Matthias H. Hennig

Early-Stage Waves in the Retinal Network Emerge Close to a Critical State Transition between Local and Global Functional Connectivity

A novel, biophysically realistic model for early-stage, acetylcholine-mediated retinal waves is presented. In this model, neural excitability is regulated through a slow after-hyperpolarization (sAHP) operating on two different temporal scales. As a result, the simulated network exhibits competition between a desynchronizing effect of spontaneous, cell-intrinsic bursts, and the synchronizing effect of synaptic transmission during retinal waves. Cell-intrinsic bursts decouple the retinal network through activation of the sAHP current, and we show that the network is capable of operating at a transition point between purely local and global functional connectedness, which corresponds to a percolation phase transition. Multielectrode array recordings show that, at this point, the properties of retinal waves are reliably predicted by the model. These results indicate that early spontaneous activity in the developing retina is regulated according to a very specific principle, which maximizes randomness and variability in the resulting activity patterns.

Acceleration of AMPA receptor kinetics underlies temperature-dependent changes in synaptic strength at the rat calyx of Held

It is well established that synaptic transmission declines at temperatures below physiological, but many in vitro studies are conducted at lower temperatures. Recent evidence suggests that temperature‐dependent changes in presynaptic mechanisms remain in overall equilibrium and have little effect on transmitter release at low transmission frequencies. Our objective was to examine the postsynaptic effects of temperature. Whole‐cell patch‐clamp recordings from principal neurons in the medial nucleus of the trapezoid body showed that a rise from 25°C to 35°C increased miniature EPSC (mEPSC) amplitude from −33 ± 2.3 to −46 ± 5.7 pA (n= 6) and accelerated mEPSC kinetics. Evoked EPSC amplitude increased from −3.14 ± 0.59 to −4.15 ± 0.73 nA with the fast decay time constant accelerating from 0.75 ± 0.09 ms at 25°C to 0.56 ± 0.08 ms at 35°C. Direct application of glutamate produced currents which similarly increased in amplitude from −0.76 ± 0.10 nA at 25°C to −1.11 ± 0.19 nA 35°C. Kinetic modelling of fast AMPA receptors showed that a temperature‐dependent scaling of all reaction rate constants by a single multiplicative factor (Q10= 2.4) drives AMPA channels with multiple subconductances into the higher‐conducting states at higher temperature. Furthermore, Monte Carlo simulation and deconvolution analysis of transmission at the calyx of Held showed that this acceleration of the receptor kinetics explained the temperature dependence of both the mEPSC and evoked EPSC. We propose that acceleration in postsynaptic AMPA receptor kinetics, rather than altered presynaptic release, is the primary mechanism by which temperature changes alter synaptic responses at low frequencies.

Following the ontogeny of retinal waves: pan‐retinal recordings of population dynamics in the neonatal mouse

Novel pan‐retinal recordings of mouse retinal waves were obtained at near cellular resolution using a large‐scale, high‐density array of 4096 electrodes to investigate changes in wave spatiotemporal properties from postnatal day 2 to eye opening. Early cholinergic waves are large, slow and random, with low cellular recruitment. A developmental shift in GABAA signalling from depolarizing to hyperpolarizing influences the dynamics of cholinergic waves. Glutamatergic waves that occur just before eye opening are focused, faster, denser, non‐random and repetitive. These results provide a new, deeper understanding of developmental changes in retinal spontaneous activity patterns, which will help researchers in the investigation of the role of early retinal activity during wiring of the visual system.

Theoretical models of synaptic short term plasticity

Short term plasticity is a highly abundant form of rapid, activity-dependent modulation of synaptic efficacy. A shared set of mechanisms can cause both depression and enhancement of the postsynaptic response at different synapses, with important consequences for information processing. Mathematical models have been extensively used to study the mechanisms and roles of short term plasticity. This review provides an overview of existing models and their biological basis, and of their main properties. Special attention will be given to slow processes such as calcium channel inactivation and the effect of activation of presynaptic autoreceptors.

Stochastic resonance in visual cortical neurons: does the eye-tremor actually improve visual acuity?

Abstract We demonstrate with electrophysiological recordings that visual cortical cell responses to moving stimuli with very small amplitudes can be enhanced by adding a small amount of noise to the motion pattern of the stimulus. This situation mimics the micro-movements of the eye during fixation and shows that these movements could enhance the performance of the cells. In a biophysically realistic model we show in addition, that micro-movements can be used to enhance the visual resolution of the cortical cells by means of spatiotemporal integration. This mechanism could partly underlie the hyperacuity properties of the visual system.

Synaptic and nonsynaptic plasticity approximating probabilistic inference

Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert.

Interactions between multiple sources of short-term plasticity during evoked and spontaneous activity at the rat calyx of Held

Sustained activity at most central synapses is accompanied by a number of short‐term changes in synaptic strength which act over a range of time scales. Here we examine experimental data and develop a model of synaptic depression at the calyx of Held synaptic terminal that combines many of these mechanisms (acting at differing sites and across a range of time scales). This new model incorporates vesicle recycling, facilitation, activity‐dependent vesicle retrieval and multiple mechanisms affecting calcium channel activity and release probability. It can accurately reproduce the time course of experimentally measured short‐term depression across different stimulus frequencies and exhibits a slow decay in EPSC amplitude during sustained stimulation. We show that the slow decay is a consequence of vesicle release inhibition by multiple mechanisms and is accompanied by a partial recovery of the releasable vesicle pool. This prediction is supported by patch‐clamp data, using long duration repetitive EPSC stimulation at up to 400 Hz. The model also explains the recovery from depression in terms of interaction between these multiple processes, which together generate a stimulus‐history‐dependent recovery after repetitive stimulation. Given the high rates of spontaneous activity in the auditory pathway, the model also demonstrates how these multiple interactions cause chronic synaptic depression under in vivo conditions. While the magnitude of the depression converges to the same steady state for a given frequency, the time courses of onset and recovery are faster in the presence of spontaneous activity. We conclude that interactions between multiple sources of short‐term plasticity can account for the complex kinetics during high frequency stimulation and cause stimulus‐history‐dependent recovery at this relay synapse.

Low‐voltage activated Kv1.1 subunits are crucial for the processing of sound source location in the lateral superior olive in mice

Non‐technical summary  Voltage‐gated potassium channels control excitability throughout the nervous system and their dysfunction (or mutation) is associated with epilepsy and movement disorders. Loss of the insulating myelin sheath around nerve fibres (axons) in multiple sclerosis causes transmission failure by exposing too many potassium channels. We show that too few potassium channels also causes errors in information transmission as measured by the ability to localize the source of a sound, and suggests a general role for potassium channels along myelinated nerve fibres. These results give insights into normal neuronal function and into neurodegenerative disease mechanisms for patients with ataxia and multiple sclerosis.

Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays

An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data and the dense sampling of spikes with closely spaced electrodes. Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data, was used to test and validate these methods. We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio (SNR) through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units. Overall, we show how the improved spatial resolution provided by high density, large scale MEAs can be reliably exploited to characterize activity from large neural populations and brain circuits.

Sloppiness in Spontaneously Active Neuronal Networks

Various plasticity mechanisms, including experience-dependent, spontaneous, as well as homeostatic ones, continuously remodel neural circuits. Yet, despite fluctuations in the properties of single neurons and synapses, the behavior and function of neuronal assemblies are generally found to be very stable over time. This raises the important question of how plasticity is coordinated across the network. To address this, we investigated the stability of network activity in cultured rat hippocampal neurons recorded with high-density multielectrode arrays over several days. We used parametric models to characterize multineuron activity patterns and analyzed their sensitivity to changes. We found that the models exhibited sloppiness, a property where the model behavior is insensitive to changes in many parameter combinations, but very sensitive to a few. The activity of neurons with sloppy parameters showed faster and larger fluctuations than the activity of a small subset of neurons associated with sensitive parameters. Furthermore, parameter sensitivity was highly correlated with firing rates. Finally, we tested our observations from cell cultures on an in vivo recording from monkey visual cortex and we confirm that spontaneous cortical activity also shows hallmarks of sloppy behavior and firing rate dependence. Our findings suggest that a small subnetwork of highly active and stable neurons supports group stability, and that this endows neuronal networks with the flexibility to continuously remodel without compromising stability and function.