Low-frequency summation of synaptically activated transient receptor potential channel-mediated depolarizations

Electrochemical control of bone microstructure on electroactive surfaces for modulation of stem cells and bone tissue engineering

Controlling stem cell behavior at the material interface is crucial for the development of novel technologies in stem cell biology and regenerative medicine. The composition and presentation of bio-factors on a surface strongly influence the activity of stem cells. Herein, we designed an electroactive surface that mimics the initial process of trabecular bone formation, by immobilizing chondrocyte-derived plasma membrane nanofragments (PMNFs) on its surface for rapid mineralization within 2 days. Moreover, the electroactive surface was based on the conducting polymer polypyrrole (PPy), which enabled dynamic control of the presentation of PMNFs on the surface via electrochemical redox switching, further resulting in the formation of bone minerals with different morphologies. Furthermore, bone minerals with contrasting surface morphologies had differential effects on the differentiation of human bone marrow-derived stem cells (hBMSCs) cultured on the surface. Together, this electroactive surface showed multifunctional characteristics, not only allowing dynamic control of PMNF presentation but also promoting the formation of bone minerals with different morphologies within 2 days. This electroactive substrate could be valuable for more precise control of stem cell growth and differentiation, and further development of more suitable microenvironments containing bone apatite for housing a bone marrow stem cell niche, such as biochips/bone-on-chips.

Insertion of Calcium-Permeable AMPA Receptors during Epileptiform Activity In Vitro Modulates Excitability of Principal Neurons in the Rat Entorhinal Cortex

Epileptic activity leads to rapid insertion of calcium-permeable α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (CP-AMPARs) into the synapses of cortical and hippocampal glutamatergic neurons, which generally do not express them. The physiological significance of this process is not yet fully understood; however, it is usually assumed to be a pathological process that augments epileptic activity. Using whole-cell patch-clamp recordings in rat entorhinal cortex slices, we demonstrate that the timing of epileptiform discharges, induced by 4-aminopyridine and gabazine, is determined by the shunting effect of Ca2+-dependent slow conductance, mediated predominantly by K+-channels. The blockade of CP-AMPARs by IEM-1460 eliminates this extra conductance and consequently increases the rate of discharge generation. The blockade of NMDARs reduced the additional conductance to a lesser extent than the blockade of CP-AMPARs, indicating that CP-AMPARs are a more significant source of intracellular Ca2+. The study's main findings were implemented in a mathematical model, which reproduces the shunting effect of activity-dependent conductance on the generation of discharges. The obtained results suggest that the expression of CP-AMPARs in principal neurons reduces the discharge generation rate and may be considered as a protective mechanism.

Channels that Cooperate with TRPV4 in the Brain

Transient receptor potential vanilloid 4 (TRPV4) is a nonselective Ca²⁺-permeable cation channel that is a member of the TRP channel family. It is clear that TRPV4 channels are broadly expressed in the brain. As they are expressed on the plasma membrane, they interact with other channels and play a crucial role in nervous system activity. Under some pathological conditions, TRPV4 channels are upregulated and sensitized via cellular signaling pathways, and this can cause nervous system diseases. In this review, we focus on receptors that cooperate with TRPV4, including large-conductance Ca²⁺-activated K⁺(BKca) channels, N-methyl-D-aspartate receptors (NMDARs), α-amino-3-hydroxy-5-methyl-4-isoxazole-propionate receptors (AMPARs), inositol 1,4,5-trisphosphate receptors (IP3Rs), ryanodine receptors (RyRs), aquaporin 4 (AQP4), and other potential cooperative receptors in the brain. The data demonstrate how these channels work together to cause nervous system diseases under pathological conditions. The aim of this review was to discuss the receptors and signaling pathways related to TRPV4 based on recent data on the important physiological functions of TRPV4 channels to provide new clues for future studies and prospective therapeutic targets for related brain diseases.

An Indexing Theory for Working Memory Based on Fast Hebbian Plasticity

Working memory (WM) is a key component of human memory and cognition. Computational models have been used to study the underlying neural mechanisms, but neglected the important role of short-term memory (STM) and long-term memory (LTM) interactions for WM. Here, we investigate these using a novel multiarea spiking neural network model of prefrontal cortex (PFC) and two parietotemporal cortical areas based on macaque data. We propose a WM indexing theory that explains how PFC could associate, maintain, and update multimodal LTM representations. Our simulations demonstrate how simultaneous, brief multimodal memory cues could build a temporary joint memory representation as an “index” in PFC by means of fast Hebbian synaptic plasticity. This index can then reactivate spontaneously and thereby also the associated LTM representations. Cueing one LTM item rapidly pattern completes the associated uncued item via PFC. The PFC–STM network updates flexibly as new stimuli arrive, thereby gradually overwriting older representations.

A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation

A dominant theory of working memory (WM), referred to as the persistent activity hypothesis, holds that recurrently connected neural networks, presumably located in the prefrontal cortex, encode and maintain WM memory items through sustained elevated activity. Reexamination of experimental data has shown that prefrontal cortex activity in single units during delay periods is much more variable than predicted by such a theory and associated computational models. Alternative models of WM maintenance based on synaptic plasticity, such as short-term nonassociative (non-Hebbian) synaptic facilitation, have been suggested but cannot account for encoding of novel associations. Here we test the hypothesis that a recently identified fast-expressing form of Hebbian synaptic plasticity (associative short-term potentiation) is a possible mechanism for WM encoding and maintenance. Our simulations using a spiking neural network model of cortex reproduce a range of cognitive memory effects in the classical multi-item WM task of encoding and immediate free recall of word lists. Memory reactivation in the model occurs in discrete oscillatory bursts rather than as sustained activity. We relate dynamic network activity as well as key synaptic characteristics to electrophysiological measurements. Our findings support the hypothesis that fast Hebbian short-term potentiation is a key WM mechanism.

SIGNIFICANCE STATEMENT Working memory (WM) is a key component of cognition. Hypotheses about the neural mechanism behind WM are currently under revision. Reflecting recent findings of fast Hebbian synaptic plasticity in cortex, we test whether a cortical spiking neural network model with such a mechanism can learn a multi-item WM task (word list learning). We show that our model can reproduce human cognitive phenomena and achieve comparable memory performance in both free and cued recall while being simultaneously compatible with experimental data on structure, connectivity, and neurophysiology of the underlying cortical tissue. These findings are directly relevant to the ongoing paradigm shift in the WM field.

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.

From one moment to the next, in an ever-changing world, and awash in a deluge of sensory data, the brain fluidly guides our actions throughout an astonishing variety of tasks. Processing this ongoing bombardment of information is a fundamental problem faced by its underlying neural circuits. Given that the structure of our actions along with the organization of the environment in which they are performed can be intuitively decomposed into sequences of simpler patterns, an encoding strategy reflecting the temporal nature of these patterns should offer an efficient approach for assembling more complex memories and behaviors. We present a model that demonstrates how activity could propagate through recurrent cortical microcircuits as a result of a learning rule based on neurobiologically plausible time courses and dynamics. The model predicts that the interaction between several learning and dynamical processes constitute a compound mnemonic engram that can flexibly generate sequential step-wise increases of activity within neural populations.

PLC-mediated PI(4,5)P2 hydrolysis regulates activation and inactivation of TRPC6/7 channels

Transient receptor potential classical (or canonical) (TRPC)3, TRPC6, and TRPC7 are a subfamily of TRPC channels activated by diacylglycerol (DAG) produced through the hydrolysis of phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) by phospholipase C (PLC). PI(4,5)P2 depletion by a heterologously expressed phosphatase inhibits TRPC3, TRPC6, and TRPC7 activity independently of DAG; however, the physiological role of PI(4,5)P2 reduction on channel activity remains unclear. We used Förster resonance energy transfer (FRET) to measure PI(4,5)P2 or DAG dynamics concurrently with TRPC6 or TRPC7 currents after agonist stimulation of receptors that couple to Gq and thereby activate PLC. Measurements made at different levels of receptor activation revealed a correlation between the kinetics of PI(4,5)P2 reduction and those of receptor-operated TRPC6 and TRPC7 current activation and inactivation. In contrast, DAG production correlated with channel activation but not inactivation; moreover, the time course of channel inactivation was unchanged in protein kinase C-insensitive mutants. These results suggest that inactivation of receptor-operated TRPC currents is primarily mediated by the dissociation of PI(4,5)P2. We determined the functional dissociation constant of PI(4,5)P2 to TRPC channels using FRET of the PLCδ Pleckstrin homology domain (PHd), which binds PI(4,5)P2, and used this constant to fit our experimental data to a model in which channel gating is controlled by PI(4,5)P2 and DAG. This model predicted similar FRET dynamics of the PHd to measured FRET in either human embryonic kidney cells or smooth muscle cells, whereas a model lacking PI(4,5)P2 regulation failed to reproduce the experimental data, confirming the inhibitory role of PI(4,5)P2 depletion on TRPC currents. Our model also explains various PLC-dependent characteristics of channel activity, including limitation of maximum open probability, shortening of the peak time, and the bell-shaped response of total current. In conclusion, our studies demonstrate a fundamental role for PI(4,5)P2 in regulating TRPC6 and TRPC7 activity triggered by PLC-coupled receptor stimulation.

Computational modeling predicts the ionic mechanism of late-onset responses in unipolar brush cells

Unipolar Brush Cells (UBCs) have been suggested to play a critical role in cerebellar functioning, yet the corresponding cellular mechanisms remain poorly understood. UBCs have recently been reported to generate, in addition to early-onset glutamate receptor-dependent synaptic responses, a late-onset response (LOR) composed of a slow depolarizing ramp followed by a spike burst (Locatelli et al., 2013). The LOR activates as a consequence of synaptic activity and involves an intracellular cascade modulating H- and TRP-current gating. In order to assess the LOR mechanisms, we have developed a UBC multi-compartmental model (including soma, dendrite, initial segment, and axon) incorporating biologically realistic representations of ionic currents and a cytoplasmic coupling mechanism regulating TRP and H channel gating. The model finely reproduced UBC responses to current injection, including a burst triggered by a low-threshold spike (LTS) sustained by CaLVA currents, a persistent discharge sustained by CaHVA currents, and a rebound burst following hyperpolarization sustained by H- and CaLVA-currents. Moreover, the model predicted that H- and TRP-current regulation was necessary and sufficient to generate the LOR and its dependence on the intensity and duration of mossy fiber activity. Therefore, the model showed that, using a basic set of ionic channels, UBCs generate a rich repertoire of bursts, which could effectively implement tunable delay-lines in the local microcircuit.

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.

Long-lasting small-amplitude TRP-mediated dendritic depolarizations in CA1 pyramidal neurons are intrinsically stable and originate from distal tuft regions

In several regions of the nervous system, neurons display bi- or multistable intrinsic properties. Such stable states may be subthreshold and long-lasting, and can appear as a sustained afterdepolarization. In hippocampal CA1 pyramidal neurons, small-amplitude (1 mV) long-lasting (seconds) afterdepolarizations have been reported and are thought to depend on calcium-activated nonselective (CAN) currents recently identified as transient receptor potential (TRP) channels. Continuing our previous experimental and computational work on synaptically metabotropic glutamate receptor (mGluR)-activated TRP currents, we here explore small-amplitude long-lasting depolarizations in a detailed multicompartmental model of a CA1 pyramidal neuron. We confirm a previous hypothesis suggesting that the depolarization results from an interplay of TRP and voltage-gated calcium channels, and contribute to the understanding of the depolarization in several ways. Specifically, we show that: (i) the long-lasting depolarization may be intrinsically stable to weak excitatory and inhibitory input, (ii) the phenomenon is essentially located in distal apical dendrites, (iii) induction is facilitated if simultaneous input arrives at several dendritic branches, and if calcium- and/or mGluR-evoked signals undergo summation, suggesting that both spatial and temporal synaptic summation might be required for the depolarization to occur and (iv) we also show that the integration of inputs to oblique dendrites is strongly modulated by the presence of small-amplitude long-lasting depolarizations in distal tuft dendrites. To conclude, we suggest that small-amplitude long-lasting dendritic depolarizations may contribute to sustaining neural information during behavioural tasks in cases where information is separated in time, as in trace conditioning and delay tasks.

TRPs in the Brain

The Transient receptor potential (TRP) family of cation channels is a large protein family, which is mainly structurally uniform. Proteins consist typically of six transmembrane domains and mostly four subunits are necessary to form a functional channel. Apart from this, TRP channels display a wide variety of activation mechanisms (ligand binding, G-protein coupled receptor dependent, physical stimuli such as temperature, pressure, etc.) and ion selectivity profiles (from highly Ca(2+) selective to non-selective for cations). They have been described now in almost every tissue of the body, including peripheral and central neurons. Especially in the sensory nervous system the role of several TRP channels is already described on a detailed level. This review summarizes data that is currently available on their role in the central nervous system. TRP channels are involved in neurogenesis and brain development, synaptic transmission and they play a key role in the development of several neurological diseases.