Biology subjects

Houghton, C. J.

Publications and source records attributed to Houghton, C. J..

10 recordsLinked to original sources

bioRxiv2026

Non-random brain connectome wiring enables robust and efficient neural network function under high sparsity

The connectivity of brain networks is extremely sparse due to metabolic, physical and spatial constraints. Although wiring sparsity can confer computational advantages for biological and artificial neural networks, sparse networks require fine parameter tuning and exhibit strong sensitivity to perturbations. How brains achieve their efficiency and robustness is unclear. Here we addressed this by analysing the dynamical properties of Echo State Networks with wiring based on the Drosophila melanogaster fruit fly connectome, compared with sparsity-matched random-wiring networks. We evaluated these networks on a set of eight cognitive tasks, and found that connectome-based neural networks (CoNNs) typically showed narrowly distributed task engagement across their neurons. The importance of a neuron for task performance correlated with its node degree, local clustering, and selfrecurrency, and these correlations were stronger in CoNNs than in random networks. CoNNs were more robust to neuronal loss, retaining their task performance and beneficial dynamical properties such as criticality and spectral radius better than random networks. Similarly, CoNNs were more robust to hyperparameter variations in both input and recurrent weight scaling. Using theoretical arguments and numerical simulations, we show that excess CoNN node self-recurrency is sufficient to explain this enhanced robustness. Overall, these results identify non-random features of connectome wiring that allow brains to reconcile extreme sparsity with reliable computation. SignificanceBrain networks support robust computation even though they operate under extreme wiring sparsity due to metabolic and spatial constraints. While sparse networks typically require fine-tuning and are sensitive to perturbations, we show that biological connectomes support specialised, efficient task engagement and remain robust to neuron loss and parameter variation. We identify excess neuronal selfrecurrency as a key structural feature underlying this stability. These results reveal how non-random connectivity stabilises computation in extremely sparse networks, providing principles for understanding brain function and designing robust, efficient artificial neural systems.

neuroscience↗

bioRxiv2025

Next-Generation Neural Mass Models Reproduce Features of Speech Processing

Segregation of speech into syllables is a key step in neural speech processing. It relies on the alignment of neural activity with the rhythmic structure of speech. Two competing hypotheses explain this neural speech tracking, phase-resetting and evoked responses. While phenomenological modelling of these hypotheses has been successful, we still lack understanding of the underlying cortical circuits. To investigate these mechanisms, we evaluate whether a biophysical next-generation neural mass model can reproduce several features of neural speech tracking, using phenomenological models of the competing hypotheses as algorithmic baselines. We investigate the models dynamics with four tests: recreating in-silico an EEG experiment that identified a correlation between tracking strength and phoneme sharpness, computing the Phase Concentration Metric, testing the effect of varying syllabic rates, and evaluating the Inter Event Phase Coherence across phoneme onsets. While all of the models that we study reproduce the sharpness-tuned rhythmic speech tracking, the evoked model requires a pre-processed acoustic edge impulse stimulus. We demonstrate that the neural mass model is performing thresholded phase-resetting triggered by sharp onsets in the continuous speech envelope. This produces cross-frequency nested oscillations that qualitatively match an experimentally-observed dual-peak signature in the Inter Event Phase Coherence. Our results indicate that the biophysical neural mass model provides a mechanistic bridge between generic oscillatory dynamics in cortical populations and the cognitive computations of speech tracking. Indeed, the non-linear dynamics of the neural mass model offer an explanation for how peak-rate event representations in auditory cortex activity arise in response to continuous acoustic input. Significance StatementSyllable segregation is crucial but challenging as natural speech lacks clear boundaries, yet humans perform this computation effortlessly. Speech aligns neural activity to syllabic rhythms, predicting syllable timing, but the underlying cortical mechanisms remain unknown. Relating this macroscopic behaviour to neurobiology is challenging; however, next-generation neural mass models promise to resolve this. We demonstrate that these models reproduce sharpness-tuned tracking and acoustic edge extraction. Dynamical analyses indicate this occurs through thresholded phase-resetting to phoneme onsets, triggering cross-frequency nested oscillations. Our results both advance biophysical understanding of syllable segregation and validate the models capacity for simulating macroscopic neural activity. These models offer a bridge between the neurobiology of the auditory cortex and speech processing dynamics that phenomenological models cannot provide.

neuroscience↗

bioRxiv2024

Cellular processing of beneficial de novo emerging proteins

Recent evidence demonstrates that eukaryotic genomes encode thousands of evolutionarily novel proteins that originate de novo from non-coding DNA and can contribute to species-specific adaptations. Yet, it remains unclear how these incipient proteins--whose sequences are entirely new to nature--navigate the cellular environment to bring about phenotypic change. Here, we conduct a systematic in vivo investigation of yeast de novo proteins with enhanced growth phenotypes, revealing the early stages of cellular integration. We find that these proteins are strongly enriched at the endoplasmic reticulum (ER) relative to conserved proteins, and that they integrate into cellular systems through conserved membrane targeting, trafficking, and degradation pathways. Despite having unrelated sequences, ER-localized de novo proteins share a common molecular signature: a C-terminal transmembrane domain that likely enables recognition by conserved post-translational ER insertion pathways. After insertion, ER-localized de novo proteins traffic from the ER and their homeostasis is regulated by conserved proteasomal and vacuolar degradation pathways. Our findings demonstrate that ancient targeting and degradation pathways can accommodate young de novo proteins sharing a convergent molecular signature. These pathways may act as selective filters, biasing which young de novo proteins persist. Significance StatementDeeply conserved genes shape the core structure and function of cells, but novel genes are key drivers of biodiversity. Novel genes can arise by divergence from ancient genes, or de novo from non-genic DNA. For de novo genes to develop novel functions, it is necessary for them to be processed by the cell so that their protein levels and localization are regulated. However, little is known about how novel de novo genes obtain the capacity to engage the cellular machinery needed for this regulation. Here, we experimentally assess how a set of endoplasmic reticulum (ER)-localized proteins encoded by recently-evolved de novo genes are localized and degraded in yeast cells. We discover that, despite having entirely unique amino acid sequences, these proteins share biochemical signatures allowing them to engage the same ancient cellular machinery and localize to the ER membrane. Interestingly though, this machinery is not the one that targets most ancient proteins to the ER. These results indicate that even recently emerged proteins without an extensive period of evolutionary adaptation can be recognized by specific ancient cellular pathways, facilitating their localization and homeostasis.

evolutionary biology↗

bioRxiv2024

Hierarchical Bayesian modeling of multi-region brain cell count data

We can now collect cell-count data across whole animal brains quantifying recent neuronal activity, gene expression, or anatomical connectivity. This is a powerful approach since it is a multi-region measurement, but because the imaging is done post-mortem, each animal only provides one set of counts. Experiments are expensive and since cells are counted by imaging and aligning a large number of brain sections, they are time-intensive. The resulting datasets tend to be under-sampled with fewer animals than brain regions. As a consequence, these data are a challenge for traditional statistical approaches. We present a standard partially-pooled Bayesian model for multi-region cell-count data and apply it to two example datasets. These examples demonstrate that hierarchical Bayesian methods are well suited to these data. In both cases the Bayesian model outperformed standard parallel t-tests. Overall, inference for cell-count data is substantially improved by the ability of the Bayesian approach to capture nested data and by its rigorous handling of uncertainty in under-sampled data.

neuroscience↗

bioRxiv2024

Golgi cells regulate timing and variability of information transfer in a cerebellar-behavioural loop

GlyT2-positive interneurons, Golgi and Lugaro cells, reside in the input layer of the cerebellar cortex in a key position to influence information processing. Here, we examine the contribution of GlyT2-positive interneurons to network dynamics in Crus 1 of mouse lateral cerebellar cortex during free whisking. We recorded neuronal population activity using NeuroPixels probes before and after chemogenetic downregulation of GlyT2-positive interneurons. Under resting conditions, cerebellar population activity reliably encoded whisker movements. Reductions in the activity of GlyT2-positive cells produced mild increases in neural activity which did not significantly impair these sensorimotor representations. However, reduced Golgi and Lugaro cell inhibition did increase the temporal alignment of local population network activity at the initiation of movement. These network alterations had variable impacts on behaviour, producing both increases and decreases in whisking velocity. Our results suggest that inhibition mediated by GlyT2-positive interneurons primarily governs the temporal patterning of population activity, which in turn is required to support downstream cerebellar dynamics and behavioural coordination. SIGNIFICANCE STATEMENTThe cerebellum has a simple and conserved structure which has tantalised neurobiologists wishing to understand its function. Here we look at the role of granule cell layer inhibitory interneurons, Golgi and Lugaro cells, in the cerebellar cortex. We selectively turned down the activity of these cells in the awake cerebellum to characterise their influence on network activity and behaviour. We show that downregulation of Golgi and Lugaro cells has very little influence on sensorimotor representations in the cerebellum (i.e., what is represented), but instead modulates the timing of cortical population activity (i.e., when information is represented). Our results indicate that inhibitory interneurons in the granule cell layer are necessary to appropriately pace changes in cerebellar activity to match ongoing behaviour.

neuroscience↗

bioRxiv2023

Cerebellar state estimation enables resilient coupling across behavioural domains

Cerebellar computations are necessary for fine behavioural control and are thought to rely on internal probabilistic models performing state estimation. We propose that the cerebellum infers how states contextualise (i.e., interact with) each other, and coordinates extra-cerebellar neuronal dynamics underpinning a range of behaviours. To support this claim, we describe a cerebellar model for state estimation that includes states interactions, and link the underlying inference with the neuronal architecture and dynamics observed empirically. This is formalised using the free energy principle, which provides a dual perspective on a system in terms of both the dynamics of its physical - in this case neuronal - states, and the inference process they entail. As a proof of principle, we simulate cerebellar-dependent synchronisation of whisking and respiration, which are known to be tightly coupled in rodents. In summary, we suggest that cerebellar-dependent contextualisation of behaviour can explain its ubiquitous involvement in most aspects of behaviour.

neuroscience↗

bioRxiv2022

A Robust, Compact and Diverse Population Code for Competing Sounds in Auditory Cortex

Cortical circuits encoding sensory information consist of populations of neurons, yet how information aggregates via pooling individual cells remains poorly understood. Such pooling may be particularly important in noisy settings where single neuron encoding is degraded. One example is the cocktail party problem, with competing sounds from multiple spatial locations. How populations of neurons in auditory cortex (ACx) code competing sounds have not been previously investigated. Here, we apply a novel information theoretic approach to estimate information in populations of neurons in ACx about competing sounds from multiple spatial locations, including both summed population (SP) and labeled line (LL) codes. We find that a small subset of neurons is sufficient to nearly maximize mutual information over different spatial configurations, with the LL code outperforming the SP code, and approaching information levels attained without noise. Moreover, with a LL code, units with diverse spatial responses, including both regular and narrow-spiking units, constitute the best pool. Finally, information in the population increases with spatial separation between target and masker, in correspondence with behavioral results on spatial release from masking in human and animals. Taken together, our results reveal that a compact and diverse population of neurons in ACx provide a robust code for competing sounds from different spatial locations.

neuroscience↗

bioRxiv2020

A Purkinje cell model that simulates complex spikes

Purkinje cells are the principal neurons of the cerebellar cortex. One of their distinguishing features is that they fire two distinct types of action potential, called simple and complex spikes, which interact with one another. Simple spikes are stereotypical action potentials that are elicited at high, but variable, rates (0 - 100 Hz) and have a consistent waveform. Complex spikes are composed of an initial action potential followed by a burst of lower amplitude spikelets. Complex spikes occur at comparatively low rates (~ 1 Hz) and have a variable waveform. Although they are critical to cerebellar operation a simple model that describes the complex spike waveform is lacking. Here, a novel single-compartment model of Purkinje cell electrodynamics is presented. The simpler version of this model, with two active conductances and a leak channel, can simulate the features typical of complex spikes recorded in vitro. If calcium dynamics are also included, the model can capture the pause in simple spike activity that occurs after complex spike events. Together, these models provide an insight into the mechanisms behind complex spike spikelet generation, waveform variability and their interactions with simple spike activity.

neuroscience↗

bioRxiv2020

Action-based organization and function of cerebellar cortical microcircuits

The cerebellum is the largest sensorimotor structure in the brain, but its mode of operation is not well understood. However, a fundamental organizational feature of the cerebellar cortex is division into elongated zones, defined by their inputs from specific parts of the inferior olive and Purkinje cell output to cerebellar and vestibular nuclei. Little is known about how the pattern of neuronal activity in zones, and their functional microcircuit subdivisions, microzones, is related to behaviour in awake animals. Here, we studied the organization of microzones within the C3 zone and their activity during a skilled forelimb reaching task in cats. Neurons in different parts of the C3 zone, functionally determined by receptive field characteristics, differed in their patterns of activity during movement. Our results suggest that the cerebellar C3 zone is organized and operates within an action-based frame of reference, with different microcircuits within the zone controlling specific muscle synergies.

neuroscience↗

bioRxiv2018

Calculating the Mutual Information Between Two Spike Trains

It is difficulty to estimate the mutual information between spike trains because established methods require more data than is usually available. Kozachenko-Leonenko estimators promise to solve this problem, but include a smoothing parameter which must be set. It is proposed here that the smoothing parameter can be selected by maximizing the estimated unbiased mutual information. This is tested on fictive data and shown to work very well.

neuroscience↗