Biology subjects

Latham, P.

Publications and source records attributed to Latham, P..

6 recordsLinked to original sources

bioRxiv2026

Structural Composition Enables Very Fast Learning

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.

neuroscience↗

bioRxiv2025

Role of NLRP3 activation in salt sensitive blood pressure regulation, effect of ND-13

Background and ObjectivesHigh salt intake is a major contributor to the development and exacerbation of hypertension, partly by inducing an inflammatory response through immune cell dysfunction. Inflammasomes, key components of the innate immune response, may influence blood pressure regulation. The renal DJ-1 protein is known for its antioxidant and anti-inflammatory properties. To explore novel pharmacological applications of renal DJ-1 pathway, we developed ND-13, a peptide consisting of 13 highly conserved amino acids derived from the DJ-1 sequence. In this study, we investigated the effects of ND-13 and MCC950, a specific NLRP3 inflammasome inhibitor, on blood pressure regulation in C57BL/6J mice on a high-salt diet. MethodsC57BL/6J mice were fed a high-salt diet (HS) for one week and then treated with ND-13 or MCC950, an NLRP3 inflammasome inhibitor. Subsequently, gene expression by qPCR, staining of immune cells, Sirius Red and Periodic Acid-Schiff (PAS) staining were determined in the mice kidneys, as well as the inflammasome activity in peritoneal cells. ResultsOne week of HS resulted increased in blood pressure, that was prevented by both ND-13 and MCC950 treatments. These treatments also prevented the increase in proteinuria that was accompanied by tubular protein deposits. Renal expression of inflammatory genes, immune cell infiltration, and renal collagen deposition were not observed in the HS group. Peritoneal macrophages isolated from HS treated mice exhibited enhanced IL-1{beta} release upon LPS+ATP stimulation, suggesting activation of the NLRP3 inflammasome. Treatment with ND-13 and MCC950 normalized this activity. Furthermore, ND-13 reduced IL-1{beta} mRNA expression in peritoneal macrophages. ConclusionsOur findings highlight the critical role of the NLRP3 inflammasome in salt-sensitive blood pressure regulation and suggest that ND-13 may serve as a potential therapeutic agent for preventing hypertension and associated inflammatory alterations induced by a high salt intake.

physiology↗

bioRxiv2024

Cell crowding induces TRPV4 inhibition and its relocation to plasma membranes, implicating pro-invasive cell volume reduction mechanotransduction pathway

Cell crowding is a common microenvironmental factor influencing various disease processes, but its role in promoting cell invasiveness remains unclear. This study investigates the biomechanical changes induced by cell crowding, focusing on pro-invasive cell volume reduction in ductal carcinoma in situ (DCIS). Crowding specifically enhanced invasiveness in high-grade DCIS cells through significant volume reduction compared to hyperplasia-mimicking or normal cells. Mass spectrometry revealed that crowding selectively relocated ion channels, including TRPV4, to the plasma membrane in high-grade DCIS cells. TRPV4 inhibition triggered by crowding decreased intracellular calcium levels, reduced cell volume, and increased invasion and motility. During this process, TRPV4 membrane relocation primed the channel for later activation, compensating for calcium loss. Analyses of patient-derived breast cancer tissues confirmed that plasma membrane- associated TRPV4 is specific to high-grade DCIS and indicates the presence of a pro-invasive cell volume reduction mechanotransduction pathway. Hyperosmotic conditions and pharmacologic TRPV4 inhibition mimicked crowding-induced effects, while TRPV4 activation reversed them. Silencing TRPV4 diminished mechanotransduction in high-grade DCIS cells, reducing calcium depletion, volume reduction, and motility. This study uncovers a novel pro-invasive mechanotransduction pathway driven by cell crowding and identifies TRPV4 as a potential biomarker for predicting invasion risk in DCIS patients.

cell biology↗

bioRxiv2023

Humans, rats and mice show species-specific adaptations to sensory statistics in categorisation behaviour

Animals must exploit environmental regularities to make adaptive decisions, yet the learning algorithms that enabels this flexibility remain unclear. A central question across neuroscience, cognitive science, and machine learning, is whether learning relies on generative or discriminative strategies. Generative learners build internal models the sensory world itself, capturing its statistical structure; discriminative learners map stimuli directly onto choices, ignoring input statistics. These strategies rely on fundamentally different internal representations and entail distinct computational trade-offs: generative learning supports flexible generalisation and transfer, whereas discriminative learning is efficient but task-specific. We compared humans, rats, and mice performing the same auditory categorisation task, where category boundaries and rewards were fixed but sensory statistics varied. All species adapted their behaviour near-optimally, consistent with a normative observer constrained by sensory and decision noise. Yet their underlying algorithms diverged: humans predominantly relied on generative representations, mice on discriminative boundary-tracking, and rats spanned both regimes. Crucially, end-point performance concealed these differences--only learning trajectories and trial-to-trial updates revealed the divergence. These results show that similar near-optimal behaviour can mask fundamentally different internal representations, establishing a comparative framework for uncovering the hidden strategies that support statistical learning.

animal behavior and cognition↗

bioRxiv2022

Evolution of neural activity in circuits bridging sensory and abstract knowledge

The ability to associate sensory stimuli with abstract classes is critical for survival. How are these associations implemented in brain circuits? And what governs how neural activity evolves during abstract knowledge acquisition? To investigate these questions, we consider a circuit model that learns to map sensory input to abstract classes via gradient descent synaptic plasticity. We focus on typical neuroscience tasks (simple, and context-dependent, categorization), and study how both synaptic connectivity and neural activity evolve during learning. To make contact with the current generation of experiments, we analyze activity via standard measures such as selectivity, correlations, and tuning symmetry. We find that the model is able to recapitulate experimental observations, including seemingly disparate ones. We determine how, in the model, the behaviour of these measures depends on details of the circuit and the task. These dependencies make experimentally-testable predictions about the circuitry supporting abstract knowledge acquisition in the brain.

neuroscience↗

bioRxiv2021

A rapid and efficient learning rule for biological neural circuits

The dominant view in neuroscience is that changes in synaptic weights underlie learning. It is unclear, however, how the brain is able to determine which synapses should change, and by how much. This uncertainty stands in sharp contrast to deep learning, where changes in weights are explicitly engineered to optimize performance. However, the main tool for that, backpropagation, has two problems. One is neuro-science related: it is not biologically plausible. The other is inherent: networks trained with this rule tend to forget old tasks when learning new ones. Here we introduce the Dendritic Gated Network (DGN), a variant of the Gated Linear Network, which offers a biologically plausible alternative to backpropagation. DGNs combine dendritic gating (whereby interneurons target dendrites to shape neuronal responses) with local learning rules to yield provably efficient performance. They are significantly more data efficient than conventional artificial networks, and are highly resistant to forgetting. Consequently, they perform well on a variety of tasks, in some cases better than backpropagation. Importantly, DGNs have structural and functional similarities to the cerebellum, a link that we strengthen by using in vivo two-photon calcium imaging to show that single interneurons suppress activity in individual dendritic branches of Purkinje cells, a key feature of the model. Thus, DGNs leverage targeted dendritic inhibition and local learning - two features ubiquitous in the brain - to achieve fast and efficient learning.

neuroscience↗