Biology subjects

Romani, S.

Publications and source records attributed to Romani, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Plateau-gated one-shot plasticity supports continual recognition memory

Biological memory systems store single experiences while continuing to learn, but how one-shot plasticity limits interference with existing memories is unclear. Behavioral timescale synaptic plasticity (BTSP) rapidly modifies synapses active within seconds of a dendritic plateau. We isolate its plateau-triggered component in a model where plastic weights and a stable instructive pathway jointly determine whether a plateau occurs, closing a feedback loop between the synaptic state and the plastic event that modifies it. For unstructured inputs, instantiated by independent uniform signed patterns, the dynamics reduce exactly to pathway alignment, which determines the fidelity of the instructed representation, the synaptic-turnover rate and the mean rewrite interval. For structured inputs, instantiated by correlated bimodal Curie--Weiss patterns, the instructive pathway biases which component of input structure enters the plastic synaptic state. In a BTSP-inspired continual-recognition network, combined instructive and plastic drives determined the selected memory unit for each one-shot write, whereas a Hebbian control used the same plastic weights for credit assignment and memory storage. The BTSP-inspired network remained accurate at longer repeat lags than Hebbian controls, an advantage that grew with network size, with both architectures optimized independently at every repeat lag. A reduced theory predicted held-out accuracy, lag capacity and dynamics of memory-trace strength directly from optimized parameters. It showed why intermediate proximal and distal coupling was optimal: proximal plastic drive guided plateau generation toward selected memory units, slowing synaptic turnover but limiting new encoding, whereas distal instructive drive enhanced familiar responses but could also make novel inputs appear familiar. The memory-trace strength in the rate-and-depth-matched Hebbian control still decayed faster and showed less effective credit assignment than in the BTSP-inspired network. These results connect dendritic plateau physiology to continual memory and support partial separation of allocation from storage as a mechanism for limiting interference during continual learning.

neuroscience↗

bioRxiv2026

Hidden symmetries in network connectivity support ring attractor dynamics in the fly's neural compass

Theoretical models can explain how network structure shapes neural computation, but they typically assume idealized connectivity that is inconsistent with the heterogeneous wiring of biological circuits. We address this issue in the Drosophila head-direction system, a recurrent network with ring-attractor dynamics that enable angular velocity integration. The networks symmetric wiring motifs are reminiscent of classical models, but with additional heterogeneity that should, in principle, destabilize attractor dynamics. Inspired by novel architectures discovered through machine-learning-based optimization, we develop an algorithm that transforms attractor models with symmetric connectivity into functionally equivalent models with heterogeneous connectivity. By replacing each unit with multiple clones that preserve its output, the algorithm embeds hidden symmetries in heterogeneous connectivity, maintaining ring-attractor dynamics and accurate integration. Analysis of multiple fly connectomes provides evidence for duplicated units whose connectivity reflects hidden symmetries, consistent with our theory. Our framework helps reconcile idealized models of neural computation with heterogeneous biological circuits.

neuroscience↗

bioRxiv2025

The organization of visual pathways in the Drosophila brain

Visual systems across species transform photoreceptor inputs into diverse perceptual representations through hierarchical networks that extract features via parallel pathways. In Drosophila, the optic lobes are layered, retinotopic visual processing centers that contain two-thirds of the brains neurons and support diverse visually guided behaviors. Although this architecture has long suggested hierarchical and parallel organization, a system-wide account of how behaviorally relevant visual features are routed and integrated across a complete visual system--in any animal--has remained elusive. The new male fly connectome now provides the synapse-level wiring needed to trace visual information from photoreceptors through the optic lobes and across the central brain. Applying a network-based analysis of information flow, we reveal a multi-layered architecture organized into distinct, functionally interpretable pathways. Using this framework to propagate signals through these pathways predicts receptive-field structure and feature selectivity consistent with physiological data, enabling large-scale functional annotation of thousands of neuron types. We find that distinct visual input channels are broadly distributed throughout the brain, yet converge in focal regions of feature specificity and acute spatial vision. Together, these analyses provide a neuron-level, connectome-based view of how a brain organizes and transforms visual input.

neuroscience↗

bioRxiv2025

Signatures of remote planning in hippocampal replay

During brief, intermittent "replay" events, hippocampal activity can express navigational trajectories disconnected from both when and where they originally occurred. While replay biased toward immediate future goals has been observed, there is no evidence yet linking replay to planning beyond the next action. Here, we designed a sequential spatial working memory task which required rats to utilize information across multiple temporally separated actions. Remote replay events matched the animals future navigational choices made after completing an intervening subtask. Critically, this occurred only when the replayed information was useful for reducing memory load, consistent with it being an active process. Our findings suggest these remote replay events are a neural correlate of episodic forethought, allowing animals to use memories to plan beyond their immediate surroundings.

neuroscience↗

bioRxiv2025

Memory traces bias new learning for hippocampal generalization

The ability to use generalized prior experience to guide behavior in novel situations is a fundamental cognitive function1. While recent evidence suggests that the hippocampus supports generalization how this is accomplished is poorly understood2-9. Here we combined longitudinal optical imaging in head-fixed mice with computational modeling to examine generalization in hippocampal area CA1. We found that prior training accelerated behavioral adaptation to a novel environment and that this was accompanied by highly stable hippocampal representations. We identified putative memory traces from prior experience that enabled this generalization at multiple levels. At the population level, novel-context network dynamics rapidly aligned with low-dimensional neural subspaces10 established during prior experience. At the cellular level, spatially-informative weak "residual" activity reflecting generalizable information about the task structure appeared to bias which neurons form place fields (PFs) and where via behavioral timescale synaptic plasticity (BTSP)11,12. Finally, this was an active process as many PFs changed their reference frame in the novel environment to reflect the consistent task structure. In sum, the influence of memory traces on new PF formation may allow past experience to guide new learning such that representations are based on generalizable features, thus enabling rapid adaptive behavior in new contexts.

neuroscience↗

bioRxiv2024

From the fly connectome to exact ring attractor dynamics

A cognitive compass enabling spatial navigation requires the neural representation of head direction (HD), yet the neural circuit architecture enabling this representation remains unclear. While various network models have been proposed to explain HD systems, these models rely on simplified circuit architectures that may be irreconcilable with empirical observations from connectomes. Here we construct a neural network model for the fruit fly HD system that satisfies both connectome-derived architectural constraints and the functional requirement of continuous head-direction representation. To achieve this, we first characterized an ensemble of continuous attractor networks where compass neurons providing local mutual excitation are coupled to inhibitory neurons. Our multipopulation model allowed us to discover a new class of ring attractor network with weaker symmetry requirements than usually assumed. For each of the four available fly connectomes, our analyses uncovered three distinct realizations of these networks. Furthermore, we found that synaptic variations among and around the fly connectomes can be compensated by cell-type-specific rescaling of synaptic weights, which could be potentially achieved through neuromodulation. The fly connectomes are ideally configured to take advantage of this mechanism, suggesting a novel design principle linking synapse-resolution connectivity to network computation.

neuroscience↗

bioRxiv2024

Bridging tuning and invariance with equivariant neuronal representations

As we move through the world, we see the same visual scene from different perspectives. But how does the brain encode scene identity invariant to perspective, while remaining sensitive to these transformations? We propose a solution through equivariance, where perspective transformations induce structured changes in neuronal population responses. This framework implies a decomposition of population responses into orthogonal subspaces that are tuned and invariant. Testing our framework with large-scale neuronal recordings across four mouse visual cortical areas, we find that the equivariant structure is more pronounced in some higher-order areas (LM, AL) than in other areas (V1, RL). This equivariant structure accounts for the observed simultaneous increase in both population tuning and invariance. In comparison, early layers of an artificial neural network trained on image classification show similar structure, but later layers increase invariance at the cost of tuning. These results suggest equivariance is a principle to achieve flexible computations with neuronal populations.

neuroscience↗

bioRxiv2023

A comprehensive neuroanatomical survey of the Drosophila Lobula Plate Tangential Neurons with predictions for their optic flow sensitivity

Flying insects exhibit remarkable navigational abilities controlled by their compact nervous systems. Optic flow, the pattern of changes in the visual scene induced by locomotion, is a crucial sensory cue for robust self-motion estimation, especially during rapid flight. Neurons that respond to specific, large-field optic flow patterns have been studied for decades, primarily in large flies, such as houseflies, blowflies, and hover flies. The best-known optic-flow sensitive neurons are the large tangential cells of the dipteran lobula plate, whose visual-motion responses, and to a lesser extent, their morphology, have been explored using single-neuron neurophysiology. Most of these studies have focused on the large, Horizontal and Vertical System neurons, yet the lobula plate houses a much larger set of optic-flow sensitive neurons, many of which have been challenging to unambiguously identify or to reliably target for functional studies. Here we report the comprehensive reconstruction and identification of the Lobula Plate Tangential Neurons in an Electron Microscopy (EM) volume of a whole Drosophila brain. This catalog of 58 LPT neurons (per brain hemisphere) contains many neurons that are described here for the first time and provides a basis for systematic investigation of the circuitry linking self-motion to locomotion control. Leveraging computational anatomy methods, we estimated the visual motion receptive fields of these neurons and compared their tuning to the visual consequence of body rotations and translational movements. We also matched these neurons, in most cases on a one-for-one basis, to stochastically labeled cells in genetic driver lines, to the mirror-symmetric neurons in the same EM brain volume, and to neurons in an additional EM data set. Using cell matches across data sets, we analyzed the integration of optic flow patterns by neurons downstream of the LPTs and find that most central brain neurons establish sharper selectivity for global optic flow patterns than their input neurons. Furthermore, we found that self-motion information extracted from optic flow is processed in distinct regions of the central brain, pointing to diverse foci for the generation of visual behaviors.

neuroscience↗

bioRxiv2023

Cell-type-specific plasticity shapes neocortical dynamics for motor learning

Neocortical spiking dynamics control aspects of behavior, yet how these dynamics emerge during motor learning remains elusive. Activity-dependent synaptic plasticity is likely a key mechanism, as it reconfigures network architectures that govern neural dynamics. Here, we examined how the mouse premotor cortex acquires its well-characterized neural dynamics that control movement timing, specifically lick timing. To probe the role of synaptic plasticity, we have genetically manipulated proteins essential for major forms of synaptic plasticity, Ca2+/calmodulin-dependent protein kinase II (CaMKII) and Cofilin, in a region and cell-type-specific manner. Transient inactivation of CaMKII in the premotor cortex blocked learning of new lick timing without affecting the execution of learned action or ongoing spiking activity. Furthermore, among the major glutamatergic neurons in the premotor cortex, CaMKII and Cofilin activity in pyramidal tract (PT) neurons, but not intratelencephalic (IT) neurons, is necessary for learning. High-density electrophysiology in the premotor cortex uncovered that neural dynamics anticipating licks are progressively shaped during learning, which explains the change in lick timing. Such reconfiguration in behaviorally relevant dynamics is impeded by CaMKII manipulation in PT neurons. Altogether, the activity of plasticity-related proteins in PT neurons plays a central role in sculpting neocortical dynamics to learn new behavior.

neuroscience↗

bioRxiv2023

Mechanisms of memory storage and retrieval in hippocampal area CA3

Hippocampal area CA3 is thought to play a central role in memory formation and retrieval. Although various network mechanisms have been hypothesized to mediate these computations, direct evidence is lacking. Using intracellular membrane potential recordings from CA3 neurons and optogenetic manipulations in behaving mice we found that place field activity is produced by a symmetric form of Behavioral Timescale Synaptic Plasticity (BTSP) at recurrent synaptic connections among CA3 principal neurons but not at synapses from the dentate gyrus (DG). Additional manipulations revealed that excitatory input from the entorhinal cortex (EC) but not DG was required to update place cell activity based on the animals movement. These data were captured by a computational model that used BTSP and an external updating input to produce attractor dynamics under online learning conditions. Additional theoretical results demonstrate the enhanced memory storage capacity of such networks, particularly in the face of correlated input patterns. The evidence sheds light on the cellular and circuit mechanisms of learning and memory formation in the hippocampus. One Sentence SummaryEvidence from behaving mice points to cellular and circuit mechanisms that underlie observed attractor dynamics in area CA3.

neuroscience↗

bioRxiv2022

Accurate angular integration with only a handful of neurons

To flexibly navigate, many animals rely on internal spatial representations that persist when the animal is standing still in darkness, and update accurately by integrating the animals movements in the absence of localizing sensory cues. Theories of mammalian head direction cells have proposed that these dynamics can be realized in a special class of networks that maintain a localized bump of activity via structured recurrent connectivity, and that shift this bump of activity via angular velocity input. Although there are many different variants of these so-called ring attractor networks, they all rely on large numbers of neurons to generate representations that persist in the absence of input and accurately integrate angular velocity input. Surprisingly, in the fly, Drosophila melanogaster, a head direction representation is maintained by a much smaller number of neurons whose dynamics and connectivity resemble those of a ring attractor network. These findings challenge our understanding of ring attractors and their putative implementation in neural circuits. Here, we analyzed failures of angular velocity integration that emerge in small attractor networks with only a few computational units. Motivated by the peak performance of the fly head direction system in darkness, we mathematically derived conditions under which small networks, even with as few as 4 neurons, achieve the performance of much larger networks. The resulting description reveals that by appropriately tuning the network connectivity, the network can maintain persistent representations over the continuum of head directions, and it can accurately integrate angular velocity inputs. We then analytically determined how performance degrades as the connectivity deviates from this optimally-tuned setting, and we find a trade-off between network size and the tuning precision needed to achieve persistence and accurate integration. This work shows how even small networks can accurately track an animals movements to guide navigation, and it informs our understanding of the functional capabilities of discrete systems more broadly.

neuroscience↗

bioRxiv2021

Non-preferred contrast responses in the Drosophila motion pathways reveal a receptive field structure that explains a common visual illusion

Diverse sensory systems, from audition to thermosensation, feature a separation of inputs into ON (increments) and OFF (decrements) signals. In the Drosophila visual system, separate ON and OFF pathways compute the direction of motion, yet anatomical and functional studies have identified some crosstalk between these channels. We used this well-studied circuit to ask whether the motion computation depends on ON-OFF pathway crosstalk. Using whole-cell electrophysiology we recorded visual responses of T4 (ON) and T5 (OFF) cells and discovered that both cell types are also directionally selective in response to non-preferred contrast motion. We mapped T4s and T5s composite ON-OFF receptive fields and found they share a similar spatiotemporal structure. We fit a biophysical model to these receptive fields that accurately predicts directionally selective T4 and T5 responses to both ON and OFF moving stimuli. This model also provides a detailed mechanistic explanation for the directional-preference inversion in response to a prominent visual illusion, a result we corroborate with electrophysiological recordings and behavioral responses of flying flies.

neuroscience↗

bioRxiv2020

Bidirectional synaptic plasticity rapidly modifies hippocampal representations independent of correlated activity

Learning requires neural adaptations thought to be mediated by activity-dependent synaptic plasticity. A relatively non-standard form of synaptic plasticity driven by dendritic plateau potentials has been reported to underlie place field formation in hippocampal CA1 neurons. Here we found that this behavioral timescale synaptic plasticity (BTSP) can also reshape existing place fields via bidirectional synaptic weight changes that depend on the temporal proximity of plateau potentials to pre-existing place fields. When evoked near an existing place field, plateau potentials induced less synaptic potentiation and more depression, suggesting BTSP might depend inversely on postsynaptic activation. However, manipulations of place cell membrane potential and computational modeling indicated that this anti-correlation actually results from a dependence on current synaptic weight such that weak inputs potentiate and strong inputs depress. A network model implementing this bidirectional synaptic learning rule suggested that BTSP enables population activity, rather than pairwise neuronal correlations, to drive neural adaptations to experience.

neuroscience↗

bioRxiv2019

Attractor dynamics gate cortical information flow during decision-making

Decisions about future actions are held in memory until enacted, making them vulnerable to distractors. The neural mechanisms controlling decision robustness to distractors remain unknown. We trained mice to report optogenetic stimulation of somatosensory cortex, with a delay separating sensation and action. Distracting stimuli influenced behavior less when delivered later during delay - demonstrating temporal gating of sensory information flow. Gating occurred even though distractor-evoked activity percolated through the cortex without attenuation. Instead, choice-related dynamics in frontal cortex became progressively robust to distractors as time passed. Reverse-engineering of neural networks trained to reproduce frontal-cortex activity revealed that chosen actions were stabilized via attractor dynamics, which gated out distracting stimuli. Our results reveal a dynamic gating mechanism that operates by controlling the degree of commitment to a chosen course of action. One Sentence SummaryMechanisms controlling state-dependent communication between brain regions allow for robust action-selection.

neuroscience↗

bioRxiv2019

Dynamic control of cortical head-direction signal by angular velocity

The sense of direction requires accurate tracking of head direction at different turning-velocities, yet it remains unclear how this is achieved in the mammalian brain. Here we recorded head-direction cells in bat dorsal presubiculum and found that, surprisingly, the head-direction signal in this cortical region was dynamically controlled by angular velocity. In most neurons, a sharp head-direction tuning emerged at some angular velocity, but was absent at other velocities - resulting in a 4-fold increase in head-direction cell abundance. The head-direction tuning changed as a function of angular velocity primarily via a redistribution of spikes between the neurons preferred and null directions - while keeping the average firing-rate constant. These results could not be explained by existing ring-attractor models of the head-direction system. We propose a novel recurrent network model that accounts for the observed dynamics of head-direction cells. This model predicts that the new classes of cells we found can improve the sensitivity of the head-direction system to directional sensory cues, and support angular-velocity integration.

neuroscience↗

bioRxiv2019

The computation of directional selectivity in the Drosophila OFF motion pathway

The direction of visual motion in Drosophila is computed by separate pathways for moving ON and OFF features. The 4th order neurons T4 (ON) and T5 (OFF) are the first neurons in their respective pathways to extract a directionally selective response from their non-selective inputs. Recent functional studies have found a major role for local inhibition in the generation of directionally selective responses. However, T5 lacks small-field inhibitory inputs. Here we use whole-cell recordings of T5 neurons and find an asymmetric receptive field structure, with fast excitation and persistent, spatially trailing inhibition. We assayed pairwise interactions of local stimulation across the receptive field, and find no active amplification, only passive suppression. We constructed a biophysical model of T5 based on the classic Receptive Field. This model, which lacks active conductances and was tuned only to match non-moving stimuli, accurately predicts responses to complex moving stimuli.

neuroscience↗

bioRxiv2019

The statistical structure of the hippocampal code for space as a function of time, context, and value

Hippocampal activity represents many behaviorally important variables, including context, an animals location within a given environmental context, time, and reward. Here we used longitudinal calcium imaging in mice, multiple large virtual environments, and differing reward contingencies to derive a unified probabilistic model of hippocampal CA1 representations centered on a single feature - the field propensity. Each cells propensity governs how many place fields it has per unit space, predicts its reward-related activity, and is preserved across distinct environments and over months. The propensity is broadly distributed--with many low, and some very high, propensity cells --and thus strongly shapes hippocampal representations. The result is a range of spatial codes, from sparse to dense. Propensity varied ~10-fold between adjacent cells in a salt-and-pepper fashion, indicating substantial functional differences within a presumed cell type. The stability of each cells propensity across conditions suggests this fundamental property has anatomical, transcriptional, and/or developmental origins.

neuroscience↗

bioRxiv2017

Low-dimensional and monotonic preparatory activity in mouse anterior lateral motor cortex

Neurons in multiple brain regions fire trains of action potentials anticipating specific movements, but this preparatory activity has rarely been compared across behavioral tasks in the same brain region. We compared preparatory activity in auditory and tactile delayed-response tasks, with directional licking as the output. The anterior lateral motor cortex (ALM) is necessary for motor planning in both tasks. Multiple features of ALM preparatory activity during the delay epoch were similar across tasks. First, majority of neurons showed direction-selective activity and spatially intermingled neurons were selective for either movement direction. Second, many cells showed mixed coding of sensory stimulus and licking direction, with a bias toward licking direction. Third, delay activity was largely monotonic and low-dimensional. Fourth, pairs of neurons with similar direction selectivity showed high spike-count correlations. Our study forms the foundation to analyze the neural circuits underlying preparatory activity in a genetically tractable model organism.

neuroscience↗