Greg J. Stephens

Speaker–listener neural coupling underlies successful communication

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 speakers spatiotemporal brain activity to model listeners’ brain activity and found that the speakers activity is spatially and temporally coupled with the listeners activity. This coupling vanishes when participants fail to communicate. Moreover, though on average the listeners brain activity mirrors the speakers 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 production- and comprehension-based processes serves as a mechanism by which brains convey information.

Dimensionality and dynamics in the behavior of C. elegans

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.

Optogenetic analysis of synaptic function.

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 γ-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 γ-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 ≤ 0.05) altered in mutants with pre- or postsynaptic defects, although the behavioral phenotypes did not unambiguously report on synaptic function in all cases tested. OptIoN facilitates the analysis of neurotransmission with high temporal precision, in a neurotransmitter-selective manner, possibly allowing future investigation of synaptic plasticity in C. elegans.

Functional structure of cortical neuronal networks grown in vitro.

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-information 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 on 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.

Active sampling and decision making in Drosophila chemotaxis

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.

Automated Tracking of Animal Posture and Movement during Exploration and Sensory Orientation Behaviors

Background The nervous functions of an organism are primarily reflected in the behavior it is capable of. Measuring behavior quantitatively, at high-resolution and in an automated fashion provides valuable information about the underlying neural circuit computation. Accordingly, computer-vision applications for animal tracking are becoming a key complementary toolkit to genetic, molecular and electrophysiological characterization in systems neuroscience. Methodology/Principal Findings We present Sensory Orientation Software (SOS) to measure behavior and infer sensory experience correlates. SOS is a simple and versatile system to track body posture and motion of single animals in two-dimensional environments. In the presence of a sensory landscape, tracking the trajectory of the animals sensors and its postural evolution provides a quantitative framework to study sensorimotor integration. To illustrate the utility of SOS, we examine the orientation behavior of fruit fly larvae in response to odor, temperature and light gradients. We show that SOS is suitable to carry out high-resolution behavioral tracking for a wide range of organisms including flatworms, fishes and mice. Conclusions/Significance Our work contributes to the growing repertoire of behavioral analysis tools for collecting rich and fine-grained data to draw and test hypothesis about the functioning of the nervous system. By providing open-access to our code and documenting the software design, we aim to encourage the adaptation of SOS by a wide community of non-specialists to their particular model organism and questions of interest.

A place for time: the spatiotemporal structure of neural dynamics during natural audition

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.

Emergence of long timescales and stereotyped behaviors in Caenorhabditis elegans

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.

From modes to movement in the behavior of Caenorhabditis elegans.

Organisms move through the world by changing their shape, and here we explore the mapping from shape space to movements in the nematode Caenorhabditis elegans as it crawls on an agar plate. We characterize the statistics of the trajectories through the correlation functions of the orientation angular velocity, orientation angle and the mean-squared displacement, and we find that the loss of orientational memory has significant contributions from both abrupt, large amplitude turning events and the continuous dynamics between these events. Further, we discover long-time persistence of orientational memory in the intervals between abrupt turns. Building on recent work demonstrating that C. elegans movements are restricted to a low-dimensional shape space, we construct a map from the dynamics in this shape space to the trajectory of the worm along the agar. We use this connection to illustrate that changes in the continuous dynamics reveal subtle differences in movement strategy that occur among mutants defective in two classes of dopamine receptors.

Statistical mechanics of letters in words

We consider words as a network of interacting letters, and approximate the probability distribution of states taken on by this network. Despite the intuition that the rules of English spelling are highly combinatorial (and arbitrary), we find that maximum entropy models consistent with pairwise correlations among letters provide a surprisingly good approximation to the full statistics of four letter words, capturing ∼ 92% of the multi–information among letters and even ‘discovering’ real words that were not represented in the data from which the pairwise correlations were estimated. The maximum entropy model defines an energy landscape on the space of possible words, and local minima in this landscape account for nearly two–thirds of words used in written English.