Biology subjects

Bird, A. D.

Publications and source records attributed to Bird, A. D..

8 recordsLinked to original sources

bioRxiv2026

An energy-dependent integrate-and-fire model explains coordinated synaptic and intrinsic adaptation under metabolic stress

Neuronal computation is constrained by the energetic cost of maintaining synaptic transmission, ionic gradients and action-potential generation. Prior work has shown that food restriction in mice shifts visual cortical neurons toward a different operating regime characterized by reduced excitatory synaptic drive, increased membrane resistance and a more depolarized resting membrane potential. This preserves neuronal firing rates, but comes at the cost of reduced coding precision. Deriving an improved estimate of neuronal energy consumption, we have recently shown that these adaptations reflect an optimal trade-off between neuronal information transmission and energy consumption. However, it remains unclear through which control strategies individual neurons adapt their biophysical parameters to maintain a favorable energy-information trade-off. Here we consider several candidate models implementing different control strategies. A "synapses-first" model, in which metabolic stress drives a downscaling of synaptic efficacies to save energy, while an intrinsic plasticity mechanism increases excitability to preserve the average firing rate, successfully reproduces key experimental observations. Alternative models in which energy shortages drive changes in intrinsic excitability or firing-rate variability can also reproduce the endpoint of neuronal adaptation, but predict different time courses and exhibit less energy-efficient and less stable dynamics. New experiments are needed to distinguish among these competing models.

neuroscience↗

bioRxiv2026

Hippocampal granule cells balance energy and function along the Pareto optimal front

The dentate gyrus' principal neuron, the granule cell (GC), plays a key role in pattern separation, transforming similar inputs into dissimilar outputs. We used a new measure based on information theory to evaluate this transformation in an experimentally derived in silico GC model with realistic ion channel distributions across its dendrites. We found that model performance aligned well with current-clamp recordings from mouse brain slices. However, it was also possible to find artificial parameter combinations in the model that functionally outperformed the recorded cells while matching their action potential shapes. Rather than maximising computational power, the biologically realistic GC model balanced energy efficiency against increased functionality in a Pareto optimal way: No possible parameter combination was found to improve one of the objectives without impairing the other. Interestingly, immature GCs adopted a different weighted trade-off with a stronger emphasis on energy efficiency than pattern separation, but they remained Pareto optimal over their putative developmental trajectory. Our results support the idea that evolution favours neurons that can satisfy functional and energetic demands simultaneously.

neuroscience↗

bioRxiv2026

Neuronal degeneracy reflects context-specific trade-offs between energy and information

Among neurons of the same type, the different electrophysiological parameters vary drastically. It is presently unclear if there is any hidden structure in this diversity and how it relates to neuronal function. One potential source of structure is the evolutionary pressure that has sculpted brains to work in an energy-efficient manner. Here we show that neurons parameters do not vary randomly, but that neurons populate a manifold within their parameter space permitting energy-efficient signaling. This manifold represents a Pareto front of degenerate solutions to the problem of energy-efficiency and explains the prevalence of low to intermediate firing rates (2 - 5 Hz) in neocortex. Furthermore, we show that neurons in different sensory brain areas populate different manifolds and that food restriction induces systematic shifts of neuronal parameters along this manifold. Our results suggest that the large diversity of neuronal properties is actually tightly regulated to ensure energy-efficient signaling in different contexts. In briefWe show that there is hidden structure in the diversity of electrophysiological properties of neurons that is governed by trade-offs between energy consumption and information transmission. HighlightsO_LIWe derive an updated neuronal energy budget for a large class of neuron models. C_LIO_LIComputational modeling predicts a manifold of degenerate solutions to energy-efficient information coding forming a Pareto front in the neuronal parameter space. C_LIO_LINeurons from visual and somatosensory cortex fall on the predicted manifold. C_LIO_LIOptimal energy-efficiency is achieved for intermediate-low firing rates (2 - 5 Hz) as observed in neocortex. C_LIO_LIWe observe an energy-efficient form of non-multiplicative synaptic scaling during food deprivation. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=164 SRC="FIGDIR/small/708507v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@ca6d36org.highwire.dtl.DTLVardef@1c81ca3org.highwire.dtl.DTLVardef@d03833org.highwire.dtl.DTLVardef@19bc7c_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Topology recapitulates ontogeny of dendritic arbors

Branching of dendrites and axons allows neurons to make synaptic contacts with large numbers of other neurons, facilitating the high connectivity of the nervous system. Neurons have geometric properties, such as the lengths and diameters of their branches, that change systematically throughout the arbor in ways that are thought to minimize construction costs and to optimize the transmission of electrical signals and the intracellular transport of materials. In this work, we investigated whether neuronal arbors also have topological properties that reflect the growth and/or functional properties of their dendritic arbors. In our efforts to uncover possible topological rules, we discovered a function that depends only on the topology of bifurcating trees such as dendritic arbors: the tip-support distribution, which is the average number of branches that support n dendrite tips. We found that for many, but not all, neurons from a wide range of invertebrate and vertebrate species, the tip-support distribution follows a power law with slopes ranging from -1.4 and -1.8 on a log-log plot. The slope is invariant under iterative trimming of terminal branches and under random ablation of internal branches. We found that power laws with similar slopes emerge from a variety of iterative growth processes including the Galton-Watson (GW) process, where the power-law behavior occurs after the percolation threshold. Through simulation, we show the slope of the power-law increases with the branching probability of a GW process, which corresponds to a more regular tree. Furthermore, the inclusion of postsynaptic spines and other terminal processes on branches causes a characteristic deviation of the tip-support distribution from a power law. Therefore, the tip-support function is a topological property that reflects the underlying branching morphogenesis of dendritic trees.

biophysics↗

bioRxiv2022

Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus

Pattern separation is a valuable computational function performed by neuronal circuits, such as the dentate gyrus, where dissimilarity between inputs is increased, reducing noise and increasing the storage capacity of downstream networks. Pattern separation is studied from both in vivo experimental and computational perspectives and, a number of different measures (such as orthogonalisation, decorrelation, or spike train distance) have been applied to quantify the process of pattern separation. However, these are known to give conclusions that can differ qualitatively depending on the choice of measure and the parameters used to calculate it. We here demonstrate that arbitrarily increasing sparsity, a noticeable feature of dentate granule cell firing and one that is believed to be key to pattern separation, typically leads to improved classical measures for pattern separation even, inappropriately, up to the point where almost all information about the inputs is lost. Standard measures therefore both cannot differentiate between pattern separation and pattern destruction, and give results that may depend on arbitrary parameter choices. We propose that techniques from information theory, in particular mutual information, transfer entropy, and redundancy, should be applied to penalise the potential for lost information (often due to increased sparsity) that is neglected by existing measures. We compare five commonly-used measures of pattern separation with three novel techniques based on information theory, showing that the latter can be applied in a principled way and provide a robust and reliable measure for comparing the pattern separation performance of different neurons and networks. We demonstrate our new measures on detailed compartmental models of individual dentate granule cells and a dentate microcircuit, and show how structural changes associated with epilepsy affect pattern separation performance. We also demonstrate how our measures of pattern separation can predict pattern completion accuracy. Overall, our measures solve a widely acknowledged problem in assessing the pattern separation of neural circuits such as the dentate gyrus, as well as the cerebellum and mushroom body. Finally we provide a publicly available toolbox allowing for easy analysis of pattern separation in spike train ensembles. Author summaryThe hippocampus is a region of the brain strongly associated with spatial navigation and encoding of episodic memories. To perform these functions effectively it makes use of circuits that perform pattern separation, where redundant structure is removed from neural representations leaving only the most salient information. Pattern separation allows downstream pattern completion networks to better distinguish between similar situations. Pathological changes, caused by Alzheimers, schizophrenia, or epilepsy, to the circuits that perform pattern separation are associated with reduced discriminative ability in both animal models and humans. Traditionally, pattern separation has been described alongside the complementary process of pattern completion, but more recent studies have focussed on the detailed neuronal and circuit features that contribute to pattern separation alone. We here show that traditional measures of pattern separation are inappropriate in this case, as they do not give consistent conclusions when parameters are changed and can confound pattern separation with the loss of important information. We show that directly accounting for the information throughput of a pattern separation circuit can provide new measures of pattern separation that are robust and consistent, and allow for nuanced analysis of the structure-function relationship of such circuits and how this may be perturbed by pathology.

neuroscience↗

bioRxiv2020

Dendritic normalisation improves learning in sparsely connected artificial neural networks

Inspired by the physiology of neuronal systems in the brain, artificial neural networks have become an invaluable tool for machine learning applications. However, their biological realism and theoretical tractability are limited, resulting in poorly understood parameters. We have recently shown that biological neuronal firing rates in response to distributed inputs are largely independent of size, meaning that neurons are typically responsive to the proportion, not the absolute number, of their inputs that are active. Here we introduce such a normalisation, where the strength of a neurons afferents is divided by their number, to various sparsely-connected artificial networks. The learning performance is dramatically increased, providing an improvement over other widely-used normalisations in sparse networks. The resulting machine learning tools are universally applicable and biologically inspired, rendering them better understood and more stable in our tests.

neuroscience↗

bioRxiv2019

A general principle of dendritic constancy – a neuron’s size and shape invariant excitability

Reducing neuronal size results in less cell membrane and therefore lower input conductance. Smaller neurons are thus more excitable as seen in their voltage responses to current injections in the soma. However, the impact of a neurons size and shape on its voltage responses to synaptic activation in dendrites is much less understood. Here we use analytical cable theory to predict voltage responses to distributed synaptic inputs and show that these are entirely independent of dendritic length. For a given synaptic density, a neurons response depends only on the average dendritic diameter and its intrinsic conductivity. These results remain true for the entire range of possible dendritic morphologies irrespective of any particular arborisation complexity. Also, spiking models result in morphology invariant numbers of action potentials that encode the percentage of active synapses. Interestingly, in contrast to spike rate, spike times do depend on dendrite morphology. In summary, a neurons excitability in response to synaptic inputs is not affected by total dendrite length. It rather provides a homeostatic input-output relation that specialised synapse distributions, local non-linearities in the dendrites and synaptic plasticity can modulate. Our work reveals a new fundamental principle of dendritic constancy that has consequences for the overall computation in neural circuits.\n\nIn briefWe show that realistic neuron models essentially collapse to point neurons when stimulated by randomly distributed inputs instead of by single synapses or current injection in the soma.\n\nHighlightsO_LIA simple equation that predicts voltage in response to distributed synaptic inputs.\nC_LIO_LIResponses to distributed and clustered inputs are largely independent of dendritic length.\nC_LIO_LISpike rates in various Hodgkin Huxley (HH) like or Leaky Integrate-and-Fire (LIF) models are largely independent of morphology.\nC_LIO_LIPrecise spike timing (firing pattern) depends on dendritic morphology.\nC_LIO_LINeuroMorpho.Org database-wide analysis of the relation between dendritic morphology and electrophysiology.\nC_LIO_LIOur equations set precise input-output relations in realistic dendrite models.\nC_LI

neuroscience↗

bioRxiv2019

Excess neuronal branching allows for innervation of specific dendritic compartments in cortex

The connectivity of cortical microcircuits is a major determinant of brain function; defining how activity propagates between different cell types is key to scaling our understanding of individual neuronal behaviour to encompass functional networks. Furthermore, the integration of synaptic currents within a dendrite depends on the spatial organisation of inputs, both excitatory and inhibitory. We identify a simple equation to estimate the number of potential anatomical contacts between neurons; finding a linear increase in potential connectivity with cable length and maximum spine length, and a decrease with overlapping volume. This enables us to predict the mean number of candidate synapses for reconstructed cells, including those realistically arranged. We identify an excess of putative connections in cortical data, with densities of neurite higher than is necessary to reliably ensure the possible implementation of any given connection. We show that potential contacts allow the particular implementation of connectivity at a subcellular level.

neuroscience↗