Circuit Mechanisms of Learning

• Research Goal and Significance

It is a central goal of neuroscience to elucidate how learning is encoded and executed by the nervous system. In the past, ethologists and geneticists established many paradigms of experience-dependent behavioral plasticity and characterized molecules that regulate learning. To fully understand complex learned behaviors, we need to mechanistically characterize how neural circuits link the “micro-level” understanding of molecules with the “macro-level” characterization of behavior.

To this end, we need to uncover the underlying neural circuits, define the properties of each functional node to address how a given circuit processes sensory inputs into motor outputs, illuminate the consequence of learning at each tier of the circuit, and characterize how these changes lead to learned behavior. We also need to understand how neuromodulators, such as amines and growth factors, set the global state of the neural circuits to modulate behavior. Our long-term goal is to achieve an integrative understanding of learning by addressing all levels of the underlying neural circuit from sensory perception to motor execution. Eventually, we will be able to test our understanding by manipulating learned behavior with genetic or optical tools, “writing in” experience-dependent changes to predictably modulate behavior.

•  System and Approach

To tackle these questions, we study learning in C. elegans. A major advantage of this system is that the complete anatomical connectome that describes the connectivity of all 302 neurons in its nervous system. Moreover, this well-defined nervous system can be dissected with powerful molecular, cellular and genetic tools. The functional properties of each neuron in sensorimotor transformation and learning – properties that are largely stereotyped among individuals - can also be characterized with optical neurophysiology in live animals as they perform complex, experience-dependent behaviors.

We focus on olfactory learning. Because olfaction plays an essential role in locating food sources, olfactory learning is similarly displayed across animal species. Thus, the underlying mechanisms are likely to be conserved. Olfaction is critical for C. elegans to navigate the environment, and the well-characterized olfactory neurons use conserved signaling pathways to process sensory inputs. We integrate quantitative behavioral analysis, molecular cellular genetics, optical imaging and ontogenetics in live animals that perform defined behavioral tasks to pursue a full scale model of learning that will ultimately incorporate all of the regulatory and modulatory elements in a small nervous system.

You can navigate to the “Movies” to see some cool displays of our methods.

• Progress

We seek the operational rules by which neural circuits for experience-dependent olfactory behavior utilize molecular and cellular mechanisms to shape neuronal properties and generate behavioral changes. To achieve this goal, we pursue the following aims:

1. Establish a robust olfactory learning paradigm and a quantitative behavioral assay.
2. Map the neural network that underlies olfactory learning.
3. Characterize the neuronal properties of the learning network to address how its functional attributes regulate olfactory sensorimotor behavior.
4. Identify the learning-correlated experience-dependent changes in the learning circuit.
5. Characterize how neuromodulation shapes the properties of the learning circuit.
6. Reveal the mechanisms whereby the learning correlates regulate behavioral changes.

In the past several years, we have accomplished much of aims 1-3 & 5, made significant progress in aim 4, and begun to pursue aim 6. We have established an olfactory learning paradigm whereby the nematode learns to avoid the smell of pathogenic bacteria after ingestion, analogous to Garcia effect found in many animals, including humans [Zhang et al., Nature 438:179]. We have developed an automated assay to quantify this aversive olfactory learning and functionally mapped a neuronal network that encodes both the naive and learned olfactory preferences [Luo et al., J Neurophysiology 99:2617; Ha et al., Neuron 68:1173]. We have also systematically characterized the property of the learning network [Ha et al., Neuron 68:1173; Hendricks et al., Nature 487:99; Qin et al, J Neuroscience 33:925]. Particularly, we have identified, for the first time, the compartmentalized axonal activity in a C. elegans neuron that plays a key role in the circuit of the olfactory learning [Hendricks et al., Nature 487:99]. This compartmentalized activity represents an internal feedback signal from motor program that resembles corollary discharge, a major means by which the nervous system monitors self-generated motion. Corollary discharge has been implicated in the learning ability of more complex brains and pathology of neurological disorders, such as schizophrenia. Identifying a simple form of corollary discharge in a genetically tractable model organism will allow us to study the mechanistic underpinnings of this important neural signaling and its role in complex brain function. We have also characterized the spatial and temporal activity of neuromodulators, such as insulin-like peptides and a TGF-beta growth factor, in regulating learning [Zhang and Zhang, PNAS 109:17081; Chen et al., Neuron 77:572; Fernandes de Abreu DA et al, PLoS Genetics 10(3):e1004225]. Our findings have elucidated remarkably conserved features in the functional structure, computational property and modulatory signaling of neural circuits that underlie experience-dependent behavioral plasticity.

• Projects

Our results provided critical foundations to further characterize the circuit mechanisms of learning. Our current research addresses the following questions.

Characterize functional consequence of learning in the underlying neural circuit  To understand how the network encodes learning, we will characterize the mechanisms underlying the neuronal correlates of learning, i.e. the experience-dependent changes in the learning circuit that correlate with learning. We will analyze the neurotransmission and genetic underpinnings of the learning correlates. We will also define how these learning correlates interact at circuit level.

Characterize the causal link between learning correlates and learned behavior  To understand how learning is encoded by the experience-dependent changes in the underlying circuit, we will characterize the causal role of the identified learning correlates in generating learning. We will manipulate the properties of the key learning neurons to eliminate the learning correlates in trained animals and “build” the learning correlates into the circuit of naive animals with optogenetics and other conditional regulation of neuronal activities, and examine the resulting effects on learning.

Characterize an ILP-network that regulates learning in the large context of animal physiology  We showed that the “INS-6 -| INS-7” pathway modulates learning by interacting with the key interneuron in the learning circuit and expression of ins-6 and ins-7 are regulated by environments. In addition, we uncovered a network of C. elegans ILPs that regulate INS-6 and INS-7. Thus, we hypothesize that this network integrates the functions of multiple ILPs to regulate learning in a context-dependent manner. We will address this hypothesis by characterizing the genetic framework and the cellular circuit through which the ILP network regulates learning and its context-dependent modulation.

Extended Research Interests

• Circuit mechanisms underlying experience-dependent salt chemotaxis

While it is crucial for our understanding of behavioral plasticity to define how the underlying neural circuits are functionally organized, this line of research is at a very early stage. To explore this question in a broader context, we collaborate with the Samuel lab (Department of Physics) to characterize the circuit property for another type of experience-dependent behavioral plasticity, salt chemotaxis. C. elegans navigates in salt gradients. The direction of the movement is regulated by previous interaction with salt and the current environment salt concentration. Using calcium imaging on animals subjected to salt gradients, we find that both the experience as well as the perception of the salt condition are encoded by the complex temporal dynamic of the activity of a single salt-sensing neuron. These findings reveal potentially complex temporal encoding in C. elegans sensory neurons. We currently study how the sensory information is processed to generate flexible behavioral outputs.

• Genetic basis of behavioral variance in hermaphrodite mating 

Our interest in understanding neural encoding of behavioral plasticity also motivates us to study how behaviors evolve. Natural selection on behavioral phenotypes acts on underlying genetic variations. However, we have very limited mechanistic insights into this process. C. elegans is well suited to address this question due to its relatively simple nervous system and genome. We study heritable differences in mating behavior. As hermaphrodites, C. elegans females self-reproduce with self-generated sperm while maintaining the ability to mate with males. We found that hermaphrodites of the wild-type reference strain N2 favor selfing, while a wild isolate CB4856 (HW) favors mating. Both mechanosensation and chemosensation are required for N2 hermaphrodites to resist mating with males, and the ability to self-reproduce negatively regulates mating. To map the genetic variation, we created recombinant inbred lines and identified two QTL that explained a large portion of N2 x HW variation in hermaphrodite mating. Intriguingly, ~40 wild isolates representing C. elegans global diversity exhibit extensive, continuous variation in hermaphrodite reproductive modes. These results show that C. elegans hermaphrodites regulate the choice between selfing and mating, highlight the natural variation in this choice, and lay the groundwork for further dissection of this evolutionarily important trait. These results were recently published in Bahrami and Zhang, Genes Genomes Genetics 3, 1851.