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Cross-frequency coupling (CFC) has been proposed to coordinate neural dynamics across spatial and temporal scales. Despite its potential relevance for understanding healthy and pathological brain function, the standard CFC analysis and physiological interpretation come with fundamental problems. For example, apparent CFC can appear because of spectral correlations due to common non-stationarities that may arise in the total absence of interactions between neural frequency components. To provide a road map towards an improved mechanistic understanding of CFC, we organize the available and potential novel statistical/modeling approaches according to their biophysical interpretability. While we do not provide solutions for all the problems described, we provide a list of practical recommendations to avoid common errors and to enhance the interpretability of CFC analysis.
The way in which dendrites spread within neural tissue determines the resulting circuit connectivity and computation. However, a general theory describing the dynamics of this growth process does not exist. Here we obtain the first time-lapse reconstructions of neurons in living fly larvae over the entirety of their developmental stages. We show that these neurons expand in a remarkably regular stretching process that conserves their shape. Newly available space is filled optimally, a direct consequence of constraining the total amount of dendritic cable. We derive a mathematical model that predicts one time point from the previous and use this model to predict dendrite morphology of other cell types and species. In summary, we formulate a novel theory of dendrite growth based on detailed developmental experimental data that optimises wiring and space filling and serves as a basis to better understand aspects of coverage and connectivity for neural circuit formation.
Developmental loss of ErbB4 in PV interneurons disrupts state-dependent cortical circuit dynamics
(2020)
GABAergic inhibition plays an important role in the establishment and maintenance of cortical circuits during development. Neuregulin 1 (Nrg1) and its interneuron-specific receptor ErbB4 are key elements of a signaling pathway critical for the maturation and proper synaptic connectivity of interneurons. Using conditional deletions of the ERBB4 gene in mice, we tested the role of this signaling pathway at two developmental timepoints in parvalbumin-expressing (PV) interneurons, the largest subpopulation of cortical GABAergic cells. Loss of ErbB4 in PV interneurons during embryonic, but not late postnatal, development leads to alterations in the activity of excitatory and inhibitory cortical neurons, along with severe disruption of cortical temporal organization. These impairments emerge by the end of the second postnatal week, prior to the complete maturation of the PV interneurons themselves. Early loss of ErbB4 in PV interneurons also results in profound dysregulation of excitatory pyramidal neuron dendritic architecture and a redistribution of spine density at the apical dendritic tuft. In association with these deficits, excitatory cortical neurons exhibit normal tuning for sensory inputs, but a loss of state-dependent modulation of the gain of sensory responses. Together these data support a key role for early developmental Nrg1/ErbB4 signaling in PV interneurons as powerful mechanism underlying the maturation of both the inhibitory and excitatory components of cortical circuits.
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 neuron’s 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.
Excess neuronal branching allows for innervation of specific dendritic compartments in cortex
(2019)
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.
Spike count correlations (SCCs) are ubiquitous in sensory cortices, are characterized by rich structure and arise from structured internal interactions. Yet, most theories of visual perception focus exclusively on the mean responses of individual neurons. Here, we argue that feedback interactions in primary visual cortex (V1) establish the context in which individual neurons process complex stimuli and that changes in visual context give rise to stimulus-dependent SCCs. Measuring V1 population responses to natural scenes in behaving macaques, we show that the fine structure of SCCs is stimulus-specific and variations in response correlations across-stimuli are independent of variations in response means. Moreover, we demonstrate that stimulus-specificity of SCCs in V1 can be directly manipulated by controlling the high-order structure of synthetic stimuli. We propose that stimulus-specificity of SCCs is a natural consequence of hierarchical inference where inferences on the presence of high-level image features modulate inferences on the presence of low-level features.
Achieving functional neuronal dendrite structure through sequential stochastic growth and retraction
(2020)
Class I ventral posterior dendritic arborisation (c1vpda) proprioceptive sensory neurons respond to contractions in the Drosophila larval body wall during crawling. Their dendritic branches run along the direction of contraction, possibly a functional requirement to maximise membrane curvature during crawling contractions. Although the molecular machinery of dendritic patterning in c1vpda has been extensively studied, the process leading to the precise elaboration of their comb-like shapes remains elusive. Here, to link dendrite shape with its proprioceptive role, we performed long-term, non-invasive, in vivo time-lapse imaging of c1vpda embryonic and larval morphogenesis to reveal a sequence of differentiation stages. We combined computer models and dendritic branch dynamics tracking to propose that distinct sequential phases of targeted growth and stochastic retraction achieve efficient dendritic trees both in terms of wire and function. Our study shows how dendrite growth balances structure–function requirements, shedding new light on general principles of self-organisation in functionally specialised dendrites.
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 neuron’s 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 neuron’s 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 neuron’s 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.
Human language relies on hierarchically structured syntax to facilitate efficient and robust communication. The correct processing of syntactic information is essential for successful communication between speakers. As an abstract level of language, syntax has often been studied separately from the physical form of the speech signal, thus often masking the interactions that can promote better syntactic processing in the human brain. We analyzed a MEG dataset to investigate how acoustic cues, specifically prosody, interact with syntactic operations. We examined whether prosody enhances the cortical encoding of syntactic representations. We decoded left-sided dependencies directly from brain activity and evaluated possible modulations of the decoding by the presence of prosodic boundaries. Our findings demonstrate that prosodic boundary presence improves the representation of left-sided dependencies, indicating the facilitative role of prosodic cues in processing abstract linguistic features. This study gives neurobiological evidence for the boosting of syntactic processing via interaction with prosody.
The pitfalls of measuring representational similarity using representational similarity analysis
(2022)
A core challenge in neuroscience is to assess whether diverse systems represent the world similarly. Representational Similarity Analysis (RSA) is an innovative approach to address this problem and has become increasingly popular across disciplines from machine learning to computational neuroscience. Despite these successes, RSA regularly uncovers difficult-to-reconcile and contradictory findings. Here we demonstrate the pitfalls of using RSA to infer representational similarity and explain how contradictory findings arise and support false inferences when left unchecked. By comparing neural representations in primate, human and computational models, we reveal two problematic phenomena that are ubiquitous in current research: a “mimic” effect, where confounds in stimuli can lead to high RSA scores between provably dissimilar systems, and a “modulation effect”, where RSA-scores become dependent on stimuli used for testing. Since our results bear on existing findings and inferences, we provide recommendations to avoid these pitfalls and sketch a way forward.