@article {69185, title = {Brain-wide neural activity underlying memory-guided movement.}, journal = {Cell}, volume = {187}, year = {2024}, month = {2024 Feb 01}, pages = {676-691.e16}, abstract = {

Behavior relies on activity in structured neural circuits that are distributed across the brain, but most experiments probe neurons in a single area at a time. Using multiple Neuropixels probes, we recorded from multi-regional loops connected to the anterior lateral motor cortex (ALM), a circuit node mediating memory-guided directional licking. Neurons encoding sensory stimuli, choices, and actions were distributed across the brain. However, choice coding was concentrated in the ALM and subcortical areas receiving input from the ALM in an ALM-dependent manner. Diverse orofacial movements were encoded in the hindbrain; midbrain; and, to a lesser extent, forebrain. Choice signals were first detected in the ALM and the midbrain, followed by the thalamus and other brain areas. At movement initiation, choice-selective activity collapsed across the brain, followed by new activity patterns driving specific actions. Our experiments provide the foundation for neural circuit models of decision-making and movement initiation.

}, keywords = {Brain, Memory, Movement, Neurons, Thalamus}, issn = {1097-4172}, doi = {10.1016/j.cell.2023.12.035}, author = {Chen, Susu and Liu, Yi and Wang, Ziyue Aiden and Colonell, Jennifer and Liu, Liu D and Hou, Han and Tien, Nai-Wen and Wang, Tim and Harris, Timothy and Druckmann, Shaul and Li, Nuo and Svoboda, Karel} } @article {66007, title = {Targeted photostimulation uncovers circuit motifs supporting short-term memory.}, journal = {Nature Neuroscience}, volume = {24}, year = {2021}, month = {2021 Jan 25}, pages = {259-265}, abstract = {

Short-term memory is associated with persistent neural activity that is maintained by positive feedback between neurons. To explore the neural circuit motifs that produce memory-related persistent activity, we measured coupling between functionally characterized motor cortex neurons in mice performing a memory-guided response task. Targeted two-photon photostimulation of small (\<10) groups of neurons produced sparse calcium responses in coupled neurons over approximately 100 μm. Neurons with similar task-related selectivity were preferentially coupled. Photostimulation of different groups of neurons modulated activity in different subpopulations of coupled neurons. Responses of stimulated and coupled neurons persisted for seconds, far outlasting the duration of the photostimuli. Photostimuli produced behavioral biases that were predictable based on the selectivity of the perturbed neuronal population, even though photostimulation preceded the behavioral response by seconds. Our results suggest that memory-related neural circuits contain intercalated, recurrently connected modules, which can independently maintain selective persistent activity.

}, issn = {1546-1726}, doi = {10.1038/s41593-020-00776-3}, author = {Daie, Kayvon and Svoboda, Karel and Druckmann, Shaul} } @article {65726, title = {A comparison of neuronal population dynamics measured with calcium imaging and electrophysiology.}, journal = {PLoS Computational Biology}, volume = {16}, year = {2020}, month = {2020 Sep 15}, pages = {e1008198}, abstract = {

Calcium imaging with fluorescent protein sensors is widely used to record activity in neuronal populations. The transform between neural activity and calcium-related fluorescence involves nonlinearities and low-pass filtering, but the effects of the transformation on analyses of neural populations are not well understood. We compared neuronal spikes and fluorescence in matched neural populations in behaving mice. We report multiple discrepancies between analyses performed on the two types of data, including changes in single-neuron selectivity and population decoding. These were only partially resolved by spike inference algorithms applied to fluorescence. To model the relation between spiking and fluorescence we simultaneously recorded spikes and fluorescence from individual neurons. Using these recordings we developed a model transforming spike trains to synthetic-imaging data. The model recapitulated the differences in analyses. Our analysis highlights challenges in relating electrophysiology and imaging data, and suggests forward modeling as an effective way to understand differences between these data.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1008198}, author = {Wei, Ziqiang and Lin, Bei-Jung and Chen, Tsai-Wen and Daie, Kayvon and Svoboda, Karel and Druckmann, Shaul} } @article {51792, title = {An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability.}, journal = {Nature Communications}, volume = {10}, year = {2019}, month = {2019 Jan 15}, pages = {216}, abstract = {

Animals are not simple input-output machines. Their responses to even very similar stimuli are variable. A key, long-standing question in neuroscience is to understand the neural correlates of such behavioral variability. To reveal these correlates, behavior and neural population activity must be related to one another on single trials. Such analysis is challenging due to the dynamical nature of brain function (e.g., in decision making), heterogeneity across neurons and limited sampling of the relevant neural population. By analyzing population recordings from mouse frontal cortex in perceptual decision-making tasks, we show that an analysis approach tailored to the coarse grain features of the dynamics is able to reveal previously unrecognized structure in the organization of population activity. This structure is similar on error and correct trials, suggesting dynamics that may be constrained by the underlying circuitry, is able to predict multiple aspects of behavioral variability and reveals long time-scale modulation of population activity.

}, issn = {2041-1723}, doi = {10.1038/s41467-018-08141-6}, author = {Wei, Ziqiang and Inagaki, Hidehiko and Li, Nuo and Svoboda, Karel and Druckmann, Shaul} } @article {65162, title = {Single-cell reconstruction of emerging population activity in an entire developing circuit.}, journal = {Cell}, volume = {179}, year = {2019}, month = {2019 Sep 23}, abstract = {

Animal survival requires a functioning nervous system to develop during embryogenesis. Newborn neurons must assemble into circuits producing activity patterns capable of instructing behaviors. Elucidating how this process is coordinated requires new methods that follow maturation and activity of all cells across a developing circuit. We present an imaging method for comprehensively tracking neuron lineages, movements, molecular identities, and activity in the entire developing zebrafish spinal cord, from neurogenesis until the emergence of patterned activity instructing the earliest spontaneous motor behavior. We found that motoneurons are active first and form local patterned ensembles with neighboring neurons. These ensembles merge, synchronize globally after reaching a threshold size, and finally recruit commissural interneurons to orchestrate the left-right alternating patterns important for locomotion in vertebrates. Individual neurons undergo functional maturation stereotypically based on their birth time and anatomical origin. Our study provides a general strategy for reconstructing how functioning circuits emerge during embryogenesis.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2019.08.039}, author = {Wan, Yinan and Wei, Ziqiang and Looger, Loren L and Koyama, Minoru and Druckmann, Shaul and Keller, Philipp J} } @article {49477, title = {Active dendritic integration and mixed neocortical network representations during an adaptive sensing behavior.}, journal = {Nature Neuroscience}, volume = {21}, year = {2018}, month = {2018 Oct 22}, pages = {1583-90}, abstract = {

Animals strategically scan the environment to form an accurate perception of their surroundings. Here we investigated the neuronal representations that mediate this behavior. Ca imaging and selective optogenetic manipulation during an active sensing task reveals that layer 5 pyramidal neurons in the vibrissae cortex produce a diverse and distributed representation that is required for mice to adapt their whisking motor strategy to changing sensory cues. The optogenetic perturbation degraded single-neuron selectivity and network population encoding through a selective inhibition of active dendritic integration. Together the data indicate that active dendritic integration in pyramidal neurons produces a nonlinearly mixed network representation of joint sensorimotor parameters that is used to transform sensory information into motor commands during adaptive behavior. The prevalence of the layer 5 cortical circuit motif suggests that this is a general circuit computation.

}, issn = {1546-1726}, doi = {10.1038/s41593-018-0254-6}, author = {Ranganathan, Gayathri N and Apostolides, Pierre F and Harnett, Mark T and Xu, Ning-long and Druckmann, Shaul and Magee, Jeffrey C} } @article {49196, title = {Schaffer collateral inputs to CA1 excitatory and inhibitory neurons follow different connectivity rules.}, journal = {The Journal of Neuroscience : the official journal of the Society for Neuroscience}, volume = {38}, year = {2018}, month = {2018 May 04}, pages = {5140-52}, abstract = {

Neural circuits, governed by a complex interplay between excitatory and inhibitory neurons, are the substrate for information processing, and the organization of synaptic connectivity in neural network is an important determinant of circuit function. Here, we analyzed the fine structure of connectivity in hippocampal CA1 excitatory and inhibitory neurons innervated by Schaffer collaterals (SCs) using mGRASP in male mice. Our previous study revealed spatially structured synaptic connectivity between CA3-CA1 pyramidal cells (PCs). Surprisingly, parvalbumin-positive interneurons (PVs) showed a significantly more random pattern spatial structure. Notably, application of Peters{\textquoteright} Rule for synapse prediction by random overlap between axons and dendrites enhanced structured connectivity in PCs, but, by contrast, made the connectivity pattern in PVs more random. In addition, PCs in a deep sublayer of striatum pyramidale appeared more highly structured than PCs in superficial layers, and little or no sublayer specificity was found in PVs. Our results show that CA1 excitatory PCs and inhibitory PVs innervated by the same SC inputs follow different connectivity rules. The different organizations of fine scale structured connectivity in hippocampal excitatory and inhibitory neurons provide important insights into the development and functions of neural networks.Understanding how neural circuits generate behavior is one of the central goals of neuroscience. An important component of this endeavor is the mapping of fine-scale connection patterns that underlie, and help us infer, signal processing in the brain. Here, using our recently developed synapse detection technology (mGRASP and neuTube), we provide detailed profiles of synaptic connectivity in excitatory (CA1 pyramidal) and inhibitory (CA1 parvalbumin-positive) neurons innervated by the same presynaptic inputs (CA3 Schaffer collaterals). Our results reveal that these two types of CA1 neurons follow different connectivity patterns. Our new evidence for differently structured connectivity at a fine scale in hippocampal excitatory and inhibitory neurons provides a better understanding of hippocampal networks and will guide theoretical and experimental studies.

}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.0155-18.2018}, author = {Kwon, Osung and Feng, Linqing and Druckmann, Shaul and Kim, Jinhyun} } @article {48507, title = {Angular velocity integration in a fly heading circuit.}, journal = {eLife}, volume = {6}, year = {2017}, month = {2017 May 22}, abstract = {

Many animals maintain an internal representation of their heading as they move through their surroundings. Such a compass representation was recently discovered in a neural population in the Drosophila melanogaster central complex, a brain region implicated in spatial navigation. Here, we use two-photon calcium imaging and electrophysiology in head-fixed walking flies to identify a different neural population that conjunctively encodes heading and angular velocity, and is excited selectively by turns in either the clockwise or counterclockwise direction. We show how these mirror-symmetric turn responses combine with the neurons{\textquoteright} connectivity to the compass neurons to create an elegant mechanism for updating the fly{\textquoteright}s heading representation when the animal turns in darkness. This mechanism, which employs recurrent loops with an angular shift, bears a resemblance to those proposed in theoretical models for rodent head direction cells. Our results provide a striking example of structure matching function for a broadly relevant computation.

}, issn = {2050-084X}, doi = {10.7554/eLife.23496}, author = {Turner-Evans, Daniel and Wegener, Stephanie and Rouault, Herv{\'e} and Franconville, Romain and Wolff, Tanya and Seelig, Johannes D and Druckmann, Shaul and Jayaraman, Vivek} } @article {48459, title = {Maintenance of persistent activity in a frontal thalamocortical loop.}, journal = {Nature}, volume = {545}, year = {2017}, month = {2017 May 03}, pages = {181-6}, abstract = {

Persistent neural activity maintains information that connects past and future events. Models of persistent activity often invoke reverberations within local cortical circuits, but long-range circuits could also contribute. Neurons in the mouse anterior lateral motor cortex (ALM) have been shown to have selective persistent activity that instructs future actions. The ALM is connected bidirectionally with parts of the thalamus, including the ventral medial and ventral anterior-lateral nuclei. We recorded spikes from the ALM and thalamus during tactile discrimination with a delayed directional response. Here we show that, similar to ALM neurons, thalamic neurons exhibited selective persistent delay activity that predicted movement direction. Unilateral photoinhibition of delay activity in the ALM or thalamus produced contralesional neglect. Photoinhibition of the thalamus caused a short-latency and near-complete collapse of ALM activity. Similarly, photoinhibition of the ALM diminished thalamic activity. Our results show that the thalamus is a circuit hub in motor preparation and suggest that persistent activity requires reciprocal excitation across multiple brain areas.

}, issn = {1476-4687}, doi = {10.1038/nature22324}, author = {Guo, Zengcai V and Inagaki, Hidehiko K and Daie, Kayvon and Druckmann, Shaul and Gerfen, Charles R and Svoboda, Karel} } @article {48462, title = {Ring attractor dynamics in the Drosophila central brain.}, journal = {Science (New York, N.Y.)}, volume = {356}, year = {2017}, month = {2017 May 04}, pages = {849-53}, abstract = {

Ring attractors are a class of recurrent networks hypothesized to underlie the representation of heading direction. Such network structures, schematized as a ring of neurons whose connectivity depends on their heading preferences, can sustain a bump-like activity pattern whose location can be updated by continuous shifts along either turn direction. We recently reported that a population of fly neurons represents the animal{\textquoteright}s heading via bump-like activity dynamics. We combined two-photon calcium imaging in head-fixed flying flies with optogenetics to overwrite the existing population representation with an artificial one, which was then maintained by the circuit with naturalistic dynamics. A network with local excitation and global inhibition enforces this unique and persistent heading representation. Ring attractor networks have long been invoked in theoretical work; our study provides physiological evidence of their existence and functional architecture.

}, issn = {1095-9203}, doi = {10.1126/science.aal4835}, author = {Kim, Sung S and Rouault, Herv{\'e} and Druckmann, Shaul and Jayaraman, Vivek} } @article {47425, title = {Robust neuronal dynamics in premotor cortex during motor planning.}, journal = {Nature}, year = {2016}, month = {2016 Apr 13}, abstract = {

Neural activity maintains representations that bridge past and future events, often over many seconds. Network models can produce persistent and ramping activity, but the positive feedback that is critical for these slow dynamics can cause sensitivity to perturbations. Here we use electrophysiology and optogenetic perturbations in the mouse premotor cortex to probe the robustness of persistent neural representations during motor planning. We show that preparatory activity is remarkably robust to large-scale unilateral silencing: detailed neural dynamics that drive specific future movements were quickly and selectively restored by the network. Selectivity did not recover after bilateral silencing of the premotor cortex. Perturbations to one hemisphere are thus corrected by information from the other hemisphere. Corpus callosum bisections demonstrated that premotor cortex hemispheres can maintain preparatory activity independently. Redundancy across selectively coupled modules, as we observed in the premotor cortex, is a hallmark of robust control systems. Network models incorporating these principles show robustness that is consistent with data.

}, issn = {1476-4687}, doi = {10.1038/nature17643}, author = {Li, Nuo and Daie, Kayvon and Svoboda, Karel and Druckmann, Shaul} } @article {46835, title = {Dynamical feature extraction at the sensory periphery guides chemotaxis.}, journal = {eLife}, volume = {4}, year = {2015}, month = {2015 Jun 16}, abstract = {

Behavioral strategies employed for chemotaxis have been described across phyla, but the sensorimotor basis of this phenomenon has seldom been studied in naturalistic contexts. Here, we examine how signals experienced during free olfactory behaviors are processed by first-order olfactory sensory neurons (OSNs) of the Drosophila larva. We find that OSNs can act as differentiators that transiently normalize stimulus intensity-a property potentially derived from a combination of integral feedback and feed-forward regulation of olfactory transduction. In olfactory virtual reality experiments, we report that high activity levels of the OSN suppress turning, whereas low activity levels facilitate turning. Using a generalized linear model, we explain how peripheral encoding of olfactory stimuli modulates the probability of switching from a run to a turn. Our work clarifies the link between computations carried out at the sensory periphery and action selection underlying navigation in odor gradients.

}, issn = {2050-084X}, doi = {10.7554/eLife.06694}, author = {Schulze, Aljoscha and Gomez-Marin, Alex and Rajendran, Vani G and Lott, Gus and Musy, Marco and Ahammad, Parvez and Deogade, Ajinkya and Sharpe, James and Riedl, Julia and Jarriault, David and Trautman, Eric T and Werner, Christopher and Venkadesan, Madhusudhan and Druckmann, Shaul and Jayaraman, Vivek and Louis, Matthieu} } @article {46862, title = {From a meso- to micro-scale connectome: array tomography and mGRASP.}, journal = {Frontiers in Neuroanatomy}, volume = {9}, year = {2015}, month = {2015 Jun 04}, pages = {78}, abstract = {

Mapping mammalian synaptic connectivity has long been an important goal of neuroscience because knowing how neurons and brain areas are connected underpins an understanding of brain function. Meeting this goal requires advanced techniques with single synapse resolution and large-scale capacity, especially at multiple scales tethering the meso- and micro-scale connectome. Among several advanced LM-based connectome technologies, Array Tomography (AT) and mammalian GFP-Reconstitution Across Synaptic Partners (mGRASP) can provide relatively high-throughput mapping synaptic connectivity at multiple scales. AT- and mGRASP-assisted circuit mapping (ATing and mGRASPing), combined with techniques such as retrograde virus, brain clearing techniques, and activity indicators will help unlock the secrets of complex neural circuits. Here, we discuss these useful new tools to enable mapping of brain circuits at multiple scales, some functional implications of spatial synaptic distribution, and future challenges and directions of these endeavors.

}, issn = {1662-5129}, doi = {10.3389/fnana.2015.00078}, author = {Rah, Jong-Cheol and Feng, Linqing and Druckmann, Shaul and Lee, Hojin and Kim, Jinhyun} } @article {5661, title = {A mechanistic model of early sensory processing based on subtracting sparse representations.}, journal = {Advances in Neural Information Processing Systems}, volume = {25}, year = {2012}, month = {2012}, pages = {1979-87}, abstract = {

Early stages of sensory systems face the challenge of compressing information from numerous receptors onto a much smaller number of projection neurons, a so called communication bottleneck. To make more efficient use of limited bandwidth, compression may be achieved using predictive coding, whereby predictable, or redundant, components of the stimulus are removed. In the case of the retina, Srinivasan et al. (1982) suggested that feedforward inhibitory connections subtracting a linear prediction generated from nearby receptors implement such compression, resulting in biphasic center-surround receptive fields. However, feedback inhibitory circuits are common in early sensory circuits and furthermore their dynamics may be nonlinear. Can such circuits implement predictive coding as well? Here, solving the transient dynamics of nonlinear reciprocal feedback circuits through analogy to a signal-processing algorithm called linearized Bregman iteration we show that nonlinear predictive coding can be implemented in an inhibitory feedback circuit. In response to a step stimulus, interneuron activity in time constructs progressively less sparse but more accurate representations of the stimulus, a temporally evolving prediction. This analysis provides a powerful theoretical framework to interpret and understand the dynamics of early sensory processing in a variety of physiological experiments and yields novel predictions regarding the relation between activity and stimulus statistics.

}, author = {Druckmann, Shaul and Hu, T and Chklovskii, D} } @article {5660, title = {Effective stimuli for constructing reliable neuron models.}, journal = {PLoS Computational Biology}, volume = {7}, year = {2011}, month = {2011 Aug}, pages = {e1002133}, abstract = {

The rich dynamical nature of neurons poses major conceptual and technical challenges for unraveling their nonlinear membrane properties. Traditionally, various current waveforms have been injected at the soma to probe neuron dynamics, but the rationale for selecting specific stimuli has never been rigorously justified. The present experimental and theoretical study proposes a novel framework, inspired by learning theory, for objectively selecting the stimuli that best unravel the neuron{\textquoteright}s dynamics. The efficacy of stimuli is assessed in terms of their ability to constrain the parameter space of biophysically detailed conductance-based models that faithfully replicate the neuron{\textquoteright}s dynamics as attested by their ability to generalize well to the neuron{\textquoteright}s response to novel experimental stimuli. We used this framework to evaluate a variety of stimuli in different types of cortical neurons, ages and animals. Despite their simplicity, a set of stimuli consisting of step and ramp current pulses outperforms synaptic-like noisy stimuli in revealing the dynamics of these neurons. The general framework that we propose paves a new way for defining, evaluating and standardizing effective electrical probing of neurons and will thus lay the foundation for a much deeper understanding of the electrical nature of these highly sophisticated and non-linear devices and of the neuronal networks that they compose.

}, keywords = {Action Potentials, Algorithms, Animals, Computational Biology, Electric Conductivity, Electrophysiology, Interneurons, Mice, Models, Neurological, Neurons, Patch-Clamp Techniques, Pyramidal Cells, Rats, Wistar}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1002133}, author = {Druckmann, Shaul and Berger, Thomas K and Sch{\"u}rmann, Felix and Hill, Sean and Markram, Henry and Segev, Idan} } @article {5827, title = {Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data.}, journal = {Biological Cybernetics}, volume = {99}, year = {2008}, month = {2008 Nov}, pages = {371-9}, abstract = {

Neuron models, in particular conductance-based compartmental models, often have numerous parameters that cannot be directly determined experimentally and must be constrained by an optimization procedure. A common practice in evaluating the utility of such procedures is using a previously developed model to generate surrogate data (e.g., traces of spikes following step current pulses) and then challenging the algorithm to recover the original parameters (e.g., the value of maximal ion channel conductances) that were used to generate the data. In this fashion, the success or failure of the model fitting procedure to find the original parameters can be easily determined. Here we show that some model fitting procedures that provide an excellent fit in the case of such model-to-model comparisons provide ill-balanced results when applied to experimental data. The main reason is that surrogate and experimental data test different aspects of the algorithm{\textquoteright}s function. When considering model-generated surrogate data, the algorithm is required to locate a perfect solution that is known to exist. In contrast, when considering experimental target data, there is no guarantee that a perfect solution is part of the search space. In this case, the optimization procedure must rank all imperfect approximations and ultimately select the best approximation. This aspect is not tested at all when considering surrogate data since at least one perfect solution is known to exist (the original parameters) making all approximations unnecessary. Furthermore, we demonstrate that distance functions based on extracting a set of features from the target data (such as time-to-first-spike, spike width, spike frequency, etc.){\textendash}rather than using the original data (e.g., the whole spike trace) as the target for fitting-are capable of finding imperfect solutions that are good approximations of the experimental data.

}, keywords = {Algorithms, Animals, Models, Neocortex, Neurological, Neurons, Organ Culture Techniques, Patch-Clamp Techniques, Rats, Wistar}, issn = {1432-0770}, doi = {10.1007/s00422-008-0269-2}, author = {Druckmann, Shaul and Berger, Thomas K and Hill, Sean and Sch{\"u}rmann, Felix and Markram, Henry and Segev, Idan} } @article {5828, title = {A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data.}, journal = {Frontiers in Neuroscience}, volume = {1}, year = {2007}, month = {2007 Nov}, pages = {7-18}, abstract = {

We present a novel framework for automatically constraining parameters of compartmental models of neurons, given a large set of experimentally measured responses of these neurons. In experiments, intrinsic noise gives rise to a large variability (e.g., in firing pattern) in the voltage responses to repetitions of the exact same input. Thus, the common approach of fitting models by attempting to perfectly replicate, point by point, a single chosen trace out of the spectrum of variable responses does not seem to do justice to the data. In addition, finding a single error function that faithfully characterizes the distance between two spiking traces is not a trivial pursuit. To address these issues, one can adopt a multiple objective optimization approach that allows the use of several error functions jointly. When more than one error function is available, the comparison between experimental voltage traces and model response can be performed on the basis of individual features of interest (e.g., spike rate, spike width). Each feature can be compared between model and experimental mean, in units of its experimental variability, thereby incorporating into the fitting this variability. We demonstrate the success of this approach, when used in conjunction with genetic algorithm optimization, in generating an excellent fit between model behavior and the firing pattern of two distinct electrical classes of cortical interneurons, accommodating and fast-spiking. We argue that the multiple, diverse models generated by this method could serve as the building blocks for the realistic simulation of large neuronal networks.

}, issn = {1662-453X}, doi = {10.3389/neuro.01.1.1.001.2007}, author = {Druckmann, Shaul and Banitt, Yoav and Gidon, Albert and Sch{\"u}rmann, Felix and Markram, Henry and Segev, Idan} }