Biology subjects

Litwin-Kumar, A.

Publications and source records attributed to Litwin-Kumar, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Neural circuit mechanisms for multi-tasking

Neural activity during performance of a cognitive or motor task is often described as lying on a low-dimensional manifold in the space of all possible activity patterns. When multiple tasks are performed, an individual brain region's activity may generate multiple such manifolds. Several models have been introduced to explain this capability, each with different assumptions concerning architecture and inputs. Here, we develop a theory of multi-tasking according to which models and experimental paradigms can be organized. We introduce a distinction between sequential and simultaneous multi-tasking, the performance of one task at a time or multiple in parallel. We show that interference between tasks often prevents multi-tasking and introduce a model of ``task barcodes" that circumvents this problem and parameterizes a neuronal population's task selectivity. Our theory reveals categorical distinctions between neural circuit mechanisms supporting multi-tasking that can be probed using physiological recordings.

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bioRxiv2025

A theory of multi-task computation and task selection

Neural activity during the performance of a stereotyped behavioral task is often described as low-dimensional, occupying only a limited region in the space of all firing-rate patterns. This region has been referred to as the "neural manifold" associated with a task. More recently, recordings of neural activity in animals challenged to perform multiple tasks have suggested that each task is associated with a different low-dimensional manifold. What connectivity structures underlie this flexibility in neural dynamics, and how is interference between the dynamics associated with different tasks avoided? We develop a theoretical model for multi-task computation in nonlinear recurrent neural networks whose connectivity is constructed as a weighted sum of many low-rank components, each encoding the dynamics associated with a different task. The model demonstrates that interference between different tasks dynamics limits flexible multi-tasking and can lead to chaotic fluctuations. However, small modulations of a networks effective connectivity overcome this interference. We derive the conditions that enable such task selection and characterize both single-neuron and population statistics in task-selected and unselected states. The model reveals the requirements for a single network to produce distinct dynamics confined to distinct neural manifolds and suggests circuit mechanisms that support this capability. Using the model, we propose different hypotheses for explaining the origin of high-dimensional neural activity in large-scale recordings.

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bioRxiv2025

Graph embeddings for identifying symmetries in connectomes

In many circuit models of neural computation, synaptic connections between neurons are organized according to their tuning to the variables being processed. The connectivities of canonical neural network models of head direction, spatial navigation, and orientation selectivity obey this principle and contain symmetries related to the angular and spatial variables they operate on. We develop a graph embedding algorithm to identify such symmetries. The algorithm segregates structure related to cell type from that related to the symmetries we seek to identify, distinguishing it from standard embedding methods. Our method successfully identifies rotational and translational symmetries in heading direction and visual projection neuron circuits using a connectome of the adult Drosophila brain, and it also identifies a toroidal symmetry in a synthetic connectome of grid cells in the medial entorhinal cortex. Such embedding geometries reveal the latent variables that are processed by a neural circuit and which cell types are responsible for this computation.

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bioRxiv2025

Avoidance engages dopaminergic punishment in Drosophila

It was classically suggested that behaviour can cause emotions (Darwin 1872). For example, smiling can make us feel happier, and in rodents the induced patterns of cardiac activity and breathing that are indicative of fear can in turn evoke it (Coles et al. 2022, Hsueh et al. 2023, Jhang et al. 2024). However, the adaptive significance of such feedback is unclear. We show that inducing backward movement, an element of avoidance behaviour in Drosophila, engages negative valence signals in these animals, and reveal the neuronal mechanisms and adaptive significance of this effect. We develop a paradigm with odours as conditioned stimuli paired with optogenetically induced backward movement instead of a punishing unconditioned stimulus, and combined these experiments with pharmacology, high-resolution video tracking, functional imaging, connectome analyses, and modelling. Our results show that backward movement engages dopaminergic punishment neurons and supports aversive memories. Such avoidance-to-punishment feedback counterbalances extinction learning and maintains learned avoidance, reducing the risk of further punishment. This can explain the long-standing "avoidance paradox", the observation that avoidance adaptively persists even when it is successful and no punishment is received (Bolles 1972). Our results provide a neurobiologically grounded argument for an integrated view of behaviour organization and valence processing.

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bioRxiv2025

Experience-dependent reorganization of inhibitory neuron synaptic connectivity

Organisms continually tune their perceptual systems to the features they encounter in their environment1-3. We have studied how ongoing experience reorganizes the synaptic connectivity of neurons in the olfactory (piriform) cortex of the mouse. We developed an approach to measure synaptic connectivity in vivo, training a deep convolutional network to reliably identify monosynaptic connections from the spike-time cross-correlograms of 4.4 million single-unit pairs. This revealed that excitatory piriform neurons with similar odor tuning are more likely to be connected. We asked whether experience enhances this like-to-like connectivity but found that it was unaffected by odor exposure. Experience did, however, alter the logic of interneuron connectivity. Following repeated encounters with a set of odorants, inhibitory neurons that responded differentially to these stimuli exhibited a high degree of both incoming and outgoing synaptic connections within the cortical network. This reorganization depended only on the odor tuning of the inhibitory interneuron and not on the tuning of its pre- or postsynaptic partners. A computational model of this reorganized connectivity predicts that it increases the dimensionality of the entire networks responses to familiar stimuli, thereby enhancing their discriminability. We confirmed that this network-level property is present in physiological measurements, which showed increased dimensionality and separability of the evoked responses to familiar versus novel odorants. Thus, a simple, non-Hebbian reorganization of interneuron connectivity may selectively enhance an organisms discrimination of the features of its environment.

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bioRxiv2024

Spatially organized striatal neuromodulator release encodes trajectory errors

Goal-directed navigation requires animals to continuously evaluate their current direction and speed of travel relative to landmarks to discern whether they are approaching or deviating from their goal. Striatal dopamine and acetylcholine are powerful modulators of goal-directed behavior, but it is unclear whether and how neuromodulator dynamics at landmarks incorporate relative motion for effective behavioral guidance. Using optical measurements in mice, we demonstrate that cue-evoked striatal dopamine release encodes bi-directional trajectory errors reflecting relationships between ongoing speed and direction of locomotion and visual flow relative to optimal goal trajectories. Striatum-wide micro-fiber array recordings resolved an anatomical gradient of trajectory error signaling across the anterior-posterior axis, distinct from trajectory error independent cue signals. Dynamic regression modeling revealed that positive and negative trajectory error encoding emerges early and late respectively during learning and over different time courses in the medial and lateral striatum, enabling region specific contributions to learning. Striatal acetylcholine release also encodes trajectory errors, but encoding is more spatially restricted, opposite polarity, and delayed relative to dopamine, supporting distinct roles in modulating striatal output and behavior. Dopamine trajectory error signaling and task performance were reproduced in a reinforcement learning model incorporating a conjunctive state space representation, suggesting a potential neural substrate for trajectory error generation. Our results establish region specific neuromodulator signals positioned to guide the speed and direction of locomotion to reach goals based on environmental landmarks during navigation.

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bioRxiv2024

Prediction of neural activity in connectome-constrained recurrent networks

We develop a theory of connectome-constrained neural networks in which a "student" network is trained to reproduce the activity of a ground-truth "teacher," representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, here the two networks have the same connectivity but different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We find that a connectome is often insufficient to constrain the dynamics of networks that perform a specific task, illustrating the difficulty of inferring function from connectivity alone. However, recordings from a small subset of neurons can remove this degeneracy, producing dynamics in the student that agree with the teacher. Our theory can also prioritize which neurons to record from to most efficiently predict unmeasured network activity. Our analysis shows that the solution spaces of connectome-constrained and unconstrained models are qualitatively different and provides a framework to determine when such models yield consistent dynamics.

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bioRxiv2024

Dynamics of striatal action selection and reinforcement learning

Spiny projection neurons (SPNs) in dorsal striatum are often proposed as a locus of reinforcement learning in the basal ganglia. Here, we identify and resolve a fundamental inconsistency between striatal reinforcement learning models and known SPN synaptic plasticity rules. Direct-pathway (dSPN) and indirect-pathway (iSPN) neurons, which promote and suppress actions, respectively, exhibit synaptic plasticity that reinforces activity associated with elevated or suppressed dopamine release. We show that iSPN plasticity prevents successful learning, as it reinforces activity patterns associated with negative outcomes. However, this pathological behavior is reversed if functionally opponent dSPNs and iSPNs, which promote and suppress the current behavior, are simultaneously activated by efferent input following action selection. This prediction is supported by striatal recordings and contrasts with prior models of SPN representations. In our model, learning and action selection signals can be multiplexed without interference, enabling learning algorithms beyond those of standard temporal difference models.

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bioRxiv2023

Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body

The arthropod mushroom body is well-studied as an expansion layer that represents olfactory stimuli and links them to contingent events. However, 8% of mushroom body Kenyon cells in Drosophila melanogaster receive predominantly visual input, and their tuning and function are poorly understood. Here, we use the FlyWire adult whole-brain connectome to identify inputs to visual Kenyon cells. The types of visual neurons we identify are similar across hemispheres and connectomes with certain inputs highly overrepresented. Many visual projection neurons presynaptic to Kenyon cells receive input from large swathes of visual space, while local visual interneurons, providing smaller fractions of input, receive more spatially restricted signals that may be tuned to specific features of the visual scene. Like olfactory Kenyon cells, visual Kenyon cells receive sparse inputs from different combinations of visual channels, including inputs from multiple optic lobe neuropils. The sets of inputs to individual visual Kenyon cells are consistent with random sampling of available inputs. These connectivity patterns suggest that visual coding in the mushroom body, like olfactory coding, is sparse, distributed, and combinatorial. However, the expansion coding properties appear different, with a specific repertoire of visual inputs projecting onto a relatively small number of visual Kenyon cells.

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bioRxiv2022

Theory of systems memory consolidation via recall-gated plasticity

In a variety of species and behavioral contexts, learning and memory formation recruits two neural systems, with initial plasticity in one system being consolidated into the other over time. Moreover, consolidation is known to be selective; that is, some experiences are more likely to be consolidated into long-term memory than others. Here, we propose and analyze a model that captures common computational principles underlying such phenomena. The key component of this model is a mechanism by which a long-term learning and memory system prioritizes the storage of synaptic changes that are consistent with prior updates to the short-term system. This mechanism, which we refer to as recall-gated consolidation, has the effect of shielding long-term memory from spurious synaptic changes, enabling it to focus on reliable signals in the environment. We describe neural circuit implementations of this model for different types of learning problems, including supervised learning, reinforcement learning, and autoassociative memory storage. These implementations involve synaptic plasticity rules modulated by factors such as prediction accuracy, decision confidence, or familiarity. We then develop an analytical theory of the learning and memory performance of the model, in comparison to alternatives relying only on synapse-local consolidation mechanisms. We find that recall-gated consolidation provides significant advantages, substantially amplifying the signal-to-noise ratio with which memories can be stored in noisy environments. We show that recall-gated consolidation gives rise to a number of phenomena that are present in behavioral learning paradigms, including spaced learning effects, task-dependent rates of consolidation, and differing neural representations in short- and long-term pathways.

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bioRxiv2022

Specific connectivity optimizes learning in thalamocortical loops

1Cortico-thalamo-cortical loops have a central role in cognition and motor control, but precisely how thalamus contributes to these processes is unclear. Recent studies showing evidence of plasticity in thalamocortical synapses indicate a role for thalamus in shaping cortical dynamics - and thus behavior - through learning. Since corticothalamic projections compress cortical activity into a lower-dimensional thalamic activity space, we hypothesized that the computational role of thalamus would depend critically on the structure of corticothalamic connectivity. To test this, we identified the optimal corticothalamic structure that promotes biologically plausible learning in thalamocortical synapses. We found that corticothalamic structures specialized to carry an efference copy of the cortical output benefit motor control, while corticothalamic connections that communicate the directions of highest variance in cortical activity are optimal for working memory tasks. We analyzed neural recordings from mice performing grasping and delayed discrimination, and found corticothalamic interactions consistent with these predictions. These results suggest that thalamus orchestrates cortical dynamics in a functionally precise manner through structured connectivity.

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bioRxiv2022

Task-dependent optimal representations for cerebellarlearning

The cerebellar granule cell layer has inspired numerous theoretical models of neural representations that support learned behaviors, beginning with the work of Marr and Albus. In these models, granule cells form a sparse, combinatorial encoding of diverse sensorimotor inputs. Such sparse representations are optimal for learning to discriminate random stimuli. However, recent observations of dense, low-dimensional activity across granule cells have called into question the role of sparse coding in these neurons. Here, we generalize theories of cerebellar learning to determine the optimal granule cell representation for tasks beyond random stimulus discrimination, including continuous input-output transformations as required for smooth motor control. We show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classic theories. Our results provide a general theory of learning in cerebellum-like systems and suggest that optimal cerebellar representations are task-dependent.

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bioRxiv2022

Hierarchical architecture of dopaminergic circuits enables second-order conditioning in Drosophila

Dopaminergic neurons with distinct projection patterns and physiological properties compose memory subsystems in a brain. However, it is poorly understood whether or how they interact during complex learning. Here, we identify a feedforward circuit formed between dopamine subsystems and show that it is essential for second-order conditioning, an ethologically important form of higher-order associative learning. The Drosophila mushroom body comprises a series of dopaminergic compartments, each of which exhibits distinct memory dynamics. We find that a slow and stable memory compartment can serve as an effective "teacher" by instructing other faster and transient memory compartments via a single key interneuron, which we identify by connectome analysis and neurotransmitter prediction. This excitatory interneuron acquires enhanced response to reward-predicting odor after first-order conditioning and, upon activation, evokes dopamine release in the "student" compartments. These hierarchical connections between dopamine subsystems explain distinct properties of first- and second-order memory long known by behavioral psychologists.

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bioRxiv2022

Optimal routing to cerebellum-like structures

The vast expansion from mossy fibers to cerebellar granule cells produces a neural representation that supports functions including associative and internal model learning. This motif is shared by other cerebellum-like structures, including the insect mushroom body, electrosensory lobe of electric fish, and mammalian dorsal cochlear nucleus, and has inspired numerous theoretical models of its functional role. Less attention has been paid to structures immediately presynaptic to granule cell layers, whose architecture can be described as a "bottleneck" and whose functional role is not understood. We therefore develop a general theory of cerebellum-like structures in conjunction with their afferent pathways. This theory predicts the role of the pontine relay to cerebellar cortex and the glomerular organization of the insect antennal lobe. It also reconciles theories of nonlinear mixing with recent observations of correlated granule cell activity. More generally, it shows that structured compression followed by random expansion is an efficient architecture for flexible computation.

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bioRxiv2021

Mushroom body input connections form independently of sensory activity in Drosophila melanogaster

Associative brain centers, such as the insect mushroom body, need to represent sensory information in an efficient manner. In Drosophila melanogaster, the Kenyon cells of the mushroom body integrate inputs from a random set of olfactory projection neurons, but some projection neurons -- namely those activated by a few ethologically meaningful odors -- connect to Kenyon cells more frequently than others. This biased and random connectivity pattern is conceivably advantageous, as it enables the mushroom body to represent a large number of odors as unique activity patterns while prioritizing the representation of a few specific odors. How this connectivity pattern is established remains largely unknown. Here, we test whether the mechanisms patterning the connections between Kenyon cells and projection neurons depend on sensory activity or whether they are hardwired. We mapped a large number of mushroom body input connections in anosmic flies -- flies lacking the obligate odorant co-receptor Orco -- and in wildtype flies. Statistical analyses of these datasets reveal that the random and biased connectivity pattern observed between Kenyon cells and projection neurons forms normally in the absence of most olfactory sensory activity. This finding supports the idea that even comparatively subtle, population-level patterns of neuronal connectivity can be encoded by fixed genetic programs and are likely to be the result of evolved prioritization of ecologically and ethologically salient stimuli.

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bioRxiv2020

The connectome of the adult Drosophila mushroom body: implications for function

Making inferences about the computations performed by neuronal circuits from synapse-level connectivity maps is an emerging opportunity in neuroscience. The mushroom body (MB) is well positioned for developing and testing such an approach due to its conserved neuronal architecture, recently completed dense connectome, and extensive prior experimental studies of its roles in learning, memory and activity regulation. Here we identify new components of the MB circuit in Drosophila, including extensive visual input and MB output neurons (MBONs) with direct connections to descending neurons. We find unexpected structure in sensory inputs, in the transfer of information about different sensory modalities to MBONs, and in the modulation of that transfer by dopaminergic neurons (DANs). We provide insights into the circuitry used to integrate MB outputs, connectivity between the MB and the central complex and inputs to DANs, including feedback from MBONs. Our results provide a foundation for further theoretical and experimental work.

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bioRxiv2019

Context-dependent decision making in a premotor circuit

Cognitive capacities afford contingent associations between sensory information and behavioral responses. We studied this problem using an olfactory delayed match to sample task whereby a sample odor specifies the association between a subsequent test odor and rewarding action. Multi-neuron recordings revealed representations of the sample and test odors in olfactory sensory and association cortex, which were sufficient to identify the test odor as match/non-match. Yet, inactivation of a downstream premotor area (ALM), but not orbitofrontal cortex, confined to the epoch preceding the test odor, led to gross impairment. Olfactory decisions that were not context dependent were unimpaired. Therefore, ALM may not receive the outcome of a match/non-match decision from upstream areas but contextual information--the identity of the sample--to establish the mapping between test odor and action. A novel population of pyramidal neurons in ALM layer 2 may mediate this process.

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bioRxiv2019

Models of heterogeneous dopamine signaling in an insect learning and memory center

The Drosophila mushroom body exhibits dopamine dependent synaptic plasticity that underlies the acquisition of associative memories. Recordings of dopamine neurons in this system have identified signals related to external reinforcement such as reward and punishment. However, other factors including locomotion, novelty, reward expectation, and internal state have also recently been shown to modulate dopamine neurons. This heterogeneity is at odds with typical modeling approaches in which these neurons are assumed to encode a global, scalar error signal. How is dopamine dependent plasticity coordinated in the presence of such heterogeneity? We develop a modeling approach that infers a pattern of dopamine activity sufficient to solve defined behavioral tasks, given architectural constraints informed by knowledge of mushroom body circuitry. Model dopamine neurons exhibit diverse tuning to task parameters while nonetheless producing coherent learned behaviors. Our results provide a mechanistic framework that accounts for the heterogeneity of dopamine activity during learning and behavior.

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