Frontiers in Computational Neuroscience

Wave recurrent neural networks (wRNNs) are biologically inspired recurrent architectures that use traveling-wave dynamics to support sequence learning and memory. However, their input-to-hidden pathway remains relatively simple compared with biological neurons, where dendrites perform nonlinear input integration. In this study, we introduce the Dendritic Wave Recurrent Neural Network (DWRNN), which augments the input pathway of the wRNN with nonlinear basal dendritic branches while preserving the original recurrent wave dynamics. We evaluate DW-RNN on a simple copy task, sequential MNIST (sMNIST), permuted sequential MNIST (psMNIST), and noisy sequential CIFAR-10 (nsCIFAR-10). On the copy task, DW-RNN shows learning behavior comparable to the standard wRNN, suggesting that dendritic input integration does not disrupt the recurrent wave-based memory mechanism. On the three sequential image-classification benchmarks, DW-RNN outperforms the standard wRNN, improving accuracy from 97.27 {+/-} 0.15% to 97.82 {+/-} 0.12% on sMNIST, from 96.74 {+/-} 0.17% to 96.92 {+/-} 0.10% on psMNIST, and from 54.30 {+/-} 0.79% to 55.65 {+/-} 0.55% on nsCIFAR-10. In addition to improving mean accuracy, DW-RNN exhibits lower across-seed variability on all three classification benchmarks, suggesting that dendritic input integration may improve the stability of wRNN training. Hidden-activity visualizations further show that DW-RNN preserves the characteristic traveling-wave patterns of the original wRNN. These results suggest that dendritic computation and traveling-wave recurrent dynamics provide complementary mechanisms for biologically inspired sequence learning.

Background / Introduction: Biologically inspired spiking neural networks can model adaptive behavior, but learning multiple goals is difficult because synaptic updates for different targets can interfere. We tested whether multi-timescale plasticity and context-specific credit assignment could improve continual multi-goal learning in a spiking navigation system inspired by entorhinal-hippocampal circuitry. Methods: We developed a closed-loop spiking model containing grid-like, place-like, target-related, association, and motor-output populations. An agent navigated in a two-dimensional environment with randomized starting locations and learned through reward-modulated spike-timing dependent plasticity (STDP/RL) and a novel evidence-gated plasticity (EGP) framework. EGP accumulates candidate synaptic modifications, evaluates them using reward evidence, and consolidates only changes that improve performance. A target-context variant maintained separate proposal stores and reward evaluation for each target. Results: STDP/RL learned and retained a single-target navigation policy, but multi-target training produced substantial interference, including attraction to incorrect targets after learning. Across 10 connectivity seeds, target-context EGP achieved higher late-stage reward than global EGP, improved weakest-target performance, and increased the fraction of targets achieving positive reward. In a longer continual-learning simulation, reward increased for all targets, TEST-phase performance increasingly exceeded TRAIN-phase performance, and proposal magnitudes grew over learning. Dwell-time confusion analyses showed that target-context EGP reduced wrong-target attraction and improved target selectivity relative to multi-target STDP/RL. Conclusions: These results demonstrate that spiking navigation circuits can learn goal-directed behavior using local plasticity, but robust multi-goal learning benefits from context-specific evidence-based consolidation. Target-context EGP provides a biologically motivated mechanism for reducing interference during continual reinforcement learning in spiking neural networks.

Despite extensive study of cellular mechanisms underlying long-term potentiation, no single specific protein or gene has been identified which encodes an individual unit of information, or memory bit. Indeed, the brain engram remains a knowledge gap. The theory of exclusion led us to cancel one-by-one several unrealistic biological options, suggesting that the explanation resides somewhere else. Superposition of up to concentric 300 myelin layers, spiraled, and highly compacted wrapping a single axon and each wrap could host hundreds to thousands of niches, as memory cells, collectively consisting of a massive array of cells. The disjointed 3D spatial superposition allows storage of charges, nodes not facing from a layer to next. The thickness of a single myelin layer ranges from 7.0 to 20 nm. The dimension scale is approximately the exact dimensions of the charge trap, the tunnel and dielectric also equipping current AI microchips. Stored charges are positive ions, with similar effect whether charges are negative or positive charges creating an electromagnetic field. To write data, following an action potential, this voltage applies to the control gates of the myelin layers producing an ionic charge injection. This causes charges to gain energy and tunnel through the myelin layer across Ranvier nodes, via quantum tunneling, and deep into the concentric myelin multilayers. This is creating an insulated trapping of K+ ions isolated from the system. In a long white matter tract bundle, the near-perfect isolation of millions of axons within compressed myelin wrap-ion channel K+/Na+ systems provides quantum coherence and precision of asynchronous firing property. The injected ionic charges (K+) become physically stuck in traps within the myelin layers. The K+ ions may not move freely, completely trapped after AP ceases. Mirroring a single-bit, single-level-cell, a trapped ionic charge (ions K+) may represent a 1, while an empty cell (absence of K+) represents a 0. The trial-and-error process, with a Bayesian inference which may have also been the core evolution of the learning human brain. Based on selected mathematical equations, we analyzed the general scheme on how deep learning may be embedded in the brain

AO_SCPLOWBSTRACTC_SCPLOWThere is a gap between neuroscientific theories of learning and the speed of learning observed in many experiments. Since the Cognitive Revolution of the 1950s, compositionality has played a central role in efforts to bridge this gap. Roughly, a compositional system is one where distinct modules are combined according to a set of rules in order to accomplish complex tasks. Recently, significant progress has been made in understanding the emergence of modules in both biological and artificial neural systems. How, and under what conditions, the rules of module recombination are represented in these systems remains an open question. Here we present a neural model that can leverage these rules to dramatically speed up learning. We first show that when faced with multiple tasks which share subcomponents, models learn a low-dimensional representation that captures how subcomponents are reused across the task set. These low-dimensional spaces encode the structure that governs how modules should be recombined. Restricting learning to these subspaces greatly reduces the amount of experience needed to acquire a novel task, even when learning from reinforcement on single trials. In some cases, we can leverage the geometric regularities of these representations to reduce learning to a form of hypothesis testing over a small set of discrete points. Finally, we use this theory to model both behavioral and neural data from non-human primates performing a compositional task, and show that key features in this data are consistent with a model in which exploration during learning is restricted to these low-dimensional spaces. Overall, this work shows that the advantages of modularity in neural systems can be greatly improved upon when models represent the structure of module reuse. Both these features working in tandem lead to learning on timescales similar to biological intelligences, and hence provide a model for how such fast, adaptable behavior can emerge from systems of neurons.

The ability to extract and exploit temporal structure across diverse tasks is central to human cognition. Neuroscientists have typically relied on recurrent neural networks (RNNs) trained with backpropagation through time (BPTT) when modelling neural and behavioural processes such as decision-making and motor control. However, this algorithm has limited biological plausibility, hence the computational principles underlying efficient learning of temporal dependencies remain unresolved. Here, we investigate temporal predictive coding (tPC), a recently proposed framework that extends predictive coding to the temporal domain while preserving local Hebbian update rules. We analyse and extend tPC to establish its relationship with several influential computational models of learning in RNNs, including BPTT, reservoir computing, and eligibility propagation (e-prop). We first demonstrate a functional equivalence between tPC and tBPTT1, a variant of BPTT in which gradients are propagated only one time step into the past. We then show that tPC can leverage reservoir dynamics to encode short-range temporal context, and simultaneously sculpt neural trajectories in state space to support downstream readout. We further demonstrate that hierarchical recurrent dynamics can facilitate learning of more complex temporal dependencies, while additionally conferring robustness to strong distractors. Finally, we show that tPC networks can be augmented with biologically inspired eligibility traces to solve temporally extended context-dependent tasks. Together, these results reveal that relatively simple recurrent networks governed by local plasticity can support temporal learning in more complex settings than previously appreciated.

Deep brain stimulation has demonstrated its therapeutic potential in modulating pathological oscillations associated with Parkinsons disease and epilepsy. However, its efficacy in treating disrupted theta-gamma phase-amplitude coupling seen in memory-related disorders, such as Alzheimers disease, remains poorly understood. While recent studies have targeted the entorhinal-hippocampal circuit, results remain inconsistent. This discrepancy stems from a lack of mechanistic understanding regarding how stimulation protocols affect this circuit. In this work, we present a reduced multicompartment model of the hippocampal CA1 area that reproduces theta-nested gamma oscillations characteristic of healthy neural activity during memory performance. The model comprises pyramidal, basket and OLM cells with simplified morphologies. We also incorporated CA3-to-CA1 axonal projections, providing a foundational framework for studying how stimulation-induced recruitment of afferent pathways modulates CA1 dynamics. By balancing computational efficiency with anatomical accuracy, our model enables systematic investigation of the effects of electrode placement and orientation, as well as stimulation amplitude and frequency on CA1 neural activity. We demonstrate that the excitatory response in CA1 is primarily driven by the recruitment of Schaffer collateral projections. Overall, this work provides a computationally efficient template for exploring diverse stimulation configurations and could be expanded for developing neuromodulatory strategies to restore physiological network dynamics. Author summaryDeep brain stimulation has shown success in treating Parkinsons disease by suppressing abnormal neural activity responsible for movement disorders. However, when applied to memory-related pathologies, such as Alzheimers disease, the therapeutic outcomes remain unpredictable, ranging from cognitive improvement to impairment. This discrepancy highlights a critical gap in our understanding of how stimulation protocols interact with neural dynamics of the targeted circuits. To address this, we developed a computationally efficient model of the hippocampus, which is involved in memory processes, in order to understand how deep brain stimulation might influence its activity. Our model maintains enough biological accuracy to capture essential memory-related neural activity while remaining lightweight enough for rapid execution and systematic exploration of different protocols. This computational efficiency allowed us to conduct systematic investigations of several stimulation configurations to study their effects on hippocampal dynamics. Overall, this model could provide a useful and computationally cost-efficient tool for exploring the mechanisms of deep brain stimulation and help optimize stimulation protocols aimed at alleviating memory disorders.

Cervical cancer represents a pressing global health challenge, emphasizing the critical need for accurate and timely diagnostic methods to facilitate effective treatment and improve survival rates. In response to this challenge, the study presents CerViX-Net, an innovative classification framework designed to advance cervical cancer detection through enhanced computational efficiency and diagnostic accuracy. The development of CerViX-Net is motivated by the limitations of traditional diagnostic models, particularly in handling the computational and memory demands of large-scale data, while ensuring precise feature extraction and classification. CerViX-Net employs a hybrid deep learning architecture that combines the capabilities of ResNet50, EfficientNet-B0, and a Modified Vision Transformer (ViT) module. The ResNet50 branch extracts hierarchical features through stacked convolutional and identity blocks. In another path, the modified ViT module transforms image patches via linear projection, augments them with positional and class embeddings, and processes them using Parallel Transformer Encoder layers to model contextual relationships. Concurrently, EfficientNet-B0 utilizes MBConv blocks to extract multi-scale representations. The feature outputs from all three branches are integrated and passed through a classification head consisting of dropout layers and dense layers to ensure robust and accurate predictions. The proposed framework is rigorously evaluated on the Mendeley LBC dataset, achieving exceptional performance metrics with an accuracy of 99.69%, precision of 99.28%, recall of 99.48%, and an F1-score of 99.52%. The robustness of CerViX-Net is further validated on the SIPaKMeD and Herlev Pap Smear datasets, where it demonstrates comparable excellence, underscoring its efficacy and adaptability across diverse cytology datasets. Statistical validation using Friedman's test further reinforces its superiority over competing methods.

Ampullary electroreceptors are widespread across aquatic vertebrates. The purpose of sensing exogeneous electric fields is conserved across species but the implementations differ and the encoding mechanisms remain incompletely understood. We compared baseline and stimulus-driven response properties of ampullary electroreceptor afferents in the weakly electric fish Apteronotus leptorhynchus and Eigenmannia virescens. We find that their activity is well captured by an extended leaky integrate-and-fire model that generalizes across both species. The model shares similarities to a previous model of the tuberous electroreceptor afferents but further incorporates a low-pass pre-filtering and additional noise sources to reproduce the observed spectral response characteristics. The low-pass is essential to shape stimulus encoding in the high-frequency range. Accurate prediction of low-frequency stimulus encoding further requires two distinct noise sources: stimulus-independent white current noise and activity-dependent noise in the adaptation current, which is shaped by the adaptation time constant to yield effective pink noise dynamics. Using simulation-based inference, we trained a neural network to map model parameters to neuronal response features. This approach enables the generation of heterogeneous, biologically plausible model populations that may serve as a realistic input layer for studying neuronal processing on the next level. With this, we provide a unified and mechanistic model of ampullary electroreceptor encoding in these species and possibly beyond.

Background: Artificial intelligence (AI), including generative and foundation-based methods, has rapidly expanded within medical imaging research. However, the structure, citation impact, collaboration patterns, and thematic orientation of national research ecosystems remain incompletely characterised. Objectives: To evaluate global research trends in AI applied to medical imaging between 2017 and 2025, with detailed analysis of United Kingdom (UK)-affiliated output, citation performance, collaboration structure, funding landscape, and thematic evolution, with emphasis on generative and foundation-based methodologies. Materials and Methods: A bibliometric analysis of Scopus-indexed publications (2017-2025) was performed using a predefined search strategy targeting AI and medical imaging concepts, with emphasis on generative and foundation-based terms. Records were analysed globally and filtered for UK affiliation. Descriptive indicators including total publications (TP), total citations (TC), citations per paper (CPP), and year-on-year growth were calculated. Co-authorship and keyword co-occurrence networks were generated using VOSviewer (v1.6.19). Results: A total of 13,452 publications were identified globally (194,650 citations; global CPP 14.47), of which 889 (6.61%) were UK-affiliated. The UK ranked fourth by publication volume yet demonstrated higher citation efficiency (CPP 21.00) than several higher-volume countries. UK output increased approximately 18-fold between 2017 and 2025, with evidence of a citation-lag effect in recent years. Research activity was concentrated within a small number of institutions accounting for nearly half of national output, although citation impact varied independently of volume. Journal-dominant dissemination was associated with higher average citation impact compared with conference-centric models. Keyword analysis identified three principal thematic clusters: generative/deep learning methodologies, MRI- and diffusion-focused applications, and broader diagnostic imaging workflows. Highly cited publications were initially dominated by generative adversarial network-based reconstruction and synthesis, with recent rapid citation growth observed in diffusion and foundation-model architectures. Conclusion: UK-affiliated research represents a rapidly expanding and highly cited component of the global AI medical imaging literature, with increasing emphasis on generative, diffusion-based, and foundation-model approaches. These findings provide a reproducible bibliometric baseline for monitoring research activity, collaboration patterns, and potential translational priorities, while recognising that citation-based indicators do not directly measure clinical implementation, methodological quality, or real-world impact.

Precise early diagnosis and progression prediction of Alzheimer's Disease (AD) are critical for optimizing clinical intervention. However, current methodologies often suffer from the passive utilization of clinical priors and rigid modal fusion strategies, failing to capture the heterogeneous variations of imaging biomarkers. Furthermore, predicting the precise time-to-conversion from Mild Cognitive Impairment (MCI) to AD remains a formidable challenge. To address these limitations, we propose COMPASS, a clinical-guided multi-modal framework that unifies diagnosis with a comprehensive survival strategy. Specifically, we instigate a paradigm shift to "clinical-prior-driven" learning by incorporating Clinical-Guided Spatial Attention (CGSA), which actively transforms clinical states into visual signals to modulate neural focus on pathological regions. To bridge the semantic gap between modalities, we introduce Reciprocal Semantic Interaction (RSI) via cross-attention, while a Disease-Stage-Aware Modal Fusion (DSAMF) module dynamically adjusts modal weights based on inferred disease severity to mimic clinical reasoning. Moreover, we specifically design a Dual-Head Joint Survival Risk and Time Prediction Network (DH-Net) to jointly perform quantitative conversion time prediction and patient risk stratification. Extensive experiments demonstrate that COMPASS outperforms state-of-the-art methods, achieving 83.19% accuracy in pMCI vs. sMCI classification, an MAE of 7.96 months for conversion time prediction, and a C-index of 0.819. Furthermore, we conducted in-depth neurobiological interpretability analyses, revealing right hippocampal dominance and synergistic regional impairment patterns, thereby providing new biological insights for early AD diagnosis and subtype identification.

Deep brain stimulation (DBS) has been demonstrated to be a successful therapeutic intervention for neurological disorders, yet the mechanisms underlying its effects on neuronal circuits remain incompletely understood. In this study, we propose a comprehensive phenomenological computational model that accounts for the impact of electrical stimulation parameters on neuronal circuits while incorporating experimentally-validated synaptic and cellular constraints. We investigate how DBS pulses modulate spiking activity in populations of homogeneous neurons representing stimulated nuclei, systematically examining the influence of circuitry architecture, including synaptic connectivity strength (weak vs. strong) and organization (sparse vs. rich). To characterize how DBS-modulated neuronal activity propagates through downstream networks, we develop a simple encoder that reveals distinct encoding patterns arising from different architectural configurations of stimulated nuclei. Furthermore, by connecting stimulated nuclei to recurrently connected neuronal populations, we examine the propagation of DBS-modulated neuronal synchrony across various circuit motifs. Our results demonstrate that three critical factors shape DBS-modulated neuronal activity: (a) the intrinsic synaptic and cellular properties of stimulated nuclei, (b) the architectural organization of stimulated nuclei in terms of synaptic strength and connectivity density, and (c) the circuit motifs formed by postsynaptic targets of stimulated nuclei. This unified model provides a mechanistic framework for understanding DBS representation and propagation in neuronal networks, offering insights that may inform optimization of stimulation parameters for clinical applications.

Every brain must adapt to an unpredictable world, yet individuals differ in how readily they learn. Theoretical work suggests that learning is fastest when a system, whether biological or synthetic, is initialized in a state close to instability - i.e., near criticality - because critical dynamics are imbued with a diverse repertoire of patterns and multi-scale correlations. Here, we empirically estimate distance to criticality in the brain and show that it predicts the rate of adaptability underlying learning, neuronal tuning, and general intelligence. In mouse motor cortex, proximity to criticality forecasts learning rate of two future complex tasks: prey capture hunt and ladder crossing. In contrast, distance to criticality predicted neither an animal's naive ability nor its asymptotic skill - isolating the rate of learning itself. In visual cortex of young ferrets, proximity to criticality predicts how strongly experience reshapes neural tuning. In human frontal cortex, it correlates with general cognitive ability. A minimal recurrent network model reproduced these results and offers a mechanism: proximity to criticality defines the timescale over which a system can learn from its past experiences, directly setting the rate of learning. A single dynamical property can account for the capacity to learn, from artificial networks to the mammalian brain.

Although neural activity often evolves along low-dimensional manifolds, such descriptions do not explain the dynamical mechanisms that generate, constrain, and stabilize computation. Identifying these mechanisms is essential for predicting responses to perturbations, understanding generalization to untrained signals, and explaining how similar computations arise from distinct circuit implementations. Here we use recurrent neural networks trained on an interval timing task as a model system to uncover the dynamical mechanisms of neural computation. We show that, despite converging to highly diverse attractor architectures, trained networks share a conserved transient dynamics. During learning, networks self-organize near dynamical bifurcations, forming structured ghost sets of slow points characterized by graded spectra of near-zero eigenvalues. These slow sets form a dynamical scaffold that constrains trajectory evolution. Inputs transiently reconfigure the vector field and reposition activity within this scaffold, while the underlying slow set governs subsequent dynamics. As a result, temporal computation is implemented through structured transient evolution rather than convergence to fixed points or persistent activity states. The extent of the slow sets predicts generalization to unseen temporal intervals, and networks lacking such organization fail to extrapolate reliably. To test sufficiency, we construct a minimal dynamical system endowed with analogous slow set geometry that reproduces interval timing without learning, providing a benchmark for identifying the essential dynamical ingredients of temporal computation. Together, these results identify structured slow transients as a candidate dynamical mechanism for temporal computation, provide a mechanistic interpretation of slow low-dimensional manifolds as emergent consequences of underlying state-space structure, and suggest that computational capacity in near-critical systems arises from the organization of transient flow rather than attractor states alone.

The hippocampus is characterized by a stereotypical macroscopic structure, where the nuclei are densely and heterogeneously packed among different subregions of the hippocampus. Despite the fact that tissue-specific cellular organization has been implicated in neural function, it has been technically challenging to quantitatively analyze mesoscopic cellular organization in the hippocampus due to its high cellular density. To overcome this technical hurdle, we developed Computational Biophysical Histomorphometry Software (CBHS), an automated image-analysis pipeline, aimed at quantifying nuclear shape and the order of the cellular ensemble in high-density areas. When applied to the subfields of hippocampus, we found that denser regions, most notably the dentate gyrus, were the most positionally, but least orientationally ordered. Nuclear shape exhibited a dependence on the local environment in a packing-dependent manner. This association was cell-type specific, with neurons, but not astrocytes displaying nuclear shape that varied with neighbour proximity, although astrocytes demonstrated greater intrinsic shape variance. The results reveal the presence of reproducible mesoscale cell packing order in hippocampal tissue, and are consistent with a nucleus-driven mechanical coupling between neighbouring cells. The present study provides a quantitative framework with which to understand mesoscopic tissue organization, thus enabling the formulation of testable hypotheses for future investigation.

Visual signals from the two eyes merge gradually as they pass through the primary visual cortex (V1). Here we use a computational model of Macaque V1 to study the first stage of this integration along the magnocellular pathway, in layer 4C, aiming to infer neuroanatomical origins of binocular response. It is known that neurons in layer 4C are predominantly monocular, though some do exhibit varying degrees of binocularity. We find (1) the emergence of narrow binocular strips along borders of ocular dominance columns (ODC), a finding that aligns with experiments; (2) most consistent with data is when 10 - 30% of interactions near ODC boundaries are cross-columnar; and (3) feedback from layer 6 is largely monocular. These results were obtained through systematic hypothesis testing using a multiscale model that is orders of magnitude faster than its biologically-detailed predecessors. We propose that multiscale modeling can be an effective tool for bridging anatomy and function.

Abnormal states such as erroneous behaviors are generally difficult to represent from neural data. However, such states are also known to have specific spatiotemporal features, indicating a feasibility of developing a method to focus on them. If a method can highlight these spatiotemporal features, it may effectively represent such abnormal states, helping evaluate abnormal brain functions. In the present study, we proposed the hierarchy of supported modules (HSM) to highlight spatiotemporal features that can represent abnormal states. HSM spatiotemporally transforms multidimensional neural time-series based on their spatiotemporal context. We evaluated HSM through decoding and similarity analyses using multiple publicly available datasets. In the HSM results, decoding accuracies were higher for erroneous behaviors than for normal behaviors, and similarities were lower between erroneous behaviors and normal behaviors than between normal behaviors, demonstrating the ability of HSM to capture the spatiotemporal features of erroneous behaviors. Surprisingly, many parts of these results were also present even before HSM learning, showing the virtue of HSM as a simple-to-use method. The proposed HSM method may help elucidate the mechanisms underlying erroneous behaviors.

Substantial progress in glial electrophysiology has revealed that astrocytes, which account for half of the cells in the human brain, exhibit membrane potentials that often reflect changes in the extracellular environment. Such responses are mediated by a variety of biochemicals, including potassium and neurotransmitters. Recent advances in voltage imaging have provided new insights into voltage activity in astrocyte peripheries, revealing highly localized depolarization that depends on local presynaptic activity. However, the electrophysiological properties of these isolated peripherals have not been explored due to limitations of spatial and temporal resolution. In this study, we aimed to explore differences in the electrophysiological response between whole-cell stimulation and isolated stimuli at different locations in the cell. Therefore, we constructed an empirical conductance-based NEURON model using a realistic morphology to simultaneously capture both astrocyte processes and soma electrophysiological dynamics. Our results predict a breakdown of the Nernstian behavior of astrocytes when potassium stimuli are localized. Instead, local responses are governed by their conductance ratios. Furthermore, we observe strong capabilities for isolating neurotransmitter responses to specific synaptic inputs, with minimal effect on the astrocyte soma. Our study highlights asymmetrical responses of astrocytic electrophysiology that depend on the spatial scale of stimulation.

Understanding how network connectivity shapes neural representations is central to systems neuroscience. While dimensionality reduction methods uncover low-dimensional manifold structure in population recordings, a rigorous framework connecting manifold geometry to network mechanisms and information encoding remains lacking. We develop a differential geometric approach for analyzing neural manifolds in rate-based recurrent networks receiving tuned feedforward inputs. We derive expressions for the pullback metric of neural manifolds, showing how input tuning curves, feedforward and recurrent synaptic connectivity shape manifold geometry. Critically, we establish that the Fisher information matrix at steady states also has the structure of a pullback metric, directly linking intrinsic manifold geometry to stimulus discriminability and information encoding. For noise with slow temporal correlations propagated through the network, we show that recurrent effects on information geometry cancel: Fisher information depends only on the feedforward connectivity. Thus, feedforward connectivity critically determines representational geometry. As an example, we demonstrate that the representation of space by a module of hexagonal grid cells is approximately isometric for random distribution of grid phases. Moreover, a linear feedforward transformation can map spatially random input tuning curves into a population of hexagonal grid cells, forming a toroidal manifold. Thus, feedforward connectivity alone can generate structured spatial representations without requiring carefully tuned recurrent connectivity or continuous attractor dynamics. Recurrent connectivity, however, is shown to improve stimulus encoding under fast noise, thereby implementing a selective noise reduction.

Transitive inference (TI) is the ability to reason about transitive relationships in an ordered set of items (e.g., if A>B and B>C, then A>C). TI is widely held to depend on a linear representation of the serial (rank) order of those items. By what computational mechanism is such an ordering constructed during learning, and how is it used to make choices that obey transitivity? Here we take a minimalist approach, applying least-squares estimation (LSE) to a serial learning task commonly used to test TI in humans and animals. In this formulation, LSE computes a linear classifier that maps task conditions onto behavioral outcomes. This algorithm makes no explicit assumptions about transitivity or serial order, yet it reproduces key empirical features of TI; namely, the ability to generalize beyond the training set, and a symbolic distance effect (SDE) in performance accuracy. Applying the classifier to individual items produces an internally ordered representation of rank from which both generalization and the SDE naturally emerge. The approach also yields a decision mechanism, in the form of a differencing operation, for selecting the correct item from any pair. These findings reframe TI as a linear classification problem, challenging conventional assumptions about the cognitive mechanisms required for transitive reasoning.

AO_SCPLOWBSTRACTC_SCPLOWDopamine activity in the tail of the striatum (TS) presents a novel challenge for reinforcement-learning theories of dopamine. Some studies suggest that TS-projecting dopamine signals encode aversive or threat prediction errors, whereas others argue that they encode action prediction errors involved in soft-habit formation. Here, we show that these accounts need not be mutually exclusive. We instantiate an entropy-regularised reinforcement-learning model in which TS-projecting dopamine neurons update both aversive values and the default policy. In this model, threat belief gates aversive value initialisations, producing TS-like activity during retreat from potentially threatening novel objects, while default-policy learning generates action prediction error signals that decline as actions become habitual. Our results further suggest why both of these signals may need to coexist in the temporal difference errors in our model, and qualitatively reproduce key response patterns and simulations from studies previously used to support both views. Beyond this descriptive reconciliation, our model simulations also highlight the normative role of the tail of the striatum in cautious behaviours in the context of potential threats and stable learning in the face of outcome uncertainty.