Biology subjects

Costa, R. P.

Publications and source records attributed to Costa, R. P..

15 recordsLinked to original sources

bioRxiv2026

Hunger reconfigures a reward learning circuit into a memory competent mode

Internal states such as hunger dynamically reshape activity across circuits to support resource seeking. Neuromodulation provides a means of controlling such physiological properties of neurons, but how this flexibility regulates memory networks remains unclear. Here, we describe how hunger reconfigures a dopaminergic food-reward circuit between two physiological modes that support memory formation, in Drosophila. Starvation suppresses baseline dopaminergic activity through peptidergic signalling, enabling reward-evoked, large-amplitude dopamine neuron spikes to reinforce learning. This spiking mode can be engaged by sugar consumption and persists beyond feeding, reflecting the fly's satiety state. Persistent dopaminergic large-amplitude spiking reinforces learning and transitions the memory network into a mode that prioritizes consolidation of recently acquired memories. The transition from hunger to satiation is also reflected in the activity of postsynaptic output neurons that shift from a tonic, decorrelated mode that is responsive to dopamine into a bursting, correlated mode in which further dopaminergic modulation is occluded. Therefore, nutrient deprivation reconfigures the memory circuit into a learning competent dopamine-receptive mode, which persistent reinforcing dopamine then switches into a satiated mode driving memory consolidation. Together these processes mechanistically intertwine state-dependent reward-signalling with subsequent memory stabilization through transitions in physiological mode.

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bioRxiv2026

Unifying error and reward action learning: a cerebello-basal ganglia theory

Learning depends on both reward- and error-based feedback, yet how the brain integrates these distinct signals to guide behaviour remains fundamentally unclear. Here, using a normative computational framework, we derive credit assignment rules for both reward-based learning (RBL) and error-based learning (EBL). In contrast to existing dual-policy accounts, our approach demonstrates that RBL and EBL updates can be reformulated into a shared action-gradient space that directly updates a single downstream policy. First, we map this action-gradient framework onto a systems-level account of coordinated interactions between the basal ganglia, cerebellum, and cortex. The model reproduces key behavioral features across both learning regimes, generates experimentally testable predictions, and provides a unified computational account of motor deficits observed in patients with cerebellar and basal ganglia disorders. Together, our work offers a normative, brain-wide framework for how distributed brain systems integrate reinforcement and error-driven feedback toward a common behavioral objective.

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bioRxiv2026

Cell-type-specific cortical feedback coordinates hierarchical credit assignment

Learning is thought to arise from synaptic modifications across brain-wide circuits, yet how these networks coordinate plasticity to support complex behaviour is not known. Inspired by deep learning, we introduce a theory of hierarchical credit assignment in which pathway-specific cortical feedback drives dendrite-dependent burst plasticity across the cortex. We show that this principle enables learning in dynamic settings, complex visual recognition, and goal-directed tasks, mechanistically linking credit assignment to cell-type-specific regulation of dendritic excitation-inhibition balance. Our framework provides a unified account of diverse experimental phenomena -- including cell-type-specific modulation of synaptic plasticity, learning-dependent changes in interneurons, and neuron-specific dendritic error signals. Furthermore, the theory predicts that interneurons constrain the dimensionality of error feedback, offering a functional rationale for cortex-wide gradients in interneuron density. Together, these findings reveal how distinct cortical cell types cooperatively coordinate learning, bridging the gap between synaptic plasticity, circuit-level computation, and behaviour.

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bioRxiv2026

Granule cells reorient cortical manifolds to separate contexts but preserve their geometry

To learn effectively, animals must generalize across yet distinguish between related contexts. Generalization relies on low-dimensional neural manifolds found throughout the neocortex1,2, which accelerate learning by constraining neural activity to task-relevant axes3. Conversely, context separation is attributed to neural expansion layers that can project information into high-dimensional feature spaces4,5, most famously cerebellar granule cells (GrCs)6-8. To investigate the generalization-separation tradeoff, we simultaneously imaged key nodes in the universal cortico-cerebellar pathway9--premotor layer 5 pyramidal tract (L5PT) and GrCs--during parallel learning of two distinct skills with shared temporal structure. Rather than expanding the cortical representations, GrCs retained their low-rank encoding of each task. Across contexts, despite stable cortico-cerebellar coupling, L5PT activity patterns generalized while GrC patterns temporally remapped. But rather than independently scrambling, GrC populations remapped coherently: their low-dimensional trajectories "rotated" apart between tasks, separating the contexts while preserving the cortical geometry of each. Moreover, GrC trajectories diverged most strongly in expert animals. This suggests a fundamental architectural division of labor: the cortex provides invariant dynamic primitives for smooth generalization, while cerebellar activity reconfigures them to drive context-specific output.

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bioRxiv2025

Credit Assignment via Behavioral Timescale Synaptic Plasticity: Theoretical Frameworks

Behavioral Timescale Synaptic Plasticity (BTSP) is a form of synaptic plasticity in which dendritic Ca2+ plateau potentials in hippocampal pyramidal neurons drive rapid place field formation. Unlike traditional learning rules, BTSP learns correlations on the timescales of seconds and rapidly changes single-unit activity in only a few trials. To explore how BTSP-like learning can be integrated into network models, we propose a generalized BTSP rule (gBTSP), which we apply to unsupervised and supervised learning tasks, in both feedforward and recurrent networks. Unsupervised gBTSP mirrors classical frameworks of competitive learning, learning place field maps (in the feed-forward case), and attractive memory networks (in the recurrent case). For supervised learning, we show that plateau events can reduce task error, enabling gBTSP to solve tasks such as trajectory matching and delayed non-match-to-sample. However, we find that credit assignment via gBTSP becomes harder to achieve with increased network depth or CA3-like recurrence. This suggests that additional features may be needed to support BTSP-mediated few-shot learning of complex tasks in the hippocampus.

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bioRxiv2025

Distinct roles of cortical layer 5 subtypes in associative learning

Adaptive behavior is critically dependent on associative learning, where environmental cues are linked with subsequent positive or negative outcomes. In mammals, primary neocortical sensory areas serve as pivotal nodes in this process, processing stimuli and distributing information to cortical and subcortical networks. Layer 5 (L5) of the cortex comprises two types of pyramidal projection neurons--intratelencephalic (IT) and extratelencephalic (ET) neurons--each with distinct downstream targets. Despite the crucial function of L5 as a main output node of the cortex, the specific contributions of these L5 neuronal subtypes to associative learning remain poorly understood. In the present study, by leveraging transgenic mouse lines, we distinguished IT and ET neurons in the primary somatosensory cortex and examined their roles in a whisker-based frequency-discrimination learning task. Longitudinal two-photon calcium imaging revealed distinct response characteristics between IT and ET neurons throughout learning. Interestingly, the activity of IT neurons hardly changed over the five days of learning, while the activity of ET neurons developed robustly. Furthermore, IT neurons appeared to show stimuli encoding from the beginning, whereas the ET neurons became increasingly responsive to stimuli associated with reward. Chemogenetic silencing of either IT or ET neurons both impaired learning, but in strikingly distinct ways, each associated with a different phase of learning. By modeling the response characteristics of IT and ET neurons using a reinforcement learning framework, we show that IT neurons primarily encode sensory stimuli, and their representations are critical for forming stimulus-reward associations. ET neurons instead represent the value of the stimulus, used for refining behavior. Thus, our results delineate the distinct roles of L5 IT and ET neurons, underscoring their integral and complementary contributions to associative learning.

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bioRxiv2024

A Cortical Microcircuit for Region-Specific Credit Assignment in Reinforcement Learning

The distributed architecture of the cortex poses a fundamental challenge for reinforcement learning: how to assign credit specifically to regions that contribute to successful behavior? Cortical neurons can be driven by both global reinforcers, like rewards, and local sensory features, making it difficult to disentangle these influences. To address this, we investigated cortical reinforcement learning by manipulating the reward-predictive sensory modality during learning tasks, while monitoring key regulators of cortical activity--local inhibitory neurons, and cholinergic inputs. We found that VIP interneurons are broadly recruited by reward-predictive cues via a modality-independent cholinergic signal. However, when task demands aligned with local computation, SST interneurons suppressed VIP recruitment through an inhibitory feedback loop. A computational model demonstrates that this cholinergic-VIP-SST interneuron circuit motif enables targeted reinforcement learning and region-specific credit assignment in the cortex. These results offer a neurobiologically-grounded framework for how the cortex uses global reinforcement signals to direct plasticity to task-relevant regions, enabling those regions to adapt and fine-tune their responses.

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bioRxiv2024

A theory of temporal self-supervised learning in neocortical layers

The neocortex constructs an internal representation of the world, but the underlying circuitry and computational principles remain unclear. Inspired by self-supervised learning algorithms, we introduce a computational theory wherein layer 2/3 (L2/3) learns to predict incoming sensory stimuli by comparing previous sensory inputs, relayed via layer 4, with current thalamic inputs arriving at layer 5 (L5). We demonstrate that our model accurately predicts sensory information in context-dependent temporal tasks, and that its predictions are robust to noisy and occluded sensory input. Additionally, our model generates layer-specific sparsity and latent representations, consistent with experimental observations. Next, using a sensorimotor task, we show that the models L2/3 and L5 prediction errors mirror mismatch responses observed in awake, behaving mice. Finally, through manipulations, we offer testable predictions to unveil the computational roles of various cortical features. In summary, our findings suggest that the multi-layered neocortex empowers the brain with self-supervised predictive learning.

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bioRxiv2023

Hippocampal networks support reinforcement learning in partially observable environments

Mastering navigation in environments with limited visibility is crucial for survival. Although the hippocam-pus has been associated with goal-oriented navigation, its role in real-world behaviour remains unclear. To investigate this, we combined deep reinforcement learning (RL) modelling with behavioural and neural data analysis. First, we trained RL agents in partially observable environments using egocentric and allocentric tasks. We show that agents equipped with recurrent hippocampal circuitry, but not purely feedforward networks, learned the tasks in line with animal behaviour. Next, using dimensionality reduction, our agents predicted reward, strategy, and temporal rep-resentations, which we validated experimentally using hippocampal recordings. Moreover, hippocampal RL agents predicted state-specific trajectories, mirroring empirical findings. In contrast, agents trained in fully observable en-vironments failed to capture experimental observations. Finally, we show that hippocampal-like RL agents demon-strated improved generalisation across novel task conditions. In summary, our findings suggest an important role of hippocampal networks in facilitating reinforcement learning in naturalistic environments.

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bioRxiv2022

Cerebellar-driven cortical dynamics enable task acquisition, switching and consolidation

To drive behavior, the cortex must bridge sensory cues with future outcomes. However, the principles by which cortical networks learn such sensory-behavioural transformations remain largely elusive. Here, we posit that the cerebellum assumes a crucial role in driving cortical dynamics, thereby enabling rapid and flexible task acquisition. We introduce a computational model of cerebellar networks which learn to drive cortical networks with task-outcome predictions. First, using sensorimotor tasks we show that cerebellar feedback in the presence of minimal cortical plasticity is suffcient for rapid task acquisition and multiple task switching. Next, we demonstrate that, when trained in working memory tasks, the cerebellum can also underlie the maintenance of cognitive-specific dynamics, explaining a range of optogenetic and behavioural observations. Finally, using our model we introduce a systems consolidation theory in which task information is gradually transferred from the cerebellum to the cortex. In summary, our findings suggest that cortico-cerebellar loops play a pivotal role in task acquisition, switching, and consolidation within the brain.

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bioRxiv2022

Distributional coding of associative learning within projection-defined populations of midbrain dopamine neurons

Midbrain dopamine neurons are thought to play key roles in learning by conveying the difference between expected and actual outcomes. While this teaching signal is often considered to be uniform, recent evidence instead supports diversity in dopamine signaling. However, it remains poorly understood how heterogeneous signals might be organized to facilitate the role of downstream circuits mediating distinct aspects of behavior. Here we investigated the organizational logic of dopaminergic signaling by recording and labeling individual midbrain dopamine neurons during associative behavior. We defined combinations of protein expression and cellular localization to sort recorded neurons according to the striatal regions they innervate. Our findings show that reward information and task variables are not only heterogeneously encoded, with multiplexing, but also differentially distributed across populations of dopamine neurons projecting to different regions of striatum. These data, supported by computational modelling, indicate that such distributional coding can maximize dynamic range and tailor dopamine signals to facilitate the specialized roles of different striatal regions.

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bioRxiv2022

Cerebro-cerebellar networks facilitate learning through feedback decoupling

Behavioural feedback is critical for learning in the cerebral cortex. However, such feedback is often not readily available. How the cerebral cortex learns efficiently despite the sparse nature of feedback remains unclear. Inspired by recent deep learning algorithms, we introduce a systems-level computational model of cerebro-cerebellar interactions. In this model a cerebral recurrent network receives feedback predictions from a cerebellar network, thereby decoupling learning in cerebral networks from future feedback. When trained in a simple sensorimotor task the model shows faster learning and reduced dysmetria-like behaviours, in line with the widely observed functional impact of the cerebellum. Next, we demonstrate that these results generalise to more complex motor and cognitive tasks. Finally, the model makes several experimentally testable predictions regarding (1) cerebro-cerebellar task-specific representations over learning, (2) task-specific benefits of cerebellar predictions and (3) the differential impact of cerebellar and inferior olive lesions. Overall, our work offers a theoretical framework of cerebro-cerebellar networks as feedback decoupling machines.

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bioRxiv2021

Pre- and postsynaptically expressed spike-timing-dependent plasticity contribute differentially to neuronal learning

A plethora of experimental studies have shown that long-term synaptic plasticity can be expressed pre- or postsynaptically depending on a range of factors such as developmental stage, synapse type, and activity patterns. The functional consequences of this diversity are not clear, although it is understood that whereas postsynaptic expression of plasticity predominantly affects synaptic response amplitude, presynaptic expression alters both synaptic response amplitude and short-term dynamics. In most models of neuronal learning, long-term synaptic plasticity is implemented as changes in connective weights. The consideration of long-term plasticity as a fixed change in amplitude corresponds more closely to post-than to presynaptic expression, which means theoretical outcomes based on this choice of implementation may have a postsynaptic bias. To explore the functional implications of the diversity of expression of long-term synaptic plasticity, we adapted a model of long-term plasticity, more specifically spike-timing-dependent plasticity (STDP), such that it was expressed either independently pre- or postsynaptically, or in a mixture of both ways. We compared pair-based standard STDP models and a biologically tuned triplet STDP model, and investigated the outcomes in a minimal setting, using two different learning schemes: in the first, inputs were triggered at different latencies, and in the second a subset of inputs were temporally correlated. We found that presynaptic changes adjusted the speed of learning, while postsynaptic expression was more efficient at regulating spike timing and frequency. When combining both expression loci, postsynaptic changes amplified the response range, while presynaptic plasticity allowed control over postsynaptic firing rates, potentially providing a form of activity homeostasis. Our findings highlight how the seemingly innocuous choice of implementing synaptic plasticity by single weight modification may unwittingly introduce a postsynaptic bias in modelling outcomes. We conclude that pre- and postsynaptically expressed plasticity are not interchangeable, but enable complimentary functions. Author summaryDifferences between functional properties of pre- or postsynaptically expressed long-term plasticity have not yet been explored in much detail. In this paper, we used minimalist models of STDP with different expression loci, in search of fundamental functional consequences. Biologically, presynaptic expression acts mostly on neurotransmitter release, thereby altering short-term synaptic dynamics, whereas postsynaptic expression affects mainly synaptic gain. We compared models where plasticity was expressed only presynaptically or postsynaptically, or in both ways. We found that postsynaptic plasticity had a bigger impact over response times, while both pre- and postsynaptic plasticity were similarly capable of detecting correlated inputs. A model with biologically tuned expression of plasticity also completed these tasks over a range of frequencies. Also, postsynaptic spiking frequency was not directly affected by presynaptic plasticity of short-term plasticity alone, however in combination with a postsynaptic component, it helped restrain positive feedback, contributing to activity homeostasis. In conclusion, expression locus may determine affinity for distinct coding schemes while also contributing to keep activity within bounds. Our findings highlight the importance of carefully implementing expression of plasticity in biological modelling, since the locus of expression may affect functional outcomes in simulations.

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bioRxiv2021

Developmental depression-facilitation shift controls excitation-inhibition balance

Changes in the short-term dynamics of excitatory synapses over development have been observed throughout cortex, but their purpose and consequences remain unclear. Here, we propose that developmental changes in synaptic dynamics buffer the effect of slow inhibitory long-term plasticity, allowing for continuously stable neural activity. Using computational modelling we demonstrate that early in development excitatory short-term depression quickly stabilises neural activity, even in the face of strong, unbalanced excitation. We introduce a model of the commonly observed developmental shift from depression to facilitation and show that neural activity remains stable throughout development, while inhibitory synaptic plasticity slowly balances excitation, consistent with experimental observations. Our model predicts changes in the input responses from phasic to phasic-and-tonic and more precise spike timings. We also observe a gradual emergence of synaptic working memory mediated by short-term facilitation. We conclude that the developmental depression-to-facilitation shift may control excitation-inhibition balance throughout development with important functional consequences.

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bioRxiv2018

Pre- and postsynaptically expressed spiking-timing-dependent plasticity contribute differentially to neuronal learning

A plethora of experimental studies have shown that long-term plasticity can be expressed pre- or postsynaptically depending on a range of factors such as developmental stage, synapse type, and activity patterns. The functional consequences of this diversity are unknown. However, in models of neuronal learning, long-term synaptic plasticity is implemented as changes in connective weights. Whereas postsynaptic expression of plasticity predominantly affects synaptic response amplitude, presynaptic expression alters both synaptic response amplitude and short-term dynamics. In other words, the consideration of long-term plasticity as a fixed change in amplitude corresponds more closely to post- than to presynaptic expression, which means theoretical outcomes based on this choice of implementation may have a postsynaptic bias. To explore the functional implications of the diversity of expression of long-term synaptic plasticity, we modelled spike-timing-dependent plasticity (STDP) such that it was expressed either pre- or postsynaptically, or both. We tested pair-based standard STDP models and a biologically tuned triplet STDP model, and investigated the outcome in a feed-forward setting, with two different learning schemes: either inputs were triggered at different latencies, or a subset of inputs were temporally correlated. Across different STDP models and learning paradigms, we found that presynaptic changes adjusted the speed of learning, while postsynaptic expression was better at regulating spike timing and frequency. When combining both expression loci, postsynaptic changes amplified the response range, while presynaptic plasticity maintained control over postsynaptic firing rates, potentially providing a form of activity homeostasis. Our findings highlight how the seemingly innocuous choice of implementing synaptic plasticity by direct weight modification may unwittingly introduce a postsynaptic bias in modelling outcomes. We conclude that pre- and postsynaptically expressed plasticity are not interchangeable, but enable complimentary functions.

neuroscience↗