Optogenetics seminars

Neural dynamics represent the hard-to-interpret substrate of circuit computations. Advances in large-scale recordings have highlighted the sheer spatiotemporal complexity of circuit dynamics within and across circuits, portraying in detail the difficulty of interpreting such dynamics and relating it to computation. Indeed, even in extremely simplified experimental conditions, one observes high-dimensional temporal dynamics in the relevant circuits. This complexity can be potentially addressed by the notion that not all changes in population activity have equal meaning, i.e., a small change in the evolution of activity along a particular dimension may have a bigger effect on a given computation than a large change in another. We term such conditions dimension-specific computation. Considering motor preparatory activity in a delayed response task we utilized neural recordings performed simultaneously with optogenetic perturbations to probe circuit dynamics. First, we revealed a remarkable robustness in the detailed evolution of certain dimensions of the population activity, beyond what was thought to be the case experimentally and theoretically. Second, the robust dimension in activity space carries nearly all of the decodable behavioral information whereas other non-robust dimensions contained nearly no decodable information, as if the circuit was setup to make informative dimensions stiff, i.e., resistive to perturbations, leaving uninformative dimensions sloppy, i.e., sensitive to perturbations. Third, we show that this robustness can be achieved by a modular organization of circuitry, whereby modules whose dynamics normally evolve independently can correct each other’s dynamics when an individual module is perturbed, a common design feature in robust systems engineering. Finally, we will recent work extending this framework to understanding the neural dynamics underlying preparation of speech.

Recent advances in techniques for monitoring and perturbing neural populations have greatly enhanced our ability to study circuits in the brain. In particular, two-photon holographic optogenetics now enables precise photostimulation of experimenter-specified groups of individual neurons, while simultaneous two-photon calcium imaging enables the measurement of ongoing and induced activity across the neural population. Despite the enormous space of potential photostimulation patterns and the time-consuming nature of photostimulation experiments, very little algorithmic work has been done to determine the most effective photostimulation patterns for identifying the neural population dynamics. Here, I will discuss ongoing development of active learning techniques to efficiently select which neurons to stimulate such that the resulting neural responses will best inform a dynamical model of the neural population activity. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-02-05. Recording duration: 00:42:29.

Inhibition has long been thought to stabilize the activity of cortical networks at low rates, and to shape significantly their response to sensory inputs. In this talk, I will describe three recent collaborative projects that shed light on these issues. (1) I will show how optogenetic excitation of inhibition neurons is consistent with cortex being inhibition stabilized even in the absence of sensory inputs, and how this data can constrain the coupling strengths of E-I cortical network models. (2) Recent analysis of the effects of optogenetic excitation of pyramidal cells in V1 of mice and monkeys shows that in some cases this optogenetic input reshuffles the firing rates of neurons of the network, leaving the distribution of rates unaffected. I will show how this surprising effect can be reproduced in sufficiently strongly coupled E-I networks. (3) Another puzzle has been to understand the respective roles of different inhibitory subtypes in network stabilization. Recent data reveal a novel, state dependent, paradoxical effect of weakening AMPAR mediated synaptic currents onto SST cells. Mathematical analysis of a network model with multiple inhibitory cell types shows that this effect tells us in which conditions SST cells are required for network stabilization.

In sexually reproducing species, males and females respond to environmental sensory cues and transform the input into sexually dimorphic traits. Yet, how sexually dimorphic behavior is encoded in the nervous system is poorly understood. We characterize the sexually dimorphic nociceptive behavior in C. elegans – hermaphrodites present a lower pain threshold than males in response to aversive stimuli, and study the underlying neuronal circuits, which are composed of the same neurons that are wired differently. By imaging receptor expression, calcium responses and glutamate secretion, we show that sensory transduction is similar in the two sexes, and therefore explore how downstream network topology shapes dimorphic behavior. We generated a computational model that replicates the observed dimorphic behavior, and used this model to predict simple network rewirings that would switch the behavior between the sexes. We then showed experimentally, using genetic manipulations, artificial gap junctions, automated tracking and optogenetics, that these subtle changes to male connectivity result in hermaphrodite-like aversive behavior in-vivo, while hermaphrodite behavior was more robust to perturbations. Strikingly, when presented with aversive cues, rewired males were compromised in finding mating partners, suggesting that the network topology that enables efficient avoidance of noxious cues would have a reproductive "cost". To summarize, we present a deconstruction of a sex-shared neural circuit that affects sexual behavior, and how to reprogram it. More broadly, our results are an example of how common neuronal circuits changed their function during evolution by subtle topological rewirings to account for different environmental and sexual needs.

In understanding circuit operations, a key issue is the extent to which neuronal spiking reflects local computation or responses to upstream inputs. Because pyramidal cells in CA1 do not have local recurrent projections, it is currently assumed that firing in CA1 is inherited from its inputs – thus, entorhinal inputs provide communication with the rest of the neocortex and the outside world, whereas CA3 inputs provide internal and past memory representations. Several studies have attempted to prove this hypothesis, by lesioning or silencing either area CA3 or the entorhinal cortex and examining the effect of firing on CA1 pyramidal cells. Despite the intense and careful work in this research area, the magnitudes and types of the reported physiological impairments vary widely across experiments. At least part of the existing variability and conflicts is due to the different behavioral paradigms, designs and evaluation methods used by different investigators. Simultaneous manipulations in the same animal or even separate manipulations of the different inputs to the hippocampal circuits in the same experiment are rare. To address these issues, I used optogenetic silencing of unilateral and bilateral mEC, of the local CA1 region, and performed bilateral pharmacogenetic silencing of the entire CA3 region. I combined this with high spatial resolution recording of local field potentials (LFP) in the CA1-dentate axis and simultaneously collected firing pattern data from thousands of single neurons. Each experimental animal had up to two of these manipulations being performed simultaneously. Silencing the medial entorhinal (mEC) largely abolished extracellular theta and gamma currents in CA1, without affecting firing rates. In contrast, CA3 and local CA1 silencing strongly decreased firing of CA1 neurons without affecting theta currents. Each perturbation reconfigured the CA1 spatial map. Yet, the ability of the CA1 circuit to support place field activity persisted, maintaining the same fraction of spatially tuned place fields, and reliable assembly expression as in the intact mouse. Thus, the CA1 network can maintain autonomous computation to support coordinated place cell assemblies without reliance on its inputs, yet these inputs can effectively reconfigure and assist in maintaining stability of the CA1 map.

Coping with threatening situations requires both identifying stimuli predicting danger and selecting adaptive behavioral responses in order to survive. The dorso medial prefrontal cortex (dmPFC) is a critical structure involved in the regulation of threat-related behaviour, yet it is still largely unclear how threat-predicting stimuli and defensive behaviours are associated within prefrontal networks in order to successfully drive adaptive responses. Over the past years, we used a combination we used a combination of extracellular recordings, neuronal decoding approaches, and state of the art optogenetic manipulations to identify key neuronal elements and mechanisms controlling defensive fear responses. I will present an overview of our recent work ranging from analyses of dedicated neuronal types and oscillatory and synchronization mechanisms to artificial intelligence approaches used to decode the activity or large population of neurons. Ultimately these analyses allowed the identification of high dimensional representations of defensive behavior unfolding within prefrontal networks.

Optogenetics is a powerful tool that allows experimentalists to perturb neural circuits. What can we learn about a network from observing its response to perturbations? I will first describe the results of optogenetic activation of inhibitory neurons in mice cortex, and show that the results are consistent with inhibition stabilization. I will then move to experiments in which excitatory neurons are activated optogenetically, with or without visual inputs, in mice and monkeys. In some conditions, these experiments show a surprising result that the distribution of firing rates is not significantly changed by stimulation, even though firing rates of individual neurons are strongly modified. I will show in which conditions a network model of excitatory and inhibitory neurons can reproduce this feature.

Recent advances in optogenetics allow for stimulation of neurons with sub-millisecond spike jitter and single neuron selectivity. Already this precision has revealed new levels of cortical sensitivity: stimulating tens of neurons can yield changes in the mean firing rate of thousands of similarly tuned neurons. This extreme sensitivity suggests that cortical dynamics are near criticality. Criticality is often studied in neural systems as a non-equilibrium thermodynamic process in which scale-free patterns of activity, called avalanches, emerge between distinct states of spontaneous activity. While criticality is well studied, it is still unclear what these distinct states of spontaneous activity are and what responses we expect from stimulation of this activity. By answering these questions, optogenetic stimulation will become a new avenue for approaching criticality and understanding cortical dynamics. Here, for the first time, we study the effects of optogenetic-like stimulation on a model near criticality. We study a model of Inhibitory/Excitatory (I/E) Leaky Integrate and Fire (LIF) spiking neurons which display a region of high sensitivity as seen in experiments. We find that this region of sensitivity is, indeed, near criticality. We derive the Dynamic Mean Field Theory of this model and find that the distinct states of activity are asynchrony and synchrony. We use our theory to characterize response to various types and strengths of optogenetic stimulation. Our model and theory predict that asynchronous, near-critical dynamics can have two qualitatively different responses to stimulation: one characterized by high sensitivity, discrete event responses, and high trial-to-trial variability, and another characterized by low sensitivity, continuous responses with characteristic frequencies, and low trial-to-trial variability. While both response types may be considered near-critical in model space, networks which are closest to criticality show a hybrid of these response effects.

Inter-areal coherence between cortical field-potentials is a widespread phenomenon and depends on numerous behavioral and cognitive factors. It has been hypothesized that inter-areal coherence reflects phase-synchronization between local oscillations and flexibly gates communication. We reveal an alternative mechanism, where coherence results from and is not the cause of communication, and naturally emerges as a consequence of the fact that spiking activity in a sending area causes post-synaptic inputs both in the same area and in other areas. Consequently, coherence depends in a lawful manner on oscillatory power and phase-locking in a sending area and inter-areal connectivity. We show that changes in oscillatory power explain prominent changes in fronto-parietal beta-coherence with movement and memory, and LGN-V1 gamma-coherence with arousal and visual stimulation. Optogenetic silencing of a receiving area and E/I network simulations demonstrate that afferent synaptic inputs rather than spiking entrainment are the main determinant of inter-areal coherence. These findings suggest that the unique spectral profiles of different brain areas automatically give rise to large-scale inter-areal coherence patterns that follow anatomical connectivity and continuously reconfigure as a function of behavior and cognition.

Ever since Bliss and Lømo discovered the phenomenon of long-term potentiation (LTP) in rabbit dentate gyrus in the 1960s, Hebb’s rule—neurons that fire together wire together—gained popularity to explain learning and memory. Accumulating evidence, however, suggests that neural activity is homeostatically regulated. Homeostatic mechanisms are mostly interpreted to stabilize network dynamics. However, recent theoretical work has shown that linking the activity of a neuron to its connectivity within the network provides a robust alternative implementation of Hebb’s rule, although entirely based on negative feedback. In this setting, both natural and artificial stimulation of neurons can robustly trigger network rewiring. We used computational models of plastic networks to simulate the complex temporal dynamics of network rewiring in response to external stimuli. In parallel, we performed optogenetic stimulation experiments in the mouse anterior cingulate cortex (ACC) and subsequently analyzed the temporal profile of morphological changes in the stimulated tissue. Our results suggest that the new theoretical framework combining neural activity homeostasis and structural plasticity provides a consistent explanation of our experimental observations.

In our everyday lives, we form connections and build up social networks that allow us to function successfully as individuals and as a society. Our social networks tend to include well-connected individuals who link us to other groups of people that we might otherwise have limited access to. In addition, we are more likely to befriend individuals who a) live nearby and b) have mutual friends. Interestingly, neurons tend to do the same…until development is perturbed. Just like social networks, neuronal networks require highly connected hubs to elicit efficient communication at minimal cost (you can’t befriend everybody you meet, nor can every neuron wire with every other!). This talk will cover some of Alex’s work showing that microscopic (cellular scale) brain networks inferred from spontaneous activity show similar complex topology to that previously described in macroscopic human brain scans. The talk will also discuss what happens when neurodevelopment is disrupted in the case of a monogenic disorder called Rett Syndrome. This will include simulations of neuronal activity and the effects of manipulation of model parameters as well as what happens when we manipulate real developing networks using optogenetics. If functional development can be restored in atypical networks, this may have implications for treatment of neurodevelopmental disorders like Rett Syndrome.

The ability to reach, grasp, and manipulate objects is a remarkable expression of motor skill, and the loss of this ability in injury, stroke, or disease can be devastating. These behaviors are controlled by the coordinated activity of tens of millions of neurons distributed across many CNS regions, including the primary motor cortex. While many studies have characterized the activity of single cortical neurons during reaching, the principles governing the dynamics of large, distributed neural populations remain largely unknown. Recent work in primates has suggested that during the execution of reaching, motor cortex may autonomously generate the neural pattern controlling the movement, much like the spinal central pattern generator for locomotion. In this seminar, I will describe recent work that tests this hypothesis using large-scale neural recording, high-resolution behavioral measurements, dynamical systems approaches to data analysis, and optogenetic perturbations in mice. We find, by contrast, that motor cortex requires strong, continuous, and time-varying thalamic input to generate the neural pattern driving reaching. In a second line of work, we demonstrate that the cortico-cerebellar loop is not critical for driving the arm towards the target, but instead fine-tunes movement parameters to enable precise and accurate behavior. Finally, I will describe my future plans to apply these experimental and analytical approaches to the adaptive control of locomotion in complex environments.

The ability to keep track of one’s location in space is a critical behavior for animals navigating to and from a salient location, and its computational basis is now beginning to be unraveled. Here, we tracked flies in a ring-shaped channel as they executed bouts of search triggered by optogenetic activation of sugar receptors. Unlike experiments in open field arenas, which produce highly tortuous search trajectories, our geometrically constrained paradigm enabled us to monitor flies’ decisions to move toward or away from the fictive food. Our results suggest that flies use path integration to remember the location of a food site even after it has disappeared, and flies can remember the location of a former food site even after walking around the arena one or more times. To determine the behavioral algorithms underlying Drosophila search, we developed multiple state transition models and found that flies likely accomplish path integration by combining odometry and compass navigation to keep track of their position relative to the fictive food. Our results indicate that whereas flies re-zero their path integrator at food when only one feeding site is present, they adjust their path integrator to a central location between sites when experiencing food at two or more locations. Together, this work provides a simple experimental paradigm and theoretical framework to advance investigations of the neural basis of path integration.

Coping with threatening situations requires both identifying stimuli predicting danger and selecting adaptive behavioral responses in order to survive. The dorso medial prefrontal cortex (dmPFC) is a critical structure involved in the regulation of threat-related behaviour, yet it is still largely unclear how threat-predicting stimuli and defensive behaviours are associated within prefrontal networks in order to successfully drive adaptive responses. To address these questions, we used a combination of extracellular recordings, neuronal decoding approaches, and optogenetic manipulations to show that threat representations and the initiation of avoidance behaviour are dynamically encoded in the overall population activity of dmPFC neurons. These data indicate that although dmPFC population activity at stimulus onset encodes sustained threat representations and discriminates threat- from non-threat cues, it does not predict action outcome. In contrast, transient dmPFC population activity prior to action initiation reliably predicts avoided from non-avoided trials. Accordingly, optogenetic inhibition of prefrontal activity critically constrained the selection of adaptive defensive responses in a time-dependent manner. These results reveal that the adaptive selection of active fear responses relies on a dynamic process of information linking threats with defensive actions unfolding within prefrontal networks.

My lab seeks to understand how the mammalian brain performs the computations that underlie cognitive functions, including decision-making, short-term memory, and spatial navigation, at the level of the building blocks of the nervous system, cell types and neural populations organized into circuits. We have developed methods to measure, manipulate, and analyze neural circuits across various spatial and temporal scales, including technology for virtual reality, optical imaging, optogenetics, intracellular electrophysiology, molecular sensors, and computational modeling. I will present recent work that uses large scale calcium imaging to reveal the functional organization of the mouse posterior cortex for flexible decision-making during spatial navigation in virtual reality. I will also discuss work that uses optogenetics and calcium imaging during a variety of decision-making tasks to highlight how cognitive experience and context greatly alter the cortical circuits necessary for navigation decisions.

Neural codes, such as temporal codes (precisely timed spikes) and rate codes (instantaneous spike firing rates), are believed to be used in encoding sensory information into spike trains of cortical neurons. Temporal and rate codes co-exist in the spike train and such multiplexed neural code-carrying spike trains have been shown to be spatially synchronized in multiple neurons across different cortical layers during sensory information processing. Inhibition is suggested to promote such synchronization, but it is unclear whether distinct subtypes of interneurons make different contributions in the synchronization of multiplexed neural codes. To test this, in vivo single-unit recordings from barrel cortex were combined with optogenetic manipulations to determine the contributions of parvalbumin (PV)- and somatostatin (SST)-positive interneurons to synchronization of precisely timed spike sequences. We found that PV interneurons preferentially promote the synchronization of spike times when instantaneous firing rates are low (<12 Hz), whereas SST interneurons preferentially promote the synchronization of spike times when instantaneous firing rates are high (>12 Hz). Furthermore, using a computational model, we demonstrate that these effects can be explained by PV and SST interneurons having preferential contribution to feedforward and feedback inhibition, respectively. Overall, these results show that PV and SST interneurons have distinct frequency (rate code)-selective roles in dynamically gating the synchronization of spike times (temporal code) through preferentially recruiting feedforward and feedback inhibitory circuit motifs. The inhibitory neural circuit mechanisms we uncovered here his may have critical roles in regulating neural code-based somatosensory information processing in the neocortex.

My lab studies the design principles of cytoskeletal materials the drive cellular morphogenesis, with a focus on contractile machinery in adherent cells. In addition to force generation, a key feature of these materials are distributed force sensors which allow for rapid assembly, adaptation, repair and disintegration. Here I will describe how optogenetic control of RhoA GTPase is a powerful and versatile force spectroscopy approach of cytoskeletal assemblies and its recent use to probe repair response in actomyosin stress fibers. I will also describe our recent identification of 18 proteins from the zyxin, paxillin, Tes and Enigma families with mechanosensitive LIM (Lin11, Isl- 1 & Mec-3) domains that bind exclusively to mechanically stressed actin filaments. Our results suggest that the evolutionary emergence of contractile F-actin machinery coincided with, or required, proteins that could report on the stresses present there to maintain homeostasis of actively stressed networks.

Olfactory navigation provides a tractable model for studying the circuit basis of sensori-motor transformations and goal-directed behaviour. Macroscopic organisms typically navigate in odor plumes that provide a noisy and uncertain signal about the location of an odor source. Work in many species has suggested that animals accomplish this task by combining temporal processing of dynamic odor information with an estimate of wind direction. Our lab has been using adult walking Drosophila to understand both the computational algorithms and the neural circuits that support navigation in a plume of attractive food odor. We developed a high-throughput paradigm to study behavioural responses to temporally-controlled odor and wind stimuli. Using this paradigm we found that flies respond to a food odor (apple cider vinegar) with two behaviours: during the odor they run upwind, while after odor loss they perform a local search. A simple computational model based one these two responses is sufficient to replicate many aspects of fly behaviour in a natural turbulent plume. In on-going work, we are seeking to identify the neural circuits and biophysical mechanisms that perform the computations delineated by our model. Using electrophysiology, we have identified mechanosensory neurons that compute wind direction from movements of the two antennae and central mechanosensory neurons that encode wind direction are are involved in generating a stable downwind orientation. Using optogenetic activation, we have traced olfactory circuits capable of evoking upwind orientation and offset search from the periphery, through the mushroom body and lateral horn, to the central complex. Finally, we have used optogenetic activation, in combination with molecular manipulation of specific synapses, to localize temporal computations performed on the odor signal to olfactory transduction and transmission at specific synapses. Our work illustrates how the tools available in fruit fly can be applied to dissect the mechanisms underlying a complex goal-directed behaviour.