Mark C. W. van Rossum

Activity Coregulates Quantal AMPA and NMDA Currents at Neocortical Synapses

AMPA and NMDA receptors are coexpressed at many central synapses, but the factors that control the ratio of these two receptors are not well understood. We recorded mixed miniature or evoked synaptic currents arising from coactivation of AMPA and NMDA receptors and found that long-lasting changes in activity scaled both currents up and down proportionally through changes in the number of postsynaptic receptors. The ratio of NMDA to AMPA current was similar at different synapses onto the same neuron, and this relationship was preserved following activity-dependent synaptic scaling. These data show that AMPA and NMDA receptors are tightly coregulated by activity at synapses at which they are both expressed and suggest that a mechanism exists to actively maintain a constant receptor ratio across a neurons synapses.

Feedback Inhibition Enables Theta-Nested Gamma Oscillations and Grid Firing Fields

Cortical circuits are thought to multiplex firing rate codes with temporal codes that rely on oscillatory network activity, but the circuit mechanisms that combine these coding schemes are unclear. We establish with optogenetic activation of layer II of the medial entorhinal cortex that theta frequency drive to this circuit is sufficient to generate nested gamma frequency oscillations in synaptic activity. These nested gamma oscillations closely resemble activity during spatial exploration, are generated by local feedback inhibition without recurrent excitation, and have clock-like features suitable as reference signals for multiplexing temporal codes within rate-coded grid firing fields. In network models deduced from our data, feedback inhibition supports coexistence of theta-nested gamma oscillations with attractor states that generate grid firing fields. These results indicate that grid cells communicate primarily via inhibitory interneurons. This circuit mechanism enables multiplexing of oscillation-based temporal codes with rate-coded attractor states.

Spike-timing-dependent plasticity: Common themes and divergent vistas

Abstract. Recent experimental observations of spike-timing-dependent synaptic plasticity (STDP) have revitalized the study of synaptic learning rules. The most surprising aspect of these experiments lies in the observation that synapses activated shortly after the occurrence of a postsynaptic spike are weakened. Thus, synaptic plasticity is sensitive to the temporal ordering of pre- and postsynaptic activation. This temporal asymmetry has been suggested to underlie a range of learning tasks. In the first part of this review we highlight some of the common themes from a range of findings in the framework of predictive coding. As an example of how this principle can be used in a learning task, we discuss a recent model of cortical map formation. In the second part of the review, we point out some of the differences in STDP models and their functional consequences. We discuss how differences in the weight-dependence, the time-constants and the non-linear properties of learning rules give rise to distinct computational functions. In light of these computational issues raised, we review current experimental findings and suggest further experiments to resolve some controversies.

State Based Model of Long-Term Potentiation and Synaptic Tagging and Capture

Recent data indicate that plasticity protocols have not only synapse-specific but also more widespread effects. In particular, in synaptic tagging and capture (STC), tagged synapses can capture plasticity-related proteins, synthesized in response to strong stimulation of other synapses. This leads to long-lasting modification of only weakly stimulated synapses. Here we present a biophysical model of synaptic plasticity in the hippocampus that incorporates several key results from experiments on STC. The model specifies a set of physical states in which a synapse can exist, together with transition rates that are affected by high- and low-frequency stimulation protocols. In contrast to most standard plasticity models, the model exhibits both early- and late-phase LTP/D, de-potentiation, and STC. As such, it provides a useful starting point for further theoretical work on the role of STC in learning and memory.

Noise removal at the rod synapse of mammalian retina

Mammalian rods respond to single photons with a hyperpolarization of about 1 mV which is accompanied by continuous noise. Since the mammalian rod bipolar cell collects signals from 20-100 rods, the noise from the converging rods would overwhelm the single-photon signal from one rod at scotopic intensities (starlight) if the bipolar cell summed signals linearly (Baylor et al., 1984). However, it is known that at scotopic intensities the retina preserves single-photon responses (Barlow et al., 1971; Mastronarde, 1983). To explore noise summation in the rod bipolar pathway, we simulated an array of rods synaptically connected to a rod bipolar cell using a compartmental model. The performance of the circuit was evaluated with a discriminator measuring errors in photon detection as false positives and false negatives, which were compared to physiologically and psychophysically measured error rates. When only one rod was connected to the rod bipolar, a Poisson rate of 80 vesicles/s was necessary for reliable transmission of the single-photon signal. When 25 rods converged through a linear synapse the noise caused an unacceptably high false positive rate, even when either dark continuous noise or synaptic noise where completely removed. We propose that a threshold nonlinearity is provided by the mGluR6 receptor in the rod bipolar dendrite (Shiells & Falk, 1994) to yield a synapse with a noise removing mechanism. With the threshold nonlinearity the synapse removed most of the noise. These results suggest that a threshold provided by the mGluR6 receptor in the rod bipolar cell is necessary for proper functioning of the retina at scotopic intensities and that the metabotropic domains in the rod bipolar are distinct. Such a nonlinear threshold could also reduce synaptic noise for cortical circuits in which sparse signals converge.

Homeostasis of intrinsic excitability in hippocampal neurones: dynamics and mechanism of the response to chronic depolarization

In order to maintain stable functionality in the face of continually changing input, neurones in the CNS must dynamically modulate their electrical characteristics. It has been hypothesized that in order to retain stable network function, neurones possess homeostatic mechanisms which integrate activity levels and alter network and cellular properties in such a way as to counter long‐term perturbations. Here we describe a simple model system where we investigate the effects of sustained neuronal depolarization, lasting up to several days, by exposing cultures of primary hippocampal pyramidal neurones to elevated concentrations (10–30 mm) of KCl. Following exposure to KCl, neurones exhibit lower input resistances and resting potentials, and require more current to be injected to evoke action potentials. This results in a rightward shift in the frequency‐input current (FI) curve which is explained by a simple linear model of the subthreshold I–V relationship. No changes are observed in action potential profiles, nor in the membrane potential at which action potentials are evoked. Furthermore, following depolarization, an increase in subthreshold potassium conductance is observed which is accounted for within a biophysical model of the subthreshold I–V characteristics of neuronal membranes. The FI curve shift was blocked by the presence of the L‐type Ca2+ channel blocker nifedipine, whilst antagonism of NMDA receptors did not interfere with the effect. Finally, changes in the intrinsic properties of neurones are reversible following removal of the depolarizing stimulus. We suggest that this experimental system provides a convenient model of homeostatic regulation of intrinsic excitability, and permits the study of temporal characteristics of homeostasis and its dependence on stimulus magnitude.

Systematic biases in early ERP and ERF components as a result of high-pass filtering

The event-related potential (ERP) and event-related field (ERF) techniques provide valuable insights into the time course of processes in the brain. Because neural signals are typically weak, researchers commonly filter the data to increase the signal-to-noise ratio. However, filtering may distort the data, leading to false results. Using our own EEG data, we show that acausal high-pass filtering can generate a systematic bias easily leading to misinterpretations of neural activity. In particular, we show that the early ERP component C1 is very sensitive to such effects. Moreover, we found that about half of the papers reporting modulations in the C1 range used a high-pass digital filter cut-off above the recommended maximum of 0.1 Hz. More generally, among 185 relevant ERP/ERF publications, 80 used cutoffs above 0.1 Hz. As a consequence, part of the ERP/ERF literature may need to be re-analyzed. We provide guidelines on how to minimize filtering artifacts.

Quantitative investigations of electrical nerve excitation treated as polarization: Louis Lapicque 1907 · Translated by:

“According to our current knowledge, a galvanic current can in organic tissue (a purely electrolytic conductor) only cause movement of ions, i.e. concentration changes, and nothing else. We conclude that these concentration changes must underlie the physiological effect. . . . It is well known that in organic tissue the composition of the aqueous solution that forms the electrolytic conductor is not everywhere the same. In particular, it is different on the inside and on the outside of the cells. The semi-permeable membranes prevent equilibration by diffusion. On these membranes, and there only, currents can lead to concentration

Memory retention and spike-timing-dependent plasticity.

Memory systems should be plastic to allow for learning; however, they should also retain earlier memories. Here we explore how synaptic weights and memories are retained in models of single neurons and networks equipped with spike-timing-dependent plasticity. We show that for single neuron models, the precise learning rule has a strong effect on the memory retention time. In particular, a soft-bound, weight-dependent learning rule has a very short retention time as compared with a learning rule that is independent of the synaptic weights. Next, we explore how the retention time is reflected in receptive field stability in networks. As in the single neuron case, the weight-dependent learning rule yields less stable receptive fields than a weight-independent rule. However, receptive fields stabilize in the presence of sufficient lateral inhibition, demonstrating that plasticity in networks can be regulated by inhibition and suggesting a novel role for inhibition in neural circuits.

Recurrent networks with short term synaptic depression

Cortical circuitry shows an abundance of recurrent connections. A widely used model that relies on recurrence is the ring attractor network, which has been used to describe phenomena as diverse as working memory, visual processing and head direction cells. Commonly, the synapses in these models are static. Here, we examine the behaviour of ring attractor networks when the recurrent connections are subject to short term synaptic depression, as observed in many brain regions. We find that in the presence of a uniform background current, the network activity can be in either of three states: a stationary attractor state, a uniform state, or a rotating attractor state. The rotation speed can be adjusted over a large range by changing the background current, opening the possibility to use the network as a variable frequency oscillator or pattern generator. Finally, using simulations we extend the network to two-dimensional fields and find a rich range of possible behaviours.