Biology subjects

Schmuker, M.

Publications and source records attributed to Schmuker, M..

5 recordsLinked to original sources

bioRxiv2024

Decoding the amplitude and slope of continuous signals into spikes with a spiking point neuron model

Scalable and efficient neural models with biological plausibility are crucial for real-time applications in neuromorphic hardware and robotics. While biophysically detailed compartmental models are valuable for their accuracy, their complexity and computational cost limit real-world application on currently available hardware. In this study, we harness the signal processing potential of the Izhikevich point neuron model to decode slope and amplitude, both important dynamical features of sensory signals. Results demonstrate that our slope-detector effectively signals up-stokes of naturalistic input signals across a wide range of frequencies. It exhibits bidirectional slope detection, with burst duration encoding slope magnitude in a graded manner. We compare to a biophysically detailed two-compartment pyramidal neuron model, showing that our bursting slope-detector performs similarly. We then demonstrate that our slope-detector does not need to burst, improving efficiency and producing more precise, discrete output by signalling events with single spikes. This makes it well-suited for real-time robotics control systems or neuromorphic hardware applications, offering greater efficiency and enabling large-scale simulations using the same computational power.

neuroscience↗

bioRxiv2022

Mapping odorant sensitivities reveals a sparse but structured representation of olfactory chemical space by sensory input to the mouse olfactory bulb.

In olfactory systems, convergence of sensory neurons onto glomeruli generates a map of odorant receptor identity. How glomerular maps relate to sensory space remains unclear. We sought to better characterize this relationship in the mouse olfactory system by defining glomeruli in terms of the odorants to which they are most sensitive. Using high-throughput odorant delivery and ultrasensitive imaging of sensory inputs, we imaged responses to 185 odorants presented at concentrations determined to activate only one or a few glomeruli across the dorsal olfactory bulb. The resulting datasets defined the tuning properties of glomeruli - and, by inference, their cognate odorant receptors - in a low-concentration regime, and yielded consensus maps of glomerular sensitivity across a wide range of chemical space. Glomeruli were extremely narrowly tuned, with ~25% responding to only one odorant, and extremely sensitive, responding to their effective odorants at sub-picomolar to nanomolar concentrations. Such narrow tuning in this concentration regime allowed for reliable functional identification of many glomeruli based on a single diagnostic odorant. At the same time, the response spectra of glomeruli responding to multiple odorants was best predicted by straightforward odorant structural features, and glomeruli sensitive to distinct odorants with common structural features were spatially clustered. These results define an underlying structure to the primary representation of sensory space by the mouse olfactory system.

neuroscience↗

bioRxiv2019

Growth Rules for the Repair of Asynchronous Irregular Neuronal Networks after Peripheral Lesions

Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprout or retract to restore network activity levels. We developed a new spiking network model, simulations of which accurately reproduce network rewiring after peripheral lesions as reported in experiments, to study these activity dependent growth regimes of neurites. To ensure that our simulations closely resemble the behaviour of networks in the brain, we deafferent a biologically realistic network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex. Our simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. Furthermore, in our model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Lastly, we observe that our proposed model of homeostatic structural plasticity and the inhibitory synaptic plasticity mechanism that also balances our AI network are both necessary for successful rewiring of the network. Author summaryAn accumulating body of evidence suggests that our brain can compensate for peripheral lesions by adaptive rewiring of its neuronal circuitry. The underlying process, structural plasticity, can modify the connectivity of neuronal networks in the brain, thus affecting their function. To better understand the mechanisms of structural plasticity in the brain, we have developed a novel model of peripheral lesions and the resulting activity-dependent rewiring in a simplified cortical network model that exhibits biologically realistic asynchronous irregular activity. In order to accurately reproduce the directionality and time course of rewiring after injury that is observed in peripheral lesion experiments, we derive activity dependent growth rules for different synaptic elements: dendritic and axonal contacts. Our simulation results suggest that excitatory and inhibitory synaptic elements have to react to changes in neuronal activity in opposite ways. We show that these rules result in a homeostatic stabilisation of activity in individual neurons. In our simulations, both synaptic and structural plasticity mechanisms are necessary for network repair. Furthermore, our simulations indicate that while activity is restored in neurons deprived by the peripheral lesion, the temporal firing characteristics of the network can be changed by the rewiring process.

neuroscience↗

bioRxiv2018

Computational exploration of molecular receptive fields in the olfactory bulb reveals a glomerulus-centric chemical map.

Progress in olfactory research is currently hampered by incomplete knowledge about chemical receptive ranges of primary receptors. Moreover, the chemical logic underlying the arrangement of computational units in the olfactory bulb has still not been resolved. We undertook a large-scale approach at characterising molecular receptive ranges (MRRs) of glomeruli innervated by the MOR18-2 olfactory receptor in the dorsal olfactory bulb (dOB). Guided by an iterative approach that combined biological screening and machine learning, we selected 214 odorants to characterise the response of MOR18-2 and its neighbouring glomeruli. We discovered several previously unknown odorants activating MOR18-2 glomeruli, and we obtained detailed MRRs of MOR18-2 glomeruli and their neighbours. Physico-chemical MRR descriptions revealed that the spatial layout of glomeruli followed a chemical logic. Our results confirm earlier findings that demonstrate a partial chemical map underlying glomerular arrangement in the dOB. Moreover, our novel methodology that combines machine learning and physiological measurements lights the way towards future high-throughput studies to deorphanise and characterise structure-activity relationships in olfaction.

neuroscience↗

bioRxiv2018

Sparse Coding with a Somato-Dendritic Rule

Cortical neurons are silent most of the time. This sparse activity is energy efficient, and the resulting neural code has favourable properties for associative learning. Most neural models of sparse coding use some form of homeostasis to ensure that each neuron fires infrequently. But homeostatic plasticity acting on a fast timescale may not be biologically plausible, and could lead to catastrophic forgetting in embodied agents that learn continuously. We set out to explore whether inhibitory plasticity could play that role instead, regulating both the population sparseness and the average firing rates. We put the idea to the test in a hybrid network where rate-based dendritic compartments integrate the feedforward input, while spiking somas compete through recurrent inhibition. A somato-dendritic learning rule allows somatic inhibition to modulate nonlinear Hebbian learning in the dendrites. Trained on MNIST digits and natural images, the network discovers independent components that form a sparse encoding of the input and support linear decoding. These findings con-firm that intrinsic plasticity is not strictly required for regulating sparseness: inhibitory plasticity can have the same effect, although that mechanism comes with its own stability-plasticity dilemma. Going beyond point neuron models, the network illustrates how a learning rule can make use of dendrites and compartmentalised inputs; it also suggests a functional interpretation for clustered somatic inhibition in cortical neurons.

neuroscience↗