Jason Neil MacLean - US grants

The mission of the MacLean laboratory is to understand how the brain encodes and stores information. The MacLean lab is multidisciplinary, combining biology and mathematics, and provides a rich training environment for neuroscientists of the future. In addition to the training and mentoring of both graduate and undergraduate students, Dr. MacLean is actively promoting scientific literacy through outreach programs including training opportunities within his lab for underrepresented groups and is also using his data in a course on advanced topics and methods in computational neuroscience. Using state of the art laser microscopy the MacLean lab films groups of neurons in the brain in action, allowing for the investigation of neuronal circuits. In the same way that an electrical circuit is formed by connecting simple components, a neural circuit is formed by its component neurons and the connections between them. It has long been known that single neurons work as part of larger circuits but only recently have the tools been available to investigate large populations of neurons at the same time. MacLean and co-workers recently showed that each neuron is co-active with a group of other neurons and that activity flows from group to group in an orderly and often repeating sequence. These organized circuit patterns are analogous to the scrolling text in Times Square, New York City. While no single light can convey even a single word, the array of lights can convey a full phrase or sentence through their patterned activity. Similarly, no single neuron can communicate the full information in a neural circuit on which the brain relies for its functioning. A fundamental, though often unspoken hypothesis in neuroscience is that information is coded by patterns of activation within circuits -- a question which the MacLean lab is investigating. Researchers also postulate that these spatiotemporal circuit patterns are a result of the specific connectivity between the neurons. Thus these patterns can be considered an elementary functional unit of information processing in the brain and simultaneously can reveal the underlying structure of brain circuits. Using data from many circuits the MacLean lab will uncover the basic circuit composition rules of the sensory cortex. For instance this work will reveal how many circuits are present in a patch of the sensory cortex containing a set number of neurons, a very basic question for which there still is no answer. Further the MacLean lab will mathematically show how information is represented by patterned circuit activations. Finally these researchers will determine the biological mechanisms which produce these patterns. These are fundamental questions critical to our understanding of cognition, learning, memory and behavior. The MacLean lab will provide substantial broader impacts by introduction of his research program into the experimental approaches taught in a course on computational neuroscience. In addition, Dr. MacLean will initiate a new Science and Technology program to show demonstrations of his work to girl scouts, in the hope of increasing participation of women and minorities in science.

Project Summary One of the hallmark features of our brains is their enormous capacity to learn and to adapt to changing environments. Theories of memory storage in neural circuits largely focus on activity-dependent changes in synaptic weights ? e.g. long-term potentiation (LTP) ? as plausible learning correlates. But does our synapto- centric view of the cellular events underlying learning really capture the essence of memory engrams? Recent studies on the formation of engrams, or ?mnemic traces? (a concept introduced by Richard Semon in 1904), suggest that the ultimate step in memory engram participation is the suprathreshold activation of neurons. We thus propose a complementary, neurocentric view, in which the participants in a functionally active engram are at least partly determined by cell-autonomous regulation of the intrinsic excitability of individual neurons. In this view, synaptic plasticity controls the formation of reciprocal connectivity patterns within and between engrams, and thus remains an important factor in circuit plasticity and learning. Recent reports indicate that a substantial percentage of pyramidal cells do not engage (remain silent) or are extremely unreliable when these neural circuits are activated, even in primary sensory cortices upon presentation of appropriate stimuli. Here, we will make use of this phenomenon to provide a proof-of-principle demonstration of a role of changes in membrane excitability (?intrinsic plasticity?) in engram formation. We will test the hypothesis that intrinsic plasticity activates previously silent (?dormant?) or unreliable neurons and integrates them into reliable engrams, thus providing a mechanism to dynamically regulate engram composition. We propose that activation of dormant or unreliable neurons constitutes a memory trace in cortical circuits (?intrinsic theory? of memory), by enhancing the capacity for input pattern representation, by increasing the engram activation probability, or by promoting engram stability. This hypothesis will be tested using whole-cell patch-clamp recordings from L2/3 pyramidal cells in the primary somatosensory cortex (S1; barrel cortex) of awake mice, which will be paired with two-photon imaging of GCaMP6s-encoded population activity. We plan to enhance intrinsic excitability by two methods: a) repeated injection of depolarizing currents through the patch pipette (non-synaptic activation; test for the intrinsic nature of this type of plasticity when combined with blockade of synaptic transmission; note that ?intrinsic? refers to the expression phase, but that under physiological conditions synaptic activity will be needed for induction), or b) deflection of select groups of whiskers at active whisking frequency (10-20Hz). We will not only monitor intrinsic excitability, but will test whether neurons become responsive to whisker stimulation (activation of dormant neurons). To assess neuronal integration into engrams, we will image activity patterns in populations of 100-200 neurons. Finally, we will examine whether cholinergic signaling ? through downregulation of SK2- type K+ channels ? facilitates intrinsic plasticity. The R21 mechanism is appropriate, because we are at an early stage of exploring and developing critical tests for the intrinsic hypothesis of learning presented here.

Abstract This project seeks to characterize the spatio-temporal organization of motor cortical (M1) activity at multiple spatial scales associated with upper limb movements of unrestrained marmoset monkeys performing ethological behaviors. The project has two goals: 1) To statistically evaluate the nature and stability of single neuron and ensemble-level motor representations in M1 at the columnar and areal spatial scales, and 2) To use our experimental data to develop a network model of a 3D patch of M1 capable of generating experimentally testable predictions about the movement representations in M1. We will combine two complementary technologies for large-scale neural recording: 1) wireless, high density multi-electrode arrays and 2) calcium fluorescence imaging - while common marmoset monkeys (Callithrix jacchus) perform naturalistic foraging behaviors. Advances in microelectrode array technology have permitted simultaneous electrophysiological recordings from hundreds of neurons in behaving animals. However, given the large inter- electrode distance (>=400 microns), much of the microcircuit activity at the subcolumnar level is unresolved. In contrast, calcium fluorescence imaging provides the opportunity to densely and simultaneously record the spiking activity of hundreds of neurons within a single cortical column. This dense, large-scale imaging allows for the resolution of neurons immediately adjacent to one another which increases the likelihood that they are synaptically connected. We will use a miniature fluorescence microscope attached to the skull which allows for head-free, unconstrained movements of the arm and hand. Moreover, by adding a prism lens to the microscope, we will be able to image neurons across lamina from layer 2/3 through layer 5. Using both technologies, we will characterize single neuron encoding properties, network dynamics, and functional connectivity within and between cortical columns. By bridging spatial scales, we will be able to interpolate between the cortical microcircuit level and the level of a whole cortical area. We will also investigate how the spatio-temporal organization of movement coding changes with motor skill acquisition. A unique and important feature of this project will be the use of natural and unconstrained foraging tasks that involve prey capture which will not require operant conditioning and will provide richer behaviors in order to build more accurate encoding models. We will also build large-scale network simulations of a patch of motor cortex constrained by the recorded data to understand how connectivity relates to tuning properties of single neurons. The model will then allow us to investigate what synaptic rules result in the observed changes in spatiotemporal patterning associated with motor learning. Ultimately, the principles of network dynamics, computation, and encoding deduced from the motor cortex may apply more generally to other neocortical areas. This research may also have applied relevance to brain-machine interface technology.

Project Summary Mechanistically linking network connectivity and the dynamics of neural networks to variation in the behavior of individuals is an overarching goal of neuroscience. Here we address this goal using techniques from network science to calculate functional networks that summarize pair-wise and higher order interactions between all recorded neurons. Network activity will be assessed using sophisticated two-photon (2P) imaging of activity- dependent Ca2+ signaling optimized to maximize the rate of recording and the numbers of neurons recorded. Multineuronal interactions within the networks will be identified, giving rise to encoding models to predict the network activity. Techniques from statistical physics will be used to optimally couple data from intracellular recordings to biologically realistic Hodgkin-Huxley (HH) models representing the contributions of ion currents and other free model parameters of the individual neurons. Networks of HH neurons using model synapses will replace pair-wise correlations to delinate the interrelationships between the ion currents of individual neurons and network interactions and dynamics. Taking advantage of the birdsong learning model, in the proposed experiments these approaches will be applied to the cortical song system HVC nucleus, allowing us to link these scales of investigation directly to behavior. Recent results demonstrate that changes in the intrinsic properties (IP) (ion current magnitudes) of HVC neurons is related to each individual's song, implicating changes within neurons as well as at synapses and networks that are related to learning. Aim 1: fast 2P imaging will be made in brain slices containing HVC that express spontaneous network activity. Model building will be supported by extensive efforts at 3-cell and 4-cell whole cell patch recordings, to better characterize HVC connectivity. The hypothesis that network structure depends on learning will be tested by examining how models vary between individual birds who sang similar or different songs. Models will be extended to in vivo observations by fast 2P imaging in sleeping birds while eliciting fictive singing using song playback, and in singing birds using 1P imaging. Results from the other Aims will further constrain the network and HH model building of Aim 1. Aim 2: the predictive power of the models will be further tested by using cellular resolution 2P optogenetic inhibition of selected neurons in in vivo and in vitro preparations. Aim 3: the role of neuronal IP in shaping network dynamics will be tested by using genetic and viral techniques to transiently modify specific ion channels in specific classes of HVC neurons. Changes in birds' singing behavior will be compared against a predictive HH model relating song structure and ion channel efficacy. Fast 2P imaging in slice and multisite extracellular recordings in singing birds will help to define how IP contribute to network models. Aim 4: single cell gene expression techniques will be used to identify all the HVC cell classes, the ion channels they express, and assess individual variation by examining cohorts of related birds or those singing the same songs. The overall goals and the four Aims are also designed to align with a subsequent U19 application.

Project Summary Here, we propose to thoroughly characterize the origins of pairwise correlations in cortex using a synergistic mix of experimental methodologies, behavior, and computation in mice and macaques. We will elucidate the mechanistic underpinnings of normalization and test our hypothesis that changes in cortical pairwise correlations and other signature arise from ongoing cortical computations. In Aim 1 we will record from populations of neurons in the middle temporal visual area of trained, behaving monkeys to test the hypothesis that pairwise spike correlations, gamma oscillation and transient responses at the onset of visual stimuli arise in part from the dynamics of the circuits that normalize neuronal responses. These tests require measurements with a precision that is not feasible in mice. Conversely, the experiments in Aim 2 and 3 address questions that are not feasible in monkeys. In Aim 2 we will exploit the accessibility of mouse visual cortex by using both two-photon laser scanning microscopy and multielectrode arrays to comprehensively measure the relationship between normalization and pairwise correlations in populations of V1 neurons and measure how spatial separation within cerebral cortex affects that relationship. Finally, in Aim 3 we will establish the contributions of specific cell classes to normalization and pairwise correlations in mouse V1. We record the activity of pyramidal neurons and the three most thoroughly characterized classes of cortical interneuron (VIP, SST and PV) during normalization. We will then separately manipulate the activity of these cells classes to revealing the role that changes to the ratio of excitation and inhibition play in driving normalization. In this way, we will establish the role these neurons play in changing pairwise correlations within the excitatory pool of neurons. Results from all three Aims will be tied together using a new family of dynamic, recurrent circuit models of normalization to formalize the hypothesis that normalization imposes pairwise correlations and other activity signatures, and will use experimental data to constrain and refine these models.