Biology subjects

Pena Fernandez, M.

Publications and source records attributed to Pena Fernandez, M..

7 recordsLinked to original sources

bioRxiv2026

Signed-XOR Error and Sparse Coding in a Dale-Complaint Substrate for Sequence Memorization

How much machinery does a network need to memorize and recall discrete sequences when constrained to a biologically plausible substrate? We address this question using 50 short monophonic melodies in 4/4, used only as a controlled sequence-memory benchmark. Each beat is encoded with two clean one-hot populations - a 12-way pitch code and a separate 2-way {onset, sustain} code - and the decoder emits the same 14-dimensional code, so the autoregressive loop closes in a single neural format. The model obeys Dales law: latent units are excitatory or inhibitory, synaptic weights are non-negative, the decoder is implemented as explicit multi-contact bundles, and the encoder is a frozen sparse random projection wired at cortical ([~]10%) density. On this substrate, a local ENGRAMMER signed-XOR read-out rule combined with a sparse k-winner-take-all code stores the training corpus exactly. With a modest latent expansion (L = 512), the model reaches 100% teacher-forced and autoregressive pitch accuracy, recognizes all training melodies, and separates all held-out melodies as novel with zero overlap. Ablations show that the signed error, sparse code, explicit E/I routing, and multi-contact synapses are the main load-bearing ingredients, whereas learning the encoder is strongly detrimental and dense input wiring does not help. Capacity sweeps show that Dales law mainly increases the capacity required for stable autoregressive recall: teacher-forced storage saturates between L = 128 and L = 256, while free-running recall becomes perfect by L = 512. A matched random corpus reaches the same final fidelity and is recalled at least as well at every capacity, indicating that musical structure does not improve recall on this benchmark and that final fidelity is set by capacity rather than by structure. The result is a Dale-compliant, gradient-free sparse associative memory rather than a general sequence learner.

neuroscience↗

bioRxiv2026

From homeostasis to credit assignment: a signed-XOR connectomic motif for local directional error signalling

AO_SCPLOWBSTRACTC_SCPLOWBiological neural circuits are widely thought to require local error signals that tell synapses not only that a prediction is wrong, but also in which direction to change. We previously proposed that a six-neuron XOR motif acts as a homeostatic comparator: matched sensory and predictive signals cancel locally, whereas mismatches propagate an error signal. We also showed that a shallow autoencoder can learn MNIST using a signed-XOR learning rule with local decoder errors and random feedback alignment, without gradient backpropagation. Here we introduce the signed-XOR motif, an eight-neuron, twelve-edge directed signed circuit that extends the XOR comparator with two feedback channels of opposite neurotransmitter identity. By construction, the motif can convert a binary mismatch into directional error signalling, with one pathway encoding potentiation and the other depression, while respecting Dales principle. We provide open-source tools to enumerate the motif at connectome scale and test its enrichment against degree- and sign-preserving null models. The motif is enriched 24.3x in C. elegans (Z = 52.2), significantly enriched in 59/80 FlyWire Drosophila neuropils including AVLP_L (13.9x, Z = 94.4), and strongly enriched in layers 2/3-5 of a biophysically detailed mouse primary visual cortex model (global 315x; per-pivot medians up to 852 x) while absent from layer 6. The same layer-specific pattern is found in the axon-proofread subset of the EM-reconstructed MICrONS connectome. A Brian2 leaky integrate-and-fire implementation reproduces the signed-XOR truth table, remains robust to Poisson drive, produces a graded signed error, and requires a fast-spiking parvalbumin-like pivot. These results identify signed-XOR as a recurrent connectomic pattern compatible with local homeostatic error cancellation and directional credit-assignment signals. Author SummaryHow does a brain decide which of its connections to adjust when it makes a mistake? Unlike an artificial network, it has no global error signal supplied from outside: each connection can react only to the neurons it directly touches. We ask whether a small, repeating wiring pattern could provide such a local correction signal. The pattern we study, the signed-XOR motif, compares an incoming signal with the brains own prediction of it. When the two agree, the circuit stays quiet, so already-expected activity is not relayed onward. When they disagree, it does more than flag an error: it also indicates the direction of the fix, routing it through two separate channels, one meaning "strengthen", the other "weaken", consistent with the biological rule that each neuron acts with a single sign. We provide open software to search for this pattern in three nervous systems, a worm, a fly, and a detailed model of mouse visual cortex, and find it more often than chance wiring predicts, with a striking layer-specific distribution in cortex. We also simulated the eight-cell circuit with realistic spiking neurons and confirmed that it can perform the computation, but only when its inhibitory cell is a fast-spiking type like those concentrated in the enriched layers. We do not claim that any brain uses this circuit to learn or memorize. What we provide is a specific motif that could deliver a local, directional error signal that may be useful for a neuromorphic implementation.

neuroscience↗

bioRxiv2026

Toward defining loss functions in neuroscience: an XOR-based neuronal mechanism

AO_SCPLOWBSTRACTC_SCPLOWOne of the most compelling ideas for bridging neuroscience and artificial neural networks is the establishment of a framework based on three main components: network architecture, optimization mechanism, and loss (or objective) function to be minimized. While the first two components have been extensively explored, the definition of a loss or objective function in neuroscience has been addressed less thoroughly, often from perspectives such as predictive coding. In this work, we propose an elementary loss function grounded in the comparison of neuronal responses to two signals: an external one, used for learning, and an internal one, reflecting the acquired knowledge. The loss function is thus simply the basic difference between the two, which, in terms of logical signals, corresponds to a well-known non-linearly separable function: the XOR function. We illustrate with a computational example how a binarized image recognition algorithm can be straightforwardly implemented in an autoencoder, and we show how a neuronal motif organized around an inhibitory neuron could implement such XOR operation and provide a feedback signal that makes optimization possible.

neuroscience↗

bioRxiv2025

Micromechanical characterisation of osteoarthritic subchondral bone by micropillar compression

Osteoarthritis (OA) is a multifaceted joint disease which poses significant socioeconomic burdens and remains a significant clinical challenge. Evidence suggests that structural and mechanical changes in subchondral bone influence the pathogenesis and development of OA, leading to diminished bone quality and cartilage degeneration. While changes in microstructure and tissue scale elastic properties are well reported, the tissue yield response of subchondral bone in OA and their correlation with compositional changes have not been investigated. Here, we performed quasistatic micropillar compression and nanoindentation within the subchondral bone plate and trabeculae of hydrated non-diseased (ND) and OA affected specimens retrieved from the distal tibia in vivo. The micropillars, extracted by laser ablation, exhibited a taper angle which mandated the use of an in silico micropillar compression routine to back-calculate elastic modulus and strength of the bone tissue that comprised each micropillar. Elastic modulus remained unchanged between ND and OA subchondral bone, whereas strength increased from 46.0 MPa to 57.3 MPa in OA subchondral trabecular bone but not in the bone plate. Micropillar matched Raman spectroscopy and quantitative backscattered electron imaging revealed mineralisation is the underlying determinant of elastic modulus and strength at the microscale. By combining micromechanical and tissue compositional analyses, we investigated how the mechanical properties are related and how these properties are affected in subchondral bone by OA. Our results may be of value in the development and optimisation of interventions used to alleviate the socioeconomic burdens associated with this debilitating joint disease. Statement of significanceOsteoarthritis imposes significant socioeconomic burden, necessitating ever improving treatment strategies. Abnormal mechanical forces at the joint, such as injury, overloading, or aging are associated with osteoarthritis development. While healthy tissue is well understood, the impact of diseases on tissue mechanical properties needs to be brought on a similar level of understanding. We present in this study the first use of micropillar compression for assessing the tissue scale compressive strength of subchondral bone in osteoarthritis. Strength was increased in osteoarthritis compared to non-diseased tissue only in trabeculae subjacent to the bone plate. Compositional analysis confirmed mineralisation as a key determinant of tissue mechanical properties. This integration of micromechanical and compositional analyses sheds light on tissue properties and how these are affected by osteoarthritis in subchondral bone.

bioengineering↗

bioRxiv2023

QCT-based computational bone strength assessment updated with MRI derived 'hidden' microporosity

Microdamage accumulated through sustained periods of cyclic loading or single over-loading events contributes to bone fragility through a reduction in stiffness and strength. Monitoring microdamage in vivo remains unattainable by clinical imaging modalities. As such, there are no established computational methods for clinical fracture risk assessment that account for microdamage that exists in vivo at any specific timepoint. We propose a method that combines multiple clinical imaging modalities to identify an indicative surro-gate, which we term hidden porosity, that incorporates pre-existing bone microdamage in vivo. To do so, we use the third metacarpal bone of the equine athlete as an exemplary model for fatigue induced microdamage, which coalesces in the subchondral bone. N=10 metacarpals were scanned by clinical quantitative computed tomography (QCT) and mag-netic resonance imaging (MRI). We used a patch-based similarity method to quantify the signal intensity of a fluid sensitive MRI sequence in bone regions where microdamage coa-lesces. The method generated MRI-derived pseudoCT images which were then used to de-termine a pre-existing damage (Dpex) variable to quantify the proposed surrogate and which we incorporate into a nonlinear constitutive model for bone tissue. The minimum, median, and maximum detected Dpex of 0.059, 0.209, and 0.353 reduced material stiffness by 5.9%, 20.9%, and 35.3% as well as yield stress by 5.9%, 20.3%, and 35.3%. Limb-specific voxel-based finite element meshes were equipped with the updated material model. Lateral and medial condyles of each metacarpal were loaded to simulate physiological joint loading dur-ing gallop. The degree of detected Dpex correlated with a relative reduction in both condylar stiffness (p = 0.001, R2 > 0.74) and strength (p < 0.001, R2 > 0.80). Our results illustrate the complementary value of looking beyond clinical CT, which neglects the inclusion of micro-damage due to partial volume effects. As we use clinically available imaging techniques, our results may aid research beyond the equine model on fracture risk assessment in human diseases such as osteoarthritis, bone cancer, or osteoporosis.

bioengineering↗

bioRxiv2022

Towards modelling cold-water coral reef-scale crumbling: Including morphological variability in mechanical surrogate models

The structural complexity of cold-water corals is threatened by ocean acidification. Increased porosity and weakening of structurally critical parts of the reef framework may lead to rapid physical collapse on an ecosystem scale, reducing their potential for biodiversity support. We can use computational models to describe the mechanisms leading to reef-crumbling. How-ever, the implementation of such models into an efficient predictive tool that allows us to determine risk and timescales of reef collapse is missing. Here, we identified possible surrogate models to represent the branching architecture of the cold-water coral species Lophelia pertusa. For length scales greater than 13 cm, a continuum finite element mechanical approach can be used to analyse mechanical competence whereas at smaller length scales, mechanical surrogate models need to explicitly account for the statistical differences in the structure. We showed large morphological variations between L. pertusa colonies and branches, as well as dead and live skeletal structures, which need to be considered for the development of rapid monitoring tools for predicting risk of cold-water coral reefs crumbling. This will allow us to investigate timescales of changes, including the impact of exposure times to acidified waters on reef-crumbling.

bioengineering↗

bioRxiv2022

Nonlinear micro finite element models based on digital volume correlation measurements predict early microdamage in newly formed bone

Bone regeneration in critical-sized defects is a clinical challenge, with biomaterials under constant development aiming at enhancing the natural bone healing process. The delivery of bone morphogenetic proteins (BMPs) in appropriate carriers represents a promising strategy for bone defect treatment but optimisation of the spatial-temporal release is still needed for the regeneration of bone with biological, structural, and mechanical properties comparable to the native tissue. Nonlinear micro finite element (FE) models can address some of these challenges by providing a tool able to predict the biomechanical strength and microdamage onset in newly formed bone when subjected to physiological or supraphysiological loads. Yet, these models need to be validated against experimental data. In this study, experimental local displacements in newly formed bone induced by osteoinductive biomaterials subjected to in situ X-ray computed tomography compression in the apparent elastic regime and measured using digital volume correlation (DVC) were used to validate FE models. Displacement predictions from homogeneous linear FE models were highly correlated to DVC-measured local displacements, while tissue heterogeneity capturing mineralisation differences showed negligible effects. Nonlinear FE models improved the correlation and showed that tissue microdamage occurs at low apparent strains. Microdamage seemed to occur next to large cavities or in biomaterial-induced thin trabeculae, independent of the mineralisation. While localisation of plastic strain accumulation was similar, the amount of damage accumulated in these locations was slightly higher when including material heterogeneity. These results demonstrate the ability of the nonlinear FE model to capture local microdamage in newly formed bone tissue and can be exploited to improve the current understanding of healing bone and mechanical competence. This will ultimately aid the development of BMPs delivery systems for bone defect treatment able to regenerate bone with optimal biological, mechanical, and structural properties.

bioengineering↗