command neurons" whose activity provides the trigger (1). In the extreme, discreteness and stereotypy of the behavior reduces to the discreteness and stereotypy of the action potentials generated by the command neurons, as with the escape behaviors in fish triggered by spiking of the Mauthner cell (2). But the stereotypy of spikes itself emerges from the continuous dynamics of currents, voltages, and ion channel populations (3, 4). Is it possible that, in more complex systems, stereotypy emerges not from the dynamics of single neurons, but from the dynamics of larger circuits of neurons, perhaps coupled to the mechanics of the behavior itself? Here we explore this question in the context of abrupt reversals in the crawling direction of the nematode Caenorhabditis elegans (5-7).Reversal behaviors of C. elegans are particularly interesting as the underlying neural circuitry includes a nominal command neuron, AVA (8), whose activity is correlated with forward vs. backward crawling (9). On the other hand, AVA is an interneuron in a network, and it is unknown whether the decision to reverse direction can be traced to a single cell. Even when AVA is ablated, reversals occur, although the distribution of times spent in the backward crawling state shifts (7). Further, most neurons in C. elegans do not generate action potentials, so even if a single neuron dominates the decision it is not obvious why the trajectory of a reversal would be stereotyped. As a complement to probing further in...