Biology subjects

Blanco-Pozo, M.

Publications and source records attributed to Blanco-Pozo, M..

6 recordsLinked to original sources

bioRxiv2026

A geometric and dynamical theory of latent computations in biological neural networks

Many neural recordings have revealed low-dimensional sets of behaviorally relevant variables encoded within large-scale neural activity patterns. However, dimensionality reduction analyses alone cannot yield causal explanations for how networks stably implement computations that are resilient to the substantial variability of single neuron dynamics. Further, existing methods for dimensionality reduction often rely on simplifying assumptions about network structure that limit their applicability and explanatory power. To provide a theoretical framework describing the dynamics of low-dimensional computation in high-dimensional neural networks, here we introduce the concept of latent processing units (LPUs), which are architecture-agnostic computational elements operating within biological neural circuitry. Six theorems governing coding and computation by LPUs collectively provide explanations for a range of common biological findings: low-dimensional sets of coding variables can generate high-dimensional neural dynamics; many neurons have activity patterns that represent behaviorally relevant variables but exert little influence on downstream circuits; linear readouts of neural population activity commonly permit near-optimal decoding; the drift of neural representations is often substantial even while network computations remain intact. Overall, our treatment of LPUs, as enacted in network dynamics, unifies the geometric and dynamical views of neural computation under a joint framework and provides systems neuroscience with a causal account of how the brain executes reliable computations.

neuroscience↗

bioRxiv2026

Low-dimensional Neural Codes Suppress Neuronal Noise and Extend the Working Memory Duration

Neurons are noisy computational substrates, yet large neural populations achieve reliable computation. What determines the maximal duration that a noisy population can sustain working memory? We study this question with recurrent networks subject to stochastic noise and present three theoretical results. First, networks suppress independent neuronal noise when activity lies on a low-dimensional latent manifold. Second, this structure induces correlated noise across neurons, limiting the downstream information that can be extracted. Third, these effects yield an analytical bound on working memory duration that scales linearly with network size. We test these predictions using large-scale neocortical recordings, and provide a behavioral signature in mice consistent with the theory. Overall, noise suppression constitutes a key functional benefit of low-dimensional neural coding, with which large populations sustain reliable working memory over extended timescales.

neuroscience↗

bioRxiv2026

Building Goal-Directed Cognitive Graphs

Flexible behavior requires building internal structured models from experience to support goal-directed action. Although environmental transition statistics accumulate gradually, how they are used to construct a compact goal-directed graph remains unclear. We introduce the Sparse Cognitive Graph (SCG), a reinforcement-learning framework that separates gradual transition learning from the sparse directed graph that governs valuation and action selection. In the SCG, transition statistics accumulate in a dense predictive representation, while nonlinear selection determines which transitions are expressed as graph edges. Consequently, gradual strengthening can trigger discrete reorganization of graph topology and abrupt behavioral shifts. Across human reward and transition revaluation tasks, the SCG explains bimodal and trimodal behavioral regimes as emergent consequences of distinct graph configurations. Across human and mouse two-step tasks, dynamic graph reconfiguration captures canonical reward-by-transition interactions without requiring mixtures of control systems. In mice, transitions preceding reward strengthened more rapidly, biasing graph topology toward reward-directed paths. Temporally precise optogenetic dopamine stimulation produced behavioral effects consistent with accelerated graph edge formation predicted by the SCG. The model further generates a testable prediction: graph topology determines the geometry of low-dimensional population activity. Directed acyclic graphs yield activity concentrated at graph entry and goal states, whereas cyclic graphs produce periodic, grid-like structure. Together, these findings identify reward-dependent graph reorganization as a computational principle that reconciles stable predictive learning with efficient goal-directed control.

neuroscience↗

bioRxiv2024

ActSort: An active-learning accelerated cell sorting algorithm for large-scale calcium imaging datasets

Recent advances in calcium imaging enable simultaneous recordings of up to a million neurons in behaving animals, producing datasets of unprecedented scales. Although individual neurons and their activity traces can be extracted from these videos with automated algorithms, the results often require human curation to remove false positives, a laborious process called cell sorting. To address this challenge, we introduce ActSort, an active-learning algorithm for sorting large-scale datasets that integrates features engineered by domain experts together with data formats with minimal memory requirements. By strategically bringing outlier cell candidates near the decision boundary up for annotation, ActSort reduces human labor to about 1-3% of cell candidates and improves curation accuracy by mitigating annotator bias. To facilitate the algorithms widespread adoption among experimental neuroscientists, we created a user-friendly software and conducted a first-of-its-kind benchmarking study involving about 160,000 annotations. Our tests validated ActSorts performance across different experimental conditions and datasets from multiple animals. Overall, ActSort addresses a crucial bottleneck in processing large-scale calcium videos of neural activity and thereby facilitates systems neuroscience experiments at previously inaccessible scales.

neuroscience↗

bioRxiv2023

Lasting dynamic effects of the psychedelic 2,5-dimethoxy-4-iodoamphetamine (+/-)-DOI) on cognitive flexibility

Psychedelic drugs can aid fast and lasting remission from various neuropsychiatric disorders, though the underlying mechanisms remain unclear. Preclinical studies suggest serotonergic psychedelics enhance neuronal plasticity, but whether neuroplastic changes can also be seen at cognitive and behavioural levels is unexplored. Here we show that a single dose of the psychedelic 2,5-dimethoxy-4-iodoamphetamine (({+/-})-DOI) affects structural brain plasticity and cognitive flexibility in young adult mice beyond the acute drug experience. Using ex vivo magnetic resonance imaging, we show increased volumes of several sensory and association areas one day after systemic administration of 2mgkg-1 ({+/-})-DOI. We then demonstrate lasting effects of ({+/-})-DOI on cognitive flexibility in a two-step probabilistic reversal learning task where 2mgkg-1 ({+/-})-DOI improved the rate of adaptation to a novel reversal in task structure occurring one-week post-treatment. Strikingly, ({+/-})-DOI-treated mice started learning from reward omissions, a unique strategy not typically seen in mice in this task, suggesting heightened sensitivity to previously overlooked cues. Crucially, further experiments revealed that ({+/-})-DOIs effects on cognitive flexibility were contingent on the timing between drug treatment and the novel reversal, as well as on the nature of the intervening experience. ({+/-})-DOIs facilitation of both cognitive adaptation and novel thinking strategies may contribute to the clinical benefits of psychedelic-assisted therapy, particularly in cases of perseverative behaviours and a resistance to change seen in depression, anxiety, or addiction. Furthermore, our findings highlight the crucial role of time-dependent neuroplasticity and the influence of experiential factors in shaping the therapeutic potential of psychedelic interventions for impaired cognitive flexibility.

animal behavior and cognition↗

bioRxiv2021

Dopamine reports reward prediction errors, but does not update policy, during inference-guided choice

Rewards are thought to influence future choices through dopaminergic reward prediction errors (RPEs) updating stored value estimates. However, accumulating evidence suggests that inference about hidden states of the environment may underlie much adaptive behaviour, and it is unclear how these two accounts of reward-guided decision-making should be integrated. Using a two-step task for mice, we show that dopamine reports RPEs using value information inferred from task structure knowledge, alongside information about recent reward rate and movement. Nonetheless, although rewards strongly influenced choices and dopamine, neither activating nor inhibiting dopamine neurons at trial outcome affected future choice. These data were recapitulated by a neural network model in which frontal cortex learned to track hidden task states by predicting observations, while basal ganglia learned corresponding values and actions via dopaminergic RPEs. Together, this two-process account reconciles how dopamine-independent state inference and dopamine-mediated reinforcement learning interact on different timescales to determine reward-guided choices.

neuroscience↗