@article {70202, title = {Memory traces bias new learning for hippocampal generalization}, journal = {bioRxiv}, year = {2025}, month = {2025 Nov 25}, abstract = {

The ability to use generalized prior experience to guide behavior in novel situations is a fundamental cognitive function. While recent evidence suggests that the hippocampus supports generalization how this is accomplished is poorly understood. 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 subspaces 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). 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.

}, doi = {10.1101/2025.11.24.690297}, url = {https://www.biorxiv.org/content/early/2025/11/25/2025.11.24.690297}, author = {Qian, Fish Kunxun and Li, Guanchun and Lipshutz, David and Romani, Sandro and Magee, Jeffrey Charles} } @article {70243, title = {The organization of visual pathways in the Drosophila brain}, journal = {bioRxiv}, year = {2025}, month = {2025 Dec 26}, abstract = {

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 brain{\textquoteright}s 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{\textemdash}in any animal{\textemdash}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.

}, doi = {10.64898/2025.12.22.696097}, url = {https://www.biorxiv.org/content/early/2025/12/26/2025.12.22.696097}, author = {Hoeller, Judith and Zhao, Arthur and Nern, Aljoscha and Rogers, Edward M and Romani, Sandro and Reiser, Michael B} } @article {70155, title = {Sexual dimorphism in the complete connectome of the Drosophila male central nervous system}, journal = {bioRxiv}, year = {2025}, month = {2025 Oct 09}, abstract = {

Sex differences in behaviour exist across the animal kingdom, typically under strong genetic regulation. In Drosophila, previous work has shown that fruitless and doublesex transcription factors identify neurons driving sexually dimorphic behaviour. However, the organisation of dimorphic neurons into functional circuits remains unclear.We now present the connectome of the entire Drosophila male central nervous system. This contains 166,691 neurons spanning the brain and ventral nerve cord, fully proofread and comprehensively annotated including fruitless and doublesex expression and 11,691 cell types. By comparison with a previous female brain connectome, we provide the first comprehensive description of the differences between male and female brains to synaptic resolution. Of 7,319 cross-matched cell types in the central brain, 114 are dimorphic with an additional 262 male- and 69 female-specific (totalling 4.8\% of neurons in males and 2.4\% in females).This resource enables analysis of full sensory-to-motor circuits underlying complex behaviours as well as the impact of dimorphic elements. Sex-specific and dimorphic neurons are concentrated in higher brain centres while the sensory and motor periphery are largely isomorphic. Within higher centres, male-specific connections are organised into hotspots defined by male-specific neurons or the presence of male-specific arbours on neurons that are otherwise similar between sexes. Numerous circuit switches reroute sensory information to form conserved, antagonistic circuits controlling opposing behaviours.

}, doi = {10.1101/2025.10.09.680999}, url = {https://www.biorxiv.org/content/early/2025/10/09/2025.10.09.680999}, author = {Berg, Stuart and Beckett, Isabella R and Costa, Marta and Schlegel, Philipp and Januszewski, Micha{\l} and Marin, Elizabeth C and Nern, Aljoscha and Preibisch, Stephan and Qiu, Wei and Takemura, Shin-ya and Fragniere, Alexandra MC and Champion, Andrew S and Adjavon, Diane-Yayra and Cook, Michael and Gkantia, Marina and Hayworth, Kenneth J and Huang, Gary B and Katz, William T and K{\"a}mpf, Florian and Lu, Zhiyuan and Ordish, Christopher and Paterson, Tyler and St{\"u}rner, Tomke and Trautman, Eric T and Whittle, Catherine R and Burnett, Laura E and Hoeller, Judith and Li, Feng and Loesche, Frank and Morris, Billy J and Pietzsch, Tobias and Pleijzier, Markus W and Silva, Valeria and Yin, Yijie and Ali, Iris and Badalamente, Griffin and Bates, Alexander Shakeel and Bogovic, John and Brooks, Paul and Cachero, Sebastian and Canino, Brandon S and Chaisrisawatsuk, Bhumpanya and Clements, Jody and Crowe, Arthur and de Haan Vicente, In{\^e}s and Dempsey, Georgia and Don{\`a}, Erika and dos Santos, M{\'a}rcia and Dreher, Marisa and Dunne, Christopher R and Eichler, Katharina and Finley-May, Samantha and Flynn, Miriam A and Hameed, Imran and Hopkins, Gary Patrick and Hubbard, Philip M and Kiassat, Ladann and Kovalyak, Julie and Lauchie, Shirley A and Leonard, Meghan and Lohff, Alanna and Longden, Kit D and Maldonado, Charli A and Mitletton, Myrto and Moitra, Ilina and Moon, Sung Soo and Mooney, Caroline and Munnelly, Eva J and Okeoma, Nneoma and Olbris, Donald J and Pai, Anika and Patel, Birava and Phillips, Emily M and Plaza, Stephen M and Richards, Alana and Rivas Salinas, Jennifer and Roberts, Ruair{\'\i} Jv and Rogers, Edward M and Scott, Ashley L and Scuderi, Louis A and Seenivasan, Pavithraa and Serratosa Capdevila, Laia and Smith, Claire and Svirskas, Rob and Takemura, Satoko and Tastekin, Ibrahim and Thomson, Alexander and Umayam, Lowell and Walsh, John J and Whittome, Holly and Xu, C Shan and Yakal, Emily A and Yang, Tansy and Zhao, Arthur and George, Reed and Jain, Viren and Jayaraman, Vivek and Korff, Wyatt and Meissner, Geoffrey W and Romani, Sandro and Funke, Jan and Knecht, Christopher and Saalfeld, Stephan and Scheffer, Louis K and Waddell, Scott and Card, Gwyneth M and Ribeiro, Carlos and Reiser, Michael B and Hess, Harald F and Rubin, Gerald M and Jefferis, Gregory Sxe} } @article {70215, title = {Signatures of remote planning in hippocampal replay}, journal = {bioRxiv}, year = {2025}, month = {2025 Dec 04}, abstract = {

During brief, intermittent {\textquotedblleft}replay{\textquotedblright} 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 animal{\textquoteright}s 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.

}, doi = {10.64898/2025.12.02.691753}, url = {https://www.biorxiv.org/content/early/2025/12/04/2025.12.02.691753}, author = {Lustig, Brian and Wang, Yingxue and Romani, Sandro and Pastalkova, Eva and Lee, Albert K.} } @article {69557, title = {Bridging tuning and invariance with equivariant neuronal representations}, journal = {bioRxiv}, year = {2024}, month = {2024 Aug 02}, abstract = {

As we move through the world, we see the same visual scenes from different perspectives. Although we experience perspective deformations, our perception of a scene remains stable. This raises the question of which neuronal representations in visual brain areas are perspective-tuned and which are invariant. Focusing on planar rotations, we introduce a mathematical framework based on the principle of equivariance, which asserts that an image rotation results in a corresponding rotation of neuronal representations, to explain how the same representation can range from being fully tuned to fully invariant. We applied this framework to large-scale simultaneous neuronal recordings from four visual cortical areas in mice, where we found that representations are both tuned and invariant but become more invariant across higher-order areas. While common deep convolutional neural networks show similar trends in orientation-invariance across layers, they are not rotation-equivariant. We propose that equivariance is a prevalent computation of populations of biological neurons to gradually achieve invariance through structured tuning.

}, doi = {10.1101/2024.08.02.606398}, url = {https://www.doi.org/10.1101/2024.08.02.606398}, author = {Hoeller, Judith and Zhong, Lin and Pachitariu, Marius and Romani, Sandro} } @article {69660, title = {From the fly connectome to exact ring attractor dynamics}, journal = {bioRxiv}, year = {2024}, month = {2024 Nov 01}, abstract = {

A cognitive compass enabling spatial navigation requires neural representation of heading 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 are incompatible with empirical observations from connectomes. Here we construct a novel network model for the fruit fly HD system that satisfies both connectome-derived architectural constraints and the functional requirement of continuous heading representation. We characterize an ensemble of continuous attractor networks where compass neurons providing local mutual excitation are coupled to inhibitory neurons. We discover a new mechanism where continuous heading representation emerges from combining symmetric and anti-symmetric activity patterns. Our analysis reveals three distinct realizations of these networks that all match observed compass neuron activity but differ in their predictions for inhibitory neuron activation patterns. Further, we found that deviations from these realizations can be compensated by cell-type-specific rescaling of synaptic weights, which could be potentially achieved through neuromodulation. This framework can be extended to incorporate the complete fly central complex connectome and could reveal principles of neural circuits representing other continuous quantities, such as spatial location, across insects and vertebrates.

}, doi = {10.1101/2024.11.01.621596}, url = {https://doi.org/10.1101/2024.11.01.621596}, author = {Biswas, Tirthabir and Stanoev, Angel and Romani, Sandro and Fitzgerald, James E.} } @article {69632, title = {Maintaining and updating accurate internal representations of continuous variables with a handful of neurons.}, journal = {Nat Neurosci}, year = {2024}, month = {2024 Oct 03}, abstract = {

Many animals rely on persistent internal representations of continuous variables for working memory, navigation, and motor control. Existing theories typically assume that large networks of neurons are required to maintain such representations accurately; networks with few neurons are thought to generate discrete representations. However, analysis of two-photon calcium imaging data from tethered flies walking in darkness suggests that their small head-direction system can maintain a surprisingly continuous and accurate representation. We thus ask whether it is possible for a small network to generate a continuous, rather than discrete, representation of such a variable. We show analytically that even very small networks can be tuned to maintain continuous internal representations, but this comes at the cost of sensitivity to noise and variations in tuning. This work expands the computational repertoire of small networks, and raises the possibility that larger networks could represent more and higher-dimensional variables than previously thought.

}, issn = {1546-1726}, doi = {10.1038/s41593-024-01766-5}, url = {https://10.1038/s41593-024-01766-5}, author = {Noorman, Marcella and Hulse, Brad K and Jayaraman, Vivek and Romani, Sandro and Hermundstad, Ann M} } @article {67965, title = {Neural Algorithms and Circuits for Motor Planning.}, journal = {Annual Reviews Neuroscience}, year = {2022}, month = {2022 Feb 22}, abstract = {

The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits. Expected final online publication date for the , Volume 45 is July 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

}, issn = {1545-4126}, doi = {10.1146/annurev-neuro-092021-121730}, author = {Inagaki, Hidehiko K and Chen, Susu and Daie, Kayvon and Finklestein, Arseny and Fontolan, Lorenzo and Romani, Sandro and Svoboda, Karel} } @article {68224, title = {Neural Algorithms and Circuits for Motor Planning.}, journal = {Annual Review Neuroscience}, volume = {45}, year = {2022}, month = {2022 Jul 08}, pages = {249-271}, abstract = {

The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.

}, keywords = {Algorithms, Animals, Brain, Motor Cortex, Movement, Neurons}, issn = {1545-4126}, doi = {10.1146/annurev-neuro-092021-121730}, author = {Inagaki, Hidehiko K and Chen, Susu and Daie, Kayvon and Finkelstein, Arseny and Fontolan, Lorenzo and Romani, Sandro and Svoboda, Karel} } @article {67231, title = {Attractor dynamics gate cortical information flow during decision-making.}, journal = {Nature Neuroscience}, volume = {24}, year = {2021}, month = {2021 Jun 1}, pages = {843-50}, abstract = {

Decisions are held in memory until enacted, which makes them potentially vulnerable to distracting sensory input. Gating of information flow from sensory to motor areas could protect memory from interference during decision-making, but the underlying network mechanisms are not understood. Here, we trained mice to detect optogenetic stimulation of the somatosensory cortex, with a delay separating sensation and action. During the delay, distracting stimuli lost influence on behavior over time, even though distractor-evoked neural activity percolated through the cortex without attenuation. Instead, choice-encoding activity in the motor cortex became progressively less sensitive to the impact of distractors. Reverse engineering of neural networks trained to reproduce motor cortex activity revealed that the reduction in sensitivity to distractors was caused by a growing separation in the neural activity space between attractors that encode alternative decisions. Our results show that communication between brain regions can be gated via attractor dynamics, which control the degree of commitment to an action.

}, issn = {1546-1726}, doi = {10.1038/s41593-021-00840-6}, author = {Finkelstein, Arseny and Fontolan, Lorenzo and Economo, Michael N and Li, Nuo and Romani, Sandro and Svoboda, Karel} } @article {67822, title = {Bidirectional synaptic plasticity rapidly modifies hippocampal representations.}, journal = {eLife}, volume = {10}, year = {2021}, month = {2021 Dec 09}, abstract = {

Learning requires neural adaptations thought to be mediated by activity-dependent synaptic plasticity. A relatively non-standard form of synaptic plasticity driven by dendritic calcium spikes, or plateau potentials, has been reported to underlie place field formation in rodent 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.

}, issn = {2050-084X}, doi = {10.7554/eLife.73046}, author = {Milstein, Aaron D and Li, Yiding and Bittner, Katie C and Grienberger, Christine and Soltesz, Ivan and Magee, Jeffrey C and Romani, Sandro} } @article {66023, title = {Mapping Low-Dimensional Dynamics to High-Dimensional Neural Activity: A Derivation of the Ring Model from the Neural Engineering Framework.}, journal = {Neural Computation}, volume = {33}, year = {2021}, month = {2021 Mar 01}, pages = {827-52}, abstract = {

Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed neural network models. These experimental and computational results suggest that mapping low-dimensional dynamics to high-dimensional neural space is a common feature of cortical computation. Despite the ubiquity of this observation, the constraints arising from such mapping are poorly understood. Here we consider a specific example of mapping low-dimensional dynamics to high-dimensional neural activity-the neural engineering framework. We analytically solve the framework for the classic ring model-a neural network encoding a static or dynamic angular variable. Our results provide a complete characterization of the success and failure modes for this model. Based on similarities between this and other frameworks, we speculate that these results could apply to more general scenarios.

}, issn = {1530-888X}, doi = {10.1162/neco_a_01361}, author = {Barak, Omri and Romani, Sandro} } @article {67773, title = {Non-preferred contrast responses in the Drosophila motion pathways reveal a receptive field structure that explains a common visual illusion.}, journal = {Current Biology}, volume = {31}, year = {2021}, month = {2021 Dec 06}, pages = {5286}, abstract = {

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, mapped their composite ON-OFF receptive fields, and found that 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 the prominent reverse-phi illusion. Finally, we used the steering responses of tethered flying flies to validate the model{\textquoteright}s predicted effects of varying stimulus parameters on the behavioral turning inversion.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2021.09.072}, author = {Gruntman, Eyal and Reimers, Pablo and Romani, Sandro and Reiser, Michael B} } @article {65283, title = {Fundamental law of memory recall.}, journal = {Physical Review Letters}, volume = {124}, year = {2020}, month = {2020 Jan 10}, pages = {018101}, abstract = {

Human memory appears to be fragile and unpredictable. Free recall of random lists of words is a standard paradigm used to probe episodic memory. We proposed an associative search process that can be reduced to a deterministic walk on random graphs defined by the structure of memory representations. The corresponding graph model can be solved analytically, resulting in a novel parameter-free prediction for the average number of memory items recalled (R) out of M items in memory: R=sqrt[3πM/2]. This prediction was verified with a specially designed experimental protocol combining large-scale crowd-sourced free recall and recognition experiments with randomly assembled lists of words or common facts. Our results show that human memory can be described by universal laws derived from first principles.

}, issn = {1079-7114}, doi = {10.1103/PhysRevLett.124.018101}, author = {Naim, Michelangelo and Katkov, Mikhail and Romani, Sandro and Tsodyks, Misha} } @article {65792, title = {The Statistical Structure of the Hippocampal Code for Space as a Function of Time, Context, and Value.}, journal = {Cell}, volume = {183}, year = {2020}, month = {2020 Oct 29}, pages = {620-35}, abstract = {

Hippocampal activity represents many behaviorally important variables, including context, an animal{\textquoteright}s location within a given environmental context, time, and reward. Using longitudinal calcium imaging in mice, multiple large virtual environments, and differing reward contingencies, we derived a unified probabilistic model of CA1 representations centered on a single feature-the field propensity. Each cell{\textquoteright}s 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. Propensity is broadly distributed-with many low, and some very high, propensity cells-and thus strongly shapes hippocampal representations. This results in a range of spatial codes, from sparse to dense. Propensity varied \~{}10-fold between adjacent cells in salt-and-pepper fashion, indicating substantial functional differences within a presumed cell type. Intracellular recordings linked propensity to cell excitability. The stability of each cell{\textquoteright}s propensity across conditions suggests this fundamental property has anatomical, transcriptional, and/or developmental origins.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2020.09.024}, author = {Lee, Jae Sung and Briguglio, John J and Cohen, Jeremy D and Romani, Sandro and Lee, Albert K} } @article {65246, title = {The computation of directional selectivity in the OFF motion pathway.}, journal = {eLife}, volume = {8}, year = {2019}, month = {2019 Dec 11}, abstract = {

In flies, the direction of moving ON and OFF features is computed separately. T4 (ON) and T5 (OFF) are the first neurons in their respective pathways to extract a directionally selective response from their non-selective inputs. Our recent study of T4 found that the integration of offset depolarizing and hyperpolarizing inputs is critical for the generation of directional selectivity. However, T5s lack small-field inhibitory inputs, suggesting they may use a different mechanism. Here we used whole-cell recordings of T5 neurons and found a similar receptive field structure: fast depolarization and persistent, spatially offset hyperpolarization. By assaying pairwise interactions of local stimulation across the receptive field, we found no amplifying responses, only suppressive responses to the non-preferred motion direction. We then evaluated passive, biophysical models and found that a model using direct inhibition, but not the removal of excitation, can accurately predict T5 responses to a range of moving stimuli.

}, issn = {2050-084X}, doi = {10.7554/eLife.50706}, author = {Gruntman, Eyal and Romani, Sandro and Reiser, Michael B} } @article {64781, title = {Discrete attractor dynamics underlies persistent activity in the frontal cortex.}, journal = {Nature}, volume = {566}, year = {2019}, month = {2019 Feb 06}, pages = {212-7}, abstract = {

Short-term memories link events separated in time, such as past sensation and future actions. Short-term memories are correlated with slow neural dynamics, including selective persistent activity, which can be maintained over seconds. In a delayed response task that requires short-term memory, neurons in the mouse anterior lateral motor cortex (ALM) show persistent activity that instructs future actions. To determine the principles that underlie this persistent activity, here we combined intracellular and extracellular electrophysiology with optogenetic perturbations and network modelling. We show that during the delay epoch, the activity of ALM neurons moved towards discrete end points that correspond to specific movement directions. These end points were robust to transient shifts in ALM activity caused by optogenetic perturbations. Perturbations occasionally switched the population dynamics to the other end point, followed by incorrect actions. Our results show that discrete attractor dynamics underlie short-term memory related to motor planning.

}, issn = {1476-4687}, doi = {10.1038/s41586-019-0919-7}, author = {Inagaki, Hidehiko K and Fontolan, Lorenzo and Romani, Sandro and Svoboda, Karel} } @article {65234, title = {Generation of stable heading representations in diverse visual scenes.}, journal = {Nature}, volume = {576}, year = {2019}, month = {2019 Nov 20}, pages = {126-31}, abstract = {

Many animals rely on an internal heading representation when navigating in varied environments. How this representation is linked to the sensory cues that define different surroundings is unclear. In the fly brain, heading is represented by {\textquoteright}compass{\textquoteright} neurons that innervate a ring-shaped structure known as the ellipsoid body. Each compass neuron receives inputs from {\textquoteright}ring{\textquoteright} neurons that are selective for particular visual features; this combination provides an ideal substrate for the extraction of directional information from a visual scene. Here we combine two-photon calcium imaging and optogenetics in tethered flying flies with circuit modelling, and show how the correlated activity of compass and visual neurons drives plasticity, which flexibly transforms two-dimensional visual cues into a stable heading representation. We also describe how this plasticity enables the fly to convert a partial heading representation, established from orienting within part of a novel setting, into a complete heading representation. Our results provide mechanistic insight into the memory-related computations that are essential for flexible navigation in varied surroundings.

}, issn = {1476-4687}, doi = {10.1038/s41586-019-1767-1}, author = {Kim, Sung Soo and Hermundstad, Ann M and Romani, Sandro and Abbott, L F and Jayaraman, Vivek} } @article {49124, title = {Low-dimensional and monotonic preparatory activity in mouse anterior lateral motor cortex.}, journal = {The Journal of Neuroscience : the official journal of the Society for Neuroscience}, volume = {38}, year = {2018}, month = {2018 Apr 25}, pages = {4163-85}, abstract = {

Neurons in multiple brain regions fire trains of action potentials anticipating specific movements, but this {\textquoteright}preparatory activity{\textquoteright} has not been systematically compared across behavioral tasks. We compared preparatory activity in auditory and tactile delayed-response tasks in male mice. Skilled, directional licking was the motor 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 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 circuit mechanisms underlying preparatory activity in a genetically tractable model organism.Short-term memories link events separated in time. Neurons in frontal cortex fire trains of action potentials anticipating specific movements, often seconds before the movement. This {\textquoteright}preparatory activity{\textquoteright} has been observed in multiple brain regions, but has rarely been compared systematically across behavioral tasks in the same brain region. To identify common features of preparatory activity, we developed and compared preparatory activity in auditory and tactile delayed-response tasks in mice. The same cortical area is necessary for both tasks. Multiple features of preparatory activity, measured with high-density silicon probes, were similar across tasks. We find that preparatory activity is low-dimensional and monotonic. Our study forms the foundation to analyze the circuit mechanisms underlying preparatory activity in a genetically tractable model organism.

}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.3152-17.2018}, author = {Inagaki, Hidehiko K and Inagaki, Miho and Romani, Sandro and Svoboda, Karel} } @article {49276, title = {A novel pyramidal cell type promotes sharp-wave synchronization in the hippocampus.}, journal = {Nature Neuroscience}, volume = {21}, year = {2018}, month = {2018 Jun 18}, pages = {985-95}, abstract = {

To support cognitive function, the CA3 region of the hippocampus performs computations involving attractor dynamics. Understanding how cellular and ensemble activities of CA3 neurons enable computation is critical for elucidating the neural correlates of cognition. Here we show that CA3 comprises not only classically described pyramid cells with thorny excrescences, but also includes previously unidentified {\textquoteright}athorny{\textquoteright} pyramid cells that lack mossy-fiber input. Moreover, the two neuron types have distinct morphological and physiological phenotypes and are differentially modulated by acetylcholine. To understand the contribution of these athorny pyramid neurons to circuit function, we measured cell-type-specific firing patterns during sharp-wave synchronization events in vivo and recapitulated these dynamics with an attractor network model comprising two principal cell types. Our data and simulations reveal a key role for athorny cell bursting in the initiation of sharp waves: transient network attractor states that signify the execution of pattern completion computations vital to cognitive function.

}, issn = {1546-1726}, doi = {10.1038/s41593-018-0172-7}, author = {Hunt, David L and Linaro, Daniele and Si, Bailu and Romani, Sandro and Spruston, Nelson} } @article {48998, title = {Simple integration of fast excitation and offset, delayed inhibition computes directional selectivity in Drosophila.}, journal = {Nature Neuroscience}, volume = {21}, year = {2018}, month = {2018 Jan 08}, pages = {250-7}, abstract = {

A neuron that extracts directionally selective motion information from upstream signals lacking this selectivity must compare visual responses from spatially offset inputs. Distinguishing among prevailing algorithmic models for this computation requires measuring fast neuronal activity and inhibition. In the Drosophila melanogaster visual system, a fourth-order neuron-T4-is the first cell type in the ON pathway to exhibit directionally selective signals. Here we use in vivo whole-cell recordings of T4 to show that directional selectivity originates from simple integration of spatially offset fast excitatory and slow inhibitory inputs, resulting in a suppression of responses to the nonpreferred motion direction. We constructed a passive, conductance-based model of a T4 cell that accurately predicts the neuron{\textquoteright}s response to moving stimuli. These results connect the known circuit anatomy of the motion pathway to the algorithmic mechanism by which the direction of motion is computed.

}, issn = {1546-1726}, doi = {10.1038/s41593-017-0046-4}, author = {Gruntman, Eyal and Romani, Sandro and Reiser, Michael B} } @article {48785, title = {Behavioral time scale synaptic plasticity underlies CA1 place fields.}, journal = {Science (New York, N.Y.)}, volume = {357}, year = {2017}, month = {2017 Sep 08}, pages = {1033-1036}, abstract = {

Learning is primarily mediated by activity-dependent modifications of synaptic strength within neuronal circuits. We discovered that place fields in hippocampal area CA1 are produced by a synaptic potentiation notably different from Hebbian plasticity. Place fields could be produced in vivo in a single trial by potentiation of input that arrived seconds before and after complex spiking. The potentiated synaptic input was not initially coincident with action potentials or depolarization. This rule, named behavioral time scale synaptic plasticity, abruptly modifies inputs that were neither causal nor close in time to postsynaptic activation. In slices, five pairings of subthreshold presynaptic activity and calcium (Ca(2+)) plateau potentials produced a large potentiation with an asymmetric seconds-long time course. This plasticity efficiently stores entire behavioral sequences within synaptic weights to produce predictive place cell activity.

}, issn = {1095-9203}, doi = {10.1126/science.aan3846}, author = {Bittner, Katie C and Milstein, Aaron D and Grienberger, Christine and Romani, Sandro and Magee, Jeffrey C} } @article {48253, title = {Inhibitory suppression of heterogeneously tuned excitation enhances spatial coding in CA1 place cells.}, journal = {Nature Neuroscience}, volume = {20}, year = {2017}, month = {2017 Jan 23}, pages = {417-26}, abstract = {

Place cells in the CA1 region of the hippocampus express location-specific firing despite receiving a steady barrage of heterogeneously tuned excitatory inputs that should compromise output dynamic range and timing. We examined the role of synaptic inhibition in countering the deleterious effects of off-target excitation. Intracellular recordings in behaving mice demonstrate that bimodal excitation drives place cells, while unimodal excitation drives weaker or no spatial tuning in interneurons. Optogenetic hyperpolarization of interneurons had spatially uniform effects on place cell membrane potential dynamics, substantially reducing spatial selectivity. These data and a computational model suggest that spatially uniform inhibitory conductance enhances rate coding in place cells by suppressing out-of-field excitation and by limiting dendritic amplification. Similarly, we observed that inhibitory suppression of phasic noise generated by out-of-field excitation enhances temporal coding by expanding the range of theta phase precession. Thus, spatially uniform inhibition allows proficient and flexible coding in hippocampal CA1 by suppressing heterogeneously tuned excitation.

}, issn = {1546-1726}, doi = {10.1038/nn.4486}, author = {Grienberger, Christine and Milstein, Aaron D and Bittner, Katie C and Romani, Sandro and Magee, Jeffrey C} } @article {48533, title = {Theta-paced flickering between place-cell maps in the hippocampus: A model based on short-term synaptic plasticity.}, journal = {Hippocampus}, volume = {27}, year = {2017}, month = {2017 May 30}, pages = {959-70}, abstract = {

Hippocampal place cells represent different environments with distinct neural activity patterns. Following an abrupt switch between two familiar configurations of visual cues defining two environments, the hippocampal neural activity pattern switches almost immediately to the corresponding representation. Surprisingly, during a transient period following the switch to the new environment, occasional fast transitions of activity patterns between the representations (flickering) were observed (Jezek et al. 2011). Here we show that an attractor neural network model of place cells with connections endowed with short-term synaptic plasticity can account for this phenomenon. A memory trace of the recent history of network activity is maintained in the state of the synapses, allowing the network to temporarily reactivate the representation of the previous environment in the absence of the corresponding sensory cues. The model predicts that the number of flickering events depends on the amplitude of the ongoing theta rhythm and the distance between the current position of the animal and its position at the time of cue switching. We test these predictions with new analysis of experimental data. These results suggest a potential role of short-term synaptic plasticity in recruiting the activity of different cell assemblies and in shaping hippocampal activity of behaving animals. This article is protected by copyright. All rights reserved.

}, issn = {1098-1063}, doi = {10.1002/hipo.22743}, author = {Mark, Shirley and Romani, Sandro and Jezek, Karel and Tsodyks, Misha} } @article {47526, title = {Hippocampal global remapping for different sensory modalities in flying bats.}, journal = {Nature Neuroscience}, volume = {19}, year = {2016}, month = {2016 May 30}, pages = {952-8}, abstract = {

Hippocampal place cells encode the animal{\textquoteright}s spatial position. However, it is unknown how different long-range sensory systems affect spatial representations. Here we alternated usage of vision and echolocation in Egyptian fruit bats while recording from single neurons in hippocampal areas CA1 and subiculum. Bats flew back and forth along a linear flight track, employing echolocation in darkness or vision in light. Hippocampal representations remapped between vision and echolocation via two kinds of remapping: subiculum neurons turned on or off, while CA1 neurons shifted their place fields. Interneurons also exhibited strong remapping. Finally, hippocampal place fields were sharper under vision than echolocation, matching the superior sensory resolution of vision over echolocation. Simulating several theoretical models of place-cells suggested that combining sensory information and path integration best explains the experimental sharpening data. In summary, here we show sensory-based global remapping in a mammal, suggesting that the hippocampus does not contain an abstract spatial map but rather a {\textquoteright}cognitive atlas{\textquoteright}, with multiple maps for different sensory modalities.

}, issn = {1546-1726}, doi = {10.1038/nn.4310}, author = {Geva-Sagiv, Maya and Romani, Sandro and Las, Liora and Ulanovsky, Nachum} } @article {47382, title = {Practice makes perfect in memory recall.}, journal = {Learning \& Memory (Cold Spring Harbor, N.Y.)}, volume = {23}, year = {2016}, month = {2016 Apr}, pages = {169-73}, abstract = {

A large variability in performance is observed when participants recall briefly presented lists of words. The sources of such variability are not known. Our analysis of a large data set of free recall revealed a small fraction of participants that reached an extremely high performance, including many trials with the recall of complete lists. Moreover, some of them developed a number of consistent input-position-dependent recall strategies, in particular recalling words consecutively ("chaining") or in groups of consecutively presented words ("chunking"). The time course of acquisition and particular choice of positional grouping were variable among participants. Our results show that acquiring positional strategies plays a crucial role in improvement of recall performance.

}, issn = {1549-5485}, doi = {10.1101/lm.041178.115}, author = {Romani, Sandro and Katkov, Mikhail and Tsodyks, Misha} } @article {46283, title = {Effects of long-term representations on free recall of unrelated words.}, journal = {Learning \& Memory}, volume = {22}, year = {2015}, month = {2015 Jan 15}, pages = {101-8}, abstract = {

Human memory stores vast amounts of information. Yet recalling this information is often challenging when specific cues are lacking. Here we consider an associative model of retrieval where each recalled item triggers the recall of the next item based on the similarity between their long-term neuronal representations. The model predicts that different items stored in memory have different probability to be recalled depending on the size of their representation. Moreover, items with high recall probability tend to be recalled earlier and suppress other items. We performed an analysis of a large data set on free recall and found a highly specific pattern of statistical dependencies predicted by the model, in particular negative correlations between the number of words recalled and their average recall probability. Taken together, experimental and modeling results presented here reveal complex interactions between memory items during recall that severely constrain recall capacity.

}, issn = {1549-5485}, doi = {10.1101/lm.035238.114}, author = {Katkov, Mikhail and Romani, Sandro and Tsodyks, Misha} } @article {47240, title = {Neural Network Model of Memory Retrieval.}, journal = {Frontiers in Computational Neuroscience}, volume = {9}, year = {2015}, month = {2015 Dec 17}, pages = {149}, abstract = {

Human memory can store large amount of information. Nevertheless, recalling is often a challenging task. In a classical free recall paradigm, where participants are asked to repeat a briefly presented list of words, people make mistakes for lists as short as 5 words. We present a model for memory retrieval based on a Hopfield neural network where transition between items are determined by similarities in their long-term memory representations. Meanfield analysis of the model reveals stable states of the network corresponding (1) to single memory representations and (2) intersection between memory representations. We show that oscillating feedback inhibition in the presence of noise induces transitions between these states triggering the retrieval of different memories. The network dynamics qualitatively predicts the distribution of time intervals required to recall new memory items observed in experiments. It shows that items having larger number of neurons in their representation are statistically easier to recall and reveals possible bottlenecks in our ability of retrieving memories. Overall, we propose a neural network model of information retrieval broadly compatible with experimental observations and is consistent with our recent graphical model (Romani et al., 2013).

}, issn = {1662-5188}, doi = {10.3389/fncom.2015.00149}, author = {Recanatesi, Stefano and Katkov, Mikhail and Romani, Sandro and Tsodyks, Misha} } @article {46285, title = {Short-term plasticity based network model of place cells dynamics.}, journal = {Hippocampus}, volume = {25}, year = {2015}, month = {2015 Jan}, pages = {94-105}, abstract = {

Rodent hippocampus exhibits strikingly different regimes of population activity in different behavioral states. During locomotion, hippocampal activity oscillates at theta frequency (5-12 Hz) and cells fire at specific locations in the environment, the place fields. As the animal runs through a place field, spikes are emitted at progressively earlier phases of the theta cycles. During immobility, hippocampus exhibits sharp irregular bursts of activity, with occasional rapid orderly activation of place cells expressing a possible trajectory of the animal. The mechanisms underlying this rich repertoire of dynamics are still unclear. We developed a novel recurrent network model that accounts for the observed phenomena. We assume that the network stores a map of the environment in its recurrent connections, which are endowed with short-term synaptic depression. We show that the network dynamics exhibits two different regimes that are similar to the experimentally observed population activity states in the hippocampus. The operating regime can be solely controlled by external inputs. Our results suggest that short-term synaptic plasticity is a potential mechanism contributing to shape the population activity in hippocampus.

}, issn = {1098-1063}, doi = {10.1002/hipo.22355}, author = {Romani, Sandro and Tsodyks, Misha} } @article {45792, title = {Theta sequences are essential for internally generated hippocampal firing fields.}, journal = {Nature Neuroscience}, volume = {18}, year = {2015}, month = {2015 Feb}, pages = {282-8}, abstract = {

Sensory cue inputs and memory-related internal brain activities govern the firing of hippocampal neurons, but which specific firing patterns are induced by either of the two processes remains unclear. We found that sensory cues guided the firing of neurons in rats on a timescale of seconds and supported the formation of spatial firing fields. Independently of the sensory inputs, the memory-related network activity coordinated the firing of neurons not only on a second-long timescale, but also on a millisecond-long timescale, and was dependent on medial septum inputs. We propose a network mechanism that might coordinate this internally generated firing. Overall, we suggest that two independent mechanisms support the formation of spatial firing fields in hippocampus, but only the internally organized system supports short-timescale sequential firing and episodic memory.

}, issn = {1546-1726}, doi = {10.1038/nn.3904}, author = {Wang, Yingxue and Romani, Sandro and Lustig, Brian and Leonardo, Anthony and Pastalkova, Eva} } @article {46286, title = {Continuous attractor network model for conjunctive position-by-velocity tuning of grid cells.}, journal = {PLoS Computational Biology}, volume = {10}, year = {2014}, month = {2014 Apr 17}, pages = {e1003558}, abstract = {

The spatial responses of many of the cells recorded in layer II of rodent medial entorhinal cortex (MEC) show a triangular grid pattern, which appears to provide an accurate population code for animal spatial position. In layer III, V and VI of the rat MEC, grid cells are also selective to head-direction and are modulated by the speed of the animal. Several putative mechanisms of grid-like maps were proposed, including attractor network dynamics, interactions with theta oscillations or single-unit mechanisms such as firing rate adaptation. In this paper, we present a new attractor network model that accounts for the conjunctive position-by-velocity selectivity of grid cells. Our network model is able to perform robust path integration even when the recurrent connections are subject to random perturbations.

}, keywords = {Animals, Models, Theoretical, Movement, Rodentia}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1003558}, author = {Si, Bailu and Romani, Sandro and Tsodyks, Misha} } @article {46284, title = {Word length effect in free recall of randomly assembled word lists.}, journal = {Frontiers in Computational Neuroscience}, volume = {8}, year = {2014}, month = {2014 Oct 14}, pages = {129}, abstract = {

In serial recall experiments, human subjects are requested to retrieve a list of words in the same order as they were presented. In a classical study, participants were reported to recall more words from study lists composed of short words compared to lists of long words, the word length effect. The world length effect was also observed in free recall experiments, where subjects can retrieve the words in any order. Here we analyzed a large dataset from free recall experiments of unrelated words, where short and long words were randomly mixed, and found a seemingly opposite effect: long words are recalled better than the short ones. We show that our recently proposed mechanism of associative retrieval can explain both these observations. Moreover, the direction of the effect depends solely on the way study lists are composed.

}, issn = {1662-5188}, doi = {10.3389/fncom.2014.00129}, author = {Katkov, Mikhail and Romani, Sandro and Tsodyks, Misha} } @article {46299, title = {Scaling laws of associative memory retrieval.}, journal = {Neural Computation}, volume = {25}, year = {2013}, month = {2013 Oct}, pages = {2523-44}, abstract = {

Most people have great difficulty in recalling unrelated items. For example, in free recall experiments, lists of more than a few randomly selected words cannot be accurately repeated. Here we introduce a phenomenological model of memory retrieval inspired by theories of neuronal population coding of information. The model predicts nontrivial scaling behaviors for the mean and standard deviation of the number of recalled words for lists of increasing length. Our results suggest that associative information retrieval is a dominating factor that limits the number of recalled items.

}, keywords = {Algorithms, Association Learning, Computer Simulation, Humans, Memory, Mental Recall, Models, Neurological, Neural Networks (Computer)}, issn = {1530-888X}, doi = {10.1162/NECO_a_00499}, author = {Romani, Sandro and Pinkoviezky, Itai and Rubin, Alon and Tsodyks, Misha} } @article {46300, title = {Intracellular dynamics of virtual place cells.}, journal = {Neural Computation}, volume = {23}, year = {2011}, month = {2011 Mar}, pages = {651-5}, abstract = {

The pattern of spikes recorded from place cells in the rodent hippocampus is strongly modulated by both the spatial location in the environment and the theta rhythm. The phases of the spikes in the theta cycle advance during movement through the place field. Recently intracellular recordings from hippocampal neurons (Harvey, Collman, Dombeck, \& Tank, 2009 ) showed an increase in the amplitude of membrane potential oscillations inside the place field, which was interpreted as evidence that an intracellular mechanism caused phase precession. Here we show that an existing network model of the hippocampus (Tsodyks, Skaggs, Sejnowski, \& McNaughton, 1996 ) can equally reproduce this and other aspects of the intracellular recordings, which suggests that new experiments are needed to distinguish the contributions of intracellular and network mechanisms to phase precession.

}, issn = {1530-888X}, doi = {10.1162/NECO_a_00087}, author = {Romani, Sandro and Sejnowski, Terrence J and Tsodyks, Misha} } @article {46301, title = {Continuous attractors with morphed/correlated maps.}, journal = {PLoS Computational Biology}, volume = {6}, year = {2010}, month = {2010 Aug 5}, pages = {e1000869}, abstract = {

Continuous attractor networks are used to model the storage and representation of analog quantities, such as position of a visual stimulus. The storage of multiple continuous attractors in the same network has previously been studied in the context of self-position coding. Several uncorrelated maps of environments are stored in the synaptic connections, and a position in a given environment is represented by a localized pattern of neural activity in the corresponding map, driven by a spatially tuned input. Here we analyze networks storing a pair of correlated maps, or a morph sequence between two uncorrelated maps. We find a novel state in which the network activity is simultaneously localized in both maps. In this state, a fixed cue presented to the network does not determine uniquely the location of the bump, i.e. the response is unreliable, with neurons not always responding when their preferred input is present. When the tuned input varies smoothly in time, the neuronal responses become reliable and selective for the environment: the subset of neurons responsive to a moving input in one map changes almost completely in the other map. This form of remapping is a non-trivial transformation between the tuned input to the network and the resulting tuning curves of the neurons. The new state of the network could be related to the formation of direction selectivity in one-dimensional environments and hippocampal remapping. The applicability of the model is not confined to self-position representations; we show an instance of the network solving a simple delayed discrimination task.

}, keywords = {Animals, Computer Simulation, Discrimination (Psychology), Hippocampus, Models, Biological, Neural Networks (Computer), Numerical Analysis, Computer-Assisted}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1000869}, author = {Romani, Sandro and Tsodyks, Misha} } @article {46302, title = {Optimizing one-shot learning with binary synapses.}, journal = {Neural Computation}, volume = {20}, year = {2008}, month = {2008 Aug}, pages = {1928-50}, abstract = {

A network of excitatory synapses trained with a conservative version of Hebbian learning is used as a model for recognizing the familiarity of thousands of once-seen stimuli from those never seen before. Such networks were initially proposed for modeling memory retrieval (selective delay activity). We show that the same framework allows the incorporation of both familiarity recognition and memory retrieval, and estimate the network{\textquoteright}s capacity. In the case of binary neurons, we extend the analysis of Amit and Fusi (1994) to obtain capacity limits based on computations of signal-to-noise ratio of the field difference between selective and non-selective neurons of learned signals. We show that with fast learning (potentiation probability approximately 1), the most recently learned patterns can be retrieved in working memory (selective delay activity). A much higher number of once-seen learned patterns elicit a realistic familiarity signal in the presence of an external field. With potentiation probability much less than 1 (slow learning), memory retrieval disappears, whereas familiarity recognition capacity is maintained at a similarly high level. This analysis is corroborated in simulations. For analog neurons, where such analysis is more difficult, we simplify the capacity analysis by studying the excess number of potentiated synapses above the steady-state distribution. In this framework, we derive the optimal constraint between potentiation and depression probabilities that maximizes the capacity.

}, keywords = {Algorithms, Brain, Computer Simulation, Learning, Models, Statistical, Neural Networks (Computer), Neurons, Pattern Recognition, Automated, Synapses, Synaptic Transmission}, issn = {0899-7667}, doi = {10.1162/neco.2008.10-07-618}, author = {Romani, Sandro and Amit, Daniel J and Amit, Yali} } @article {46303, title = {Universal memory mechanism for familiarity recognition and identification.}, journal = {The Journal of Neuroscience : The Official Journal of the Society for Neuroscience}, volume = {28}, year = {2008}, month = {2008 Jan 2}, pages = {239-48}, abstract = {

Macaque monkeys were tested on a delayed-match-to-multiple-sample task, with either a limited set of well trained images (in randomized sequence) or with never-before-seen images. They performed much better with novel images. False positives were mostly limited to catch-trial image repetitions from the preceding trial. This result implies extremely effective one-shot learning, resembling Standing{\textquoteright}s finding that people detect familiarity for 10,000 once-seen pictures (with 80\% accuracy) (Standing, 1973). Familiarity memory may differ essentially from identification, which embeds and generates contextual information. When encountering another person, we can say immediately whether his or her face is familiar. However, it may be difficult for us to identify the same person. To accompany the psychophysical findings, we present a generic neural network model reproducing these behaviors, based on the same conservative Hebbian synaptic plasticity that generates delay activity identification memory. Familiarity becomes the first step toward establishing identification. Adding an inter-trial reset mechanism limits false positives for previous-trial images. The model, unlike previous proposals, relates repetition-recognition with enhanced neural activity, as recently observed experimentally in 92\% of differential cells in prefrontal cortex, an area directly involved in familiarity recognition. There may be an essential functional difference between enhanced responses to novel versus to familiar images: The maximal signal from temporal cortex is for novel stimuli, facilitating additional sensory processing of newly acquired stimuli. The maximal signal for familiar stimuli arising in prefrontal cortex facilitates the formation of selective delay activity, as well as additional consolidation of the memory of the image in an upstream cortical module.

}, keywords = {Animals, Behavior, Animal, Computer Simulation, Discrimination (Psychology), Haplorhini, Identification (Psychology), Models, Neurological, Neural Networks (Computer), Photic Stimulation, Recognition (Psychology), ROC Curve, Task Performance and Analysis}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.4799-07.2008}, author = {Yakovlev, Volodya and Amit, Daniel J and Romani, Sandro and Hochstein, Shaul} } @article {46304, title = {Search for fMRI BOLD signals in networks of spiking neurons.}, journal = {European Journal of Neuroscience}, volume = {25}, year = {2007}, month = {2007 Mar}, pages = {1882-92}, abstract = {

In a recent experiment, functional magnetic resonance imaging blood oxygen level-dependent (fMRI BOLD) signals were compared in different cortical areas (primary-visual and associative), when subjects were required covertly to name images in two protocols: sequences of images, with and without intervening delays. The amplitude of the BOLD signal in protocols with delay was found to be closer to that without delays in associative areas than in primary areas. The present study provides an exploratory proposal for the identification of the neural activity substrate of the BOLD signal in quasi-realistic networks of spiking neurons, in networks sustaining selective delay activity (associative) and in networks responsive to stimuli, but whose unique stationary state is one of spontaneous activity (primary). A variety of observables are {\textquoteright}recorded{\textquoteright} in the network simulations, applying the experimental stimulation protocol. The ratios of the candidate BOLD signals, in the two protocols, are compared in networks with and without delay activity. There are several options for recovering the experimental result in the model networks. One common conclusion is that the distinguishing factor is the presence of delay activity. The effect of NMDAr is marginal. The ultimate quantitative agreement with the experiment results depends on a distinction of the baseline signal level from its value in delay-period spontaneous activity. This may be attributable to the subjects{\textquoteright} attention. Modifying the baseline results in a quantitative agreement for the ratios, and provided a definite choice of the candidate signals. The proposed framework produces predictions for the BOLD signal in fMRI experiments, upon modification of the protocol presentation rate and the form of the response function.

}, keywords = {Action Potentials, Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Nerve Net, Neurons, Oxygen, Synapses}, issn = {0953-816X}, doi = {10.1111/j.1460-9568.2007.05408.x}, author = {Amit, Daniel J and Romani, Sandro} } @article {46305, title = {Mean-field analysis of selective persistent activity in presence of short-term synaptic depression.}, journal = {Journal of Computational Neuroscience}, volume = {20}, year = {2006}, month = {2006 Apr}, pages = {201-17}, abstract = {

Mean-Field theory is extended to recurrent networks of spiking neurons endowed with short-term depression (STD) of synaptic transmission. The extension involves the use of the distribution of interspike intervals of an integrate-and-fire neuron receiving a Gaussian current, with a given mean and variance, in input. This, in turn, is used to obtain an accurate estimate of the resulting postsynaptic current in presence of STD. The stationary states of the network are obtained requiring self-consistency for the currents-those driving the emission processes and those generated by the emitted spikes. The model network stores in the distribution of two-state efficacies of excitatory-to-excitatory synapses, a randomly composed set of external stimuli. The resulting synaptic structure allows the network to exhibit selective persistent activity for each stimulus in the set. Theory predicts the onset of selective persistent, or working memory (WM) activity upon varying the constitutive parameters (e.g. potentiated/depressed long-term efficacy ratio, parameters associated with STD), and provides the average emission rates in the various steady states. Theoretical estimates are in remarkably good agreement with data "recorded" in computer simulations of the microscopic model.

}, keywords = {Action Potentials, Algorithms, Animals, Cerebral Cortex, Excitatory Postsynaptic Potentials, Humans, Memory, Short-Term, Nerve Net, Neural Inhibition, Neural Networks (Computer), Neural Pathways, Neurons, Normal Distribution, Reaction Time, Synaptic Transmission, Time Factors}, issn = {0929-5313}, doi = {10.1007/s10827-006-6308-x}, author = {Romani, Sandro and Amit, Daniel J and Mongillo, Gianluigi} } @article {46306, title = {Learning in realistic networks of spiking neurons and spike-driven plastic synapses.}, journal = {European Journal of Neuroscience}, volume = {21}, year = {2005}, month = {2005 Jun}, pages = {3143-60}, abstract = {

We have used simulations to study the learning dynamics of an autonomous, biologically realistic recurrent network of spiking neurons connected via plastic synapses, subjected to a stream of stimulus-delay trials, in which one of a set of stimuli is presented followed by a delay. Long-term plasticity, produced by the neural activity experienced during training, structures the network and endows it with active (working) memory, i.e. enhanced, selective delay activity for every stimulus in the training set. Short-term plasticity produces transient synaptic depression. Each stimulus used in training excites a selective subset of neurons in the network, and stimuli can share neurons (overlapping stimuli). Long-term plasticity dynamics are driven by presynaptic spikes and coincident postsynaptic depolarization; stability is ensured by a refresh mechanism. In the absence of stimulation, the acquired synaptic structure persists for a very long time. The dependence of long-term plasticity dynamics on the characteristics of the stimulus response (average emission rates, time course and synchronization), and on the single-cell emission statistics (coefficient of variation) is studied. The study clarifies the specific roles of short-term synaptic depression, NMDA receptors, stimulus representation overlaps, selective stimulation of inhibition, and spike asynchrony during stimulation. Patterns of network spiking activity before, during and after training reproduce most of the in vivo physiological observations in the literature.

}, keywords = {Action Potentials, Animals, Central Nervous System, Electric Stimulation, Humans, Memory, Short-Term, Nerve Net, Neural Inhibition, Neural Networks (Computer), Neural Pathways, Neuronal Plasticity, Neurons, Receptors, N-Methyl-D-Aspartate, Synapses, Synaptic Transmission}, issn = {0953-816X}, doi = {10.1111/j.1460-9568.2005.04087.x}, author = {Mongillo, Gianluigi and Curti, Emanuele and Romani, Sandro and Amit, Daniel J} }