A Neanderthal Sodium Channel Increases Pain Sensitivity in Present-Day Humans.

The sodium channel Nav1.7 is crucial for impulse generation and conduction in peripheral pain pathways [1]. In Neanderthals, the Nav1.7 protein carried three amino acid substitutions (M932L, V991L, and D1908G) relative to modern humans. We expressed Nav1.7 proteins carrying all combinations of these substitutions and studied their electrophysiological effects. Whereas the single amino acid substitutions do not affect the function of the ion channel, the full Neanderthal variant carrying all three substitutions, as well as the combination of V991L with D1908G, shows reduced inactivation, suggesting that peripheral nerves were more sensitive to painful stimuli in Neanderthals than in modern humans. We show that, due to gene flow from Neanderthals, the three Neanderthal substitutions are found in ∼0.4% of present-day Britons, where they are associated with heightened pain sensitivity.

Synaptic proximity enables NMDAR signalling to promote brain metastasis.

Metastasis-the disseminated growth of tumours in distant organs-underlies cancer mortality. Breast-to-brain metastasis (B2BM) is a common and disruptive form of cancer and is prevalent in the aggressive basal-like subtype, but is also found at varying frequencies in all cancer subtypes. Previous studies revealed parameters of breast cancer metastasis to the brain, but its preference for this site remains an enigma. Here we show that B2BM cells co-opt a neuronal signalling pathway that was recently implicated in invasive tumour growth, involving activation by glutamate ligands of N-methyl-D-aspartate receptors (NMDARs), which is key in model systems for metastatic colonization of the brain and is associated with poor prognosis. Whereas NMDAR activation is autocrine in some primary tumour types, human and mouse B2BM cells express receptors but secrete insufficient glutamate to induce signalling, which is instead achieved by the formation of pseudo-tripartite synapses between cancer cells and glutamatergic neurons, presenting a rationale for brain metastasis.

Kv4.2 channel activity controls intrinsic firing dynamics of arcuate kisspeptin neurons.

KEY POINTS: Neurons in the hypothalamus of the brain which secrete the peptide kisspeptin are important regulators of reproduction, and normal reproductive development. Electrical activity, in the form of action potentials, or spikes, leads to secretion of peptides and neurotransmitters, influencing the activity of downstream neurons; in kisspeptin neurons, this activity is highly irregular, but the mechanism of this is not known. In this study, we show that irregularity depends on the presence of a particular type of potassium ion channel in the membrane, which opens transiently in response to electrical excitation. The results contribute to understanding how kisspeptin neurons generate and time their membrane potential spikes, and how reliable this process is. Improved understanding of the activity of kisspeptin neurons, and how it shapes their secretion of peptides, is expected to lead to better treatment for reproductive dysfunction and disorders of reproductive development. ABSTRACT: Kisspeptin neurons in the hypothalamus are critically involved in reproductive function, via their effect on GnRH neuron activity and consequent gonadotropin release. Kisspeptin neurons show an intrinsic irregularity of firing, but the mechanism of this remains unclear. To address this, we carried out targeted whole-cell patch-clamp recordings of kisspeptin neurons in the arcuate nucleus (Kiss1Arc ), in brain slices isolated from adult male Kiss-Cre:tdTomato mice. Cells fired irregularly in response to constant current stimuli, with a wide range of spike time variability, and prominent subthreshold voltage fluctuations. In voltage clamp, both a persistent sodium (NaP) current and a fast transient (A-type) potassium current were apparent, activating at potentials just below the threshold for spiking. These currents have also previously been described in irregular-spiking cortical interneurons, in which the A-type current, mediated by Kv4 channels, interacts with NaP current to generate complex dynamics of the membrane potential, and irregular firing. In Kiss1Arc neurons, A-type current was blocked by phrixotoxin, a specific blocker of Kv4.2/4.3 channels, and consistent expression of Kv4.2 transcripts was detected by single-cell RT-PCR. In addition, firing irregularity was correlated to the density of A-type current in the membrane. Using conductance injection, we demonstrated that adding Kv4-like potassium conductance (gKv4 ) to a cell produces a striking increase in firing irregularity, and excitability is reduced, while subtracting gKv4 has the opposite effects. Thus, we propose that Kv4 interacting dynamically with NaP is a key determinant of the irregular firing behaviour of Kiss1Arc neurons, shaping their physiological function in gonadotropin release.

Fluctuation Analysis in Nonstationary Conditions: Single Ca2+ Channel Current in Pyramidal Neurons.

Fluctuation analysis is a method that allows measurement of the single-channel current of ion channels even when it is too small to be resolved directly with the patch-clamp technique. This is the case for voltage-gated calcium channels. They are present in all mammalian central neurons, controlling presynaptic release of transmitter, postsynaptic signaling, and synaptic integration. The amplitudes of their single-channel currents in a physiological concentration of extracellular calcium, however, are small and not well determined. But measurement of this quantity is essential for estimating numbers of functional voltage-gated calcium channels in the membrane and the size of channel-associated calcium signaling domains, and for understanding the stochastic nature of calcium signaling. Here, we recorded the voltage-gated calcium channel current in nucleated patches from layer 5 pyramidal neurons in rat neocortex, in physiological external calcium (1-2 mM). The ensemble-averaging of current responses required for conventional fluctuation analysis proved impractical because of the rapid rundown of calcium channel currents. We therefore developed a more robust method, using mean current fitting of individual current responses and band-pass filtering. Furthermore, voltage-ramp stimulation proved useful. We validated the accuracy of the method by analyzing simulated data. At an external calcium concentration of 1 mM, and a membrane potential of -20 mV, we found that the average single-channel current amplitude was ∼0.04 pA, increasing to 0.065 pA at 2 mM external calcium, and 0.12 pA at 5 mM. The relaxation time constant of the fluctuations was in the range 0.2-0.8 ms. The results are relevant to understanding the stochastic properties of dendritic Ca2+ spikes in neocortical layer 5 pyramidal neurons. With the reported method, single-channel current amplitude of native voltage-gated calcium channels can be resolved accurately despite conditions of unstable rundown.

Autocrine, paracrine and necrotic NMDA receptor signalling in mouse pancreatic neuroendocrine tumour cells.

N-Methyl-d-aspartate receptor (NMDAR) activation is implicated in the malignant progression of many cancer types, as previously shown by the growth-inhibitory effects of NMDAR antagonists. NMDAR-mediated calcium influx and its downstream signalling depend critically, however, on the dynamics of membrane potential and ambient glutamate concentration, which are poorly characterized in cancer cells. Here, we have used low-noise whole-cell patch-clamp recording to investigate the electrophysiology of glutamate signalling in pancreatic neuroendocrine tumour (PanNET) cells derived from a genetically-engineered mouse model (GEMM) of PanNET, in which NMDAR signalling is known to promote cancer progression. Activating NMDARs caused excitation and intracellular calcium elevation, and intracellular perfusion with physiological levels of glutamate led to VGLUT-dependent autocrine NMDAR activation. Necrotic cells, which are often present in rapidly-growing tumours, were shown to release endogenous cytoplasmic glutamate, and necrosis induced by mechanical rupture of the plasma membrane produced intense NMDAR activation in nearby cells. Computational modelling, based on these results, predicts that NMDARs in cancer cells can be strongly activated in the tumour microenvironment by both autocrine glutamate release and necrosis.

Intrinsic Cornu Ammonis Area 1 Theta-Nested Gamma Oscillations Induced by Optogenetic Theta Frequency Stimulation.

Gamma oscillations (30-120 Hz) are thought to be important for various cognitive functions, including perception and working memory, and disruption of these oscillations has been implicated in brain disorders, such as schizophrenia and Alzheimer's disease. The cornu ammonis area 1 (CA1) of the hippocampus receives gamma frequency inputs from upstream regions (cornu ammonis area 3 and medial entorhinal cortex) and generates itself a faster gamma oscillation. The exact nature and origin of the intrinsic CA1 gamma oscillation is still under debate. Here, we expressed channel rhodopsin-2 under the CaMKIIα promoter in mice and prepared hippocampal slices to produce a model of intrinsic CA1 gamma oscillations. Sinusoidal optical stimulation of CA1 at theta frequency was found to induce robust theta-nested gamma oscillations with a temporal and spatial profile similar to CA1 gamma in vivo The results suggest the presence of a single gamma rhythm generator with a frequency range of 65-75 Hz at 32 °C. Pharmacological analysis found that the oscillations depended on both AMPA and GABAA receptors. Cell-attached and whole-cell recordings revealed that excitatory neuron firing slightly preceded interneuron firing within each gamma cycle, suggesting that this intrinsic CA1 gamma oscillation is generated with a pyramidal-interneuron circuit mechanism. SIGNIFICANCE STATEMENT: This study demonstrates that the cornu ammonis area 1 (CA1) is capable of generating intrinsic gamma oscillations in response to theta input. This gamma generator is independent of activity in the upstream regions, highlighting that CA1 can produce its own gamma oscillation in addition to inheriting activity from the upstream regions. This supports the theory that gamma oscillations predominantly function to achieve local synchrony, and that a local gamma generated in each area conducts the signal to the downstream region.

Development and function of human cerebral cortex neural networks from pluripotent stem cells in vitro.

A key aspect of nervous system development, including that of the cerebral cortex, is the formation of higher-order neural networks. Developing neural networks undergo several phases with distinct activity patterns in vivo, which are thought to prune and fine-tune network connectivity. We report here that human pluripotent stem cell (hPSC)-derived cerebral cortex neurons form large-scale networks that reflect those found in the developing cerebral cortex in vivo. Synchronised oscillatory networks develop in a highly stereotyped pattern over several weeks in culture. An initial phase of increasing frequency of oscillations is followed by a phase of decreasing frequency, before giving rise to non-synchronous, ordered activity patterns. hPSC-derived cortical neural networks are excitatory, driven by activation of AMPA- and NMDA-type glutamate receptors, and can undergo NMDA-receptor-mediated plasticity. Investigating single neuron connectivity within PSC-derived cultures, using rabies-based trans-synaptic tracing, we found two broad classes of neuronal connectivity: most neurons have small numbers (<10) of presynaptic inputs, whereas a small set of hub-like neurons have large numbers of synaptic connections (>40). These data demonstrate that the formation of hPSC-derived cortical networks mimics in vivo cortical network development and function, demonstrating the utility of in vitro systems for mechanistic studies of human forebrain neural network biology.

Density of voltage-gated potassium channels is a bifurcation parameter in pyramidal neurons.

Several types of intrinsic dynamics have been identified in brain neurons. Type 1 excitability is characterized by a continuous frequency-stimulus relationship and, thus, an arbitrarily low frequency at threshold current. Conversely, Type 2 excitability is characterized by a discontinuous frequency-stimulus relationship and a nonzero threshold frequency. In previous theoretical work we showed that the density of Kv channels is a bifurcation parameter, such that increasing the Kv channel density in a neuron model transforms Type 1 excitability into Type 2 excitability. Here we test this finding experimentally, using the dynamic clamp technique on Type 1 pyramidal cells in rat cortex. We found that increasing the density of slow Kv channels leads to a shift from Type 1 to Type 2 threshold dynamics, i.e., a distinct onset frequency, subthreshold oscillations, and reduced latency to first spike. In addition, the action potential was resculptured, with a narrower spike width and more pronounced afterhyperpolarization. All changes could be captured with a two-dimensional model. It may seem paradoxical that an increase in slow K channel density can lead to a higher threshold firing frequency; however, this can be explained in terms of bifurcation theory. In contrast to previous work, we argue that an increased outward current leads to a change in dynamics in these neurons without a rectification of the current-voltage curve. These results demonstrate that the behavior of neurons is determined by the global interactions of their dynamical elements and not necessarily simply by individual types of ion channels.

libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience.

NeuroML is an XML-based model description language, which provides a powerful common data format for defining and exchanging models of neurons and neuronal networks. In the latest version of NeuroML, the structure and behavior of ion channel, synapse, cell, and network model descriptions are based on underlying definitions provided in LEMS, a domain-independent language for expressing hierarchical mathematical models of physical entities. While declarative approaches for describing models have led to greater exchange of model elements among software tools in computational neuroscience, a frequent criticism of XML-based languages is that they are difficult to work with directly. Here we describe two Application Programming Interfaces (APIs) written in Python (http://www.python.org), which simplify the process of developing and modifying models expressed in NeuroML and LEMS. The libNeuroML API provides a Python object model with a direct mapping to all NeuroML concepts defined by the NeuroML Schema, which facilitates reading and writing the XML equivalents. In addition, it offers a memory-efficient, array-based internal representation, which is useful for handling large-scale connectomics data. The libNeuroML API also includes support for performing common operations that are required when working with NeuroML documents. Access to the LEMS data model is provided by the PyLEMS API, which provides a Python implementation of the LEMS language, including the ability to simulate most models expressed in LEMS. Together, libNeuroML and PyLEMS provide a comprehensive solution for interacting with NeuroML models in a Python environment.

Quantifying network properties in multi-electrode recordings: spatiotemporal characterization and inter-trial variation of evoked gamma oscillations in mouse somatosensory cortex in vitro.

Linking the structural connectivity of brain circuits to their cooperative dynamics and emergent functions is a central aim of neuroscience research. Graph theory has recently been applied to study the structure-function relationship of networks, where dynamical similarity of different nodes has been turned into a "static" functional connection. However, the capability of the brain to adapt, learn and process external stimuli requires a constant dynamical functional rewiring between circuitries and cell assemblies. Hence, we must capture the changes of network functional connectivity over time. Multi-electrode array data present a unique challenge within this framework. We study the dynamics of gamma oscillations in acute slices of the somatosensory cortex from juvenile mice recorded by planar multi-electrode arrays. Bursts of gamma oscillatory activity lasting a few hundred milliseconds could be initiated only by brief trains of electrical stimulations applied at the deepest cortical layers and simultaneously delivered at multiple locations. Local field potentials were used to study the spatio-temporal properties and the instantaneous synchronization profile of the gamma oscillatory activity, combined with current source density (CSD) analysis. Pair-wise differences in the oscillation phase were used to determine the presence of instantaneous synchronization between the different sites of the circuitry during the oscillatory period. Despite variation in the duration of the oscillatory response over successive trials, they showed a constant average power, suggesting that the rate of expenditure of energy during the gamma bursts is consistent across repeated stimulations. Within each gamma burst, the functional connectivity map reflected the columnar organization of the neocortex. Over successive trials, an apparently random rearrangement of the functional connectivity was observed, with a more stable columnar than horizontal organization. This work reveals new features of evoked gamma oscillations in developing cortex.

Control of layer 5 pyramidal cell spiking by oscillatory inhibition in the distal apical dendrites: a computational modeling study.

The distal apical dendrites of layer 5 pyramidal neurons receive cortico-cortical and thalamocortical top-down and feedback inputs, as well as local recurrent inputs. A prominent source of recurrent inhibition in the neocortical circuit is somatostatin-positive Martinotti cells, which preferentially target distal apical dendrites of pyramidal cells. These electrically coupled cells can fire synchronously at various frequencies, including over a relatively slow range (5∼30 Hz), thereby imposing oscillatory inhibition on the pyramidal apical tuft dendrites. We examined how such distal oscillatory inhibition influences the firing of a biophysically detailed layer 5 pyramidal neuron model, which reproduced the spatiotemporal properties of sodium, calcium, and N-methyl-D-aspartate receptor spikes found experimentally. We found that oscillatory synchronization strongly influences the impact of distal inhibition on the pyramidal cell firing. Whereas asynchronous inhibition largely cancels out the facilitatory effects of distal excitatory inputs, inhibition oscillating synchronously at around 10∼20 Hz allows distal excitation to drive axosomatic firing, as if distal inhibition were absent. Underlying this is a switch from relatively infrequent burst firing to single spike firing at every period of the inhibitory oscillation. This phenomenon depends on hyperpolarization-activated cation current-dependent membrane potential resonance in the dendrite, but also, in a novel manner, on a cooperative amplification of this resonance by N-methyl-D-aspartate-receptor-driven dendritic action potentials. Our results point to a surprising dependence of the effect of recurrent inhibition by Martinotti cells on their oscillatory synchronization, which may control not only the local circuit activity, but also how it is transmitted to and decoded by downstream circuits.

The Hodgkin-Huxley heritage: from channels to circuits.

The Hodgkin-Huxley studies of the action potential, published 60 years ago, are a central pillar of modern neuroscience research, ranging from molecular investigations of the structural basis of ion channel function to the computational implications at circuit level. In this Symposium Review, we aim to demonstrate the ongoing impact of Hodgkin's and Huxley's ideas. The Hodgkin-Huxley model established a framework in which to describe the structural and functional properties of ion channels, including the mechanisms of ion permeation, selectivity, and gating. At a cellular level, the model is used to understand the conditions that control both the rate and timing of action potentials, essential for neural encoding of information. Finally, the Hodgkin-Huxley formalism is central to computational neuroscience to understand both neuronal integration and circuit level information processing, and how these mechanisms might have evolved to minimize energy cost.

Human cerebral cortex development from pluripotent stem cells to functional excitatory synapses.

Efforts to study the development and function of the human cerebral cortex in health and disease have been limited by the availability of model systems. Extrapolating from our understanding of rodent cortical development, we have developed a robust, multistep process for human cortical development from pluripotent stem cells: directed differentiation of human embryonic stem (ES) and induced pluripotent stem (iPS) cells to cortical stem and progenitor cells, followed by an extended period of cortical neurogenesis, neuronal terminal differentiation to acquire mature electrophysiological properties, and functional excitatory synaptic network formation. We found that induction of cortical neuroepithelial stem cells from human ES cells and human iPS cells was dependent on retinoid signaling. Furthermore, human ES cell and iPS cell differentiation to cerebral cortex recapitulated in vivo development to generate all classes of cortical projection neurons in a fixed temporal order. This system enables functional studies of human cerebral cortex development and the generation of individual-specific cortical networks ex vivo for disease modeling and therapeutic purposes.

Effects of divalent cations on slow unblock of native NMDA receptors in mouse neocortical pyramidal neurons.

The N-methyl-D-aspartate receptor (NMDAR) exhibits strong voltage-dependent block by extracellular Mg(2+) , which is relieved by sustained depolarization and glutamate binding, and which is central to the function of the NMDAR in synaptic plasticity. Rapid membrane depolarization during agonist application reveals a slow unblock of NMDARs, which has important functional implications, for example in the generation of NMDAR spikes, and in determining the narrow time window for spike-timing-dependent plasticity. However, its mechanism is still unclear. Here, we study unblock of divalent cations in native NMDARs in nucleated patches isolated from mouse cortical layer 2/3 pyramidal neurons. Comparing unblock kinetics of NMDARs in the presence of extracellular Mg(2+) or in nominally zero Mg(2+) , and with Mn(2+) or Co(2+) substituting for Mg(2+) , we found that the properties of slow unblock were determined by the identity of the blocking metal ion at the binding site, presumably by affecting the operation of a structural link to channel gating. The time course of slow unblock was not affected by zinc, or the zinc chelator TPEN [N,N,N',N'-tetrakis-(2-pyridylmethyl)-ethylenediamine], while the slower fraction of unblock was reduced by ifenprodil, an NR2B-selective antagonist. Slow unblock was only weakly temperature dependent, speeding up with rise in temperature with a Q(10) of ≈1.5. Finally, using action potential waveform voltage-clamp, we show that this slow relief from divalent cation block is a prominent feature in physiologically realistic patterns of changing membrane potential.

The mechanism of ethanol action on midbrain dopaminergic neuron firing: a dynamic-clamp study of the role of I(h) and GABAergic synaptic integration.

Hyperpolarization-activated and cyclic nucleotide-gated (HCN) channels are expressed in dopaminergic (DA) neurons of the ventral tegmental area (VTA) as well as in DA and GABAergic neurons of the substantia nigra (SN). The excitation of DA neurons induced by ethanol has been proposed to result from its enhancing HCN channel current, I(h). Using perforated patch-clamp recordings in rat midbrain slices, we isolated I(h) in these neurons by voltage clamp. We showed that ethanol reversibly increased the amplitude and accelerated the activation kinetics of I(h) and caused a depolarizing shift in its voltage dependence. Using dynamic-clamp conductance injection, we injected artificial I(h) and fluctuating GABAergic synaptic conductance inputs into neurons following block of intrinsic I(h). This demonstrated directly a major role of I(h) in promoting rebound spiking following phasic inhibition, which was enhanced as the kinetics and amplitude of I(h) were changed in the manner induced by ethanol. Similar effects of ethanol were observed on I(h) and firing rate in non-DA, putatively GABAergic interneurons, indicating that in addition to its direct effects on firing, ethanol will produce large changes in the inhibition and disinhibition (via GABAergic interneurons) converging on DA neurons. Thus the overall effects of ethanol on firing of DA cells of the VTA and SN in vivo, and hence on phasic dopamine release in the striatum, appear to be determined substantially by its action on I(h) in both DA cells and GABAergic interneurons.

Impact of gamma-oscillatory inhibition on the signal transmission of a cortical pyramidal neuron.

Networks of synchronized fast-spiking interneurons are thought to be key elements in the generation of gamma (γ) oscillations (30-80 Hz) in the brain. We examined how such γ-oscillatory inhibition regulates the output of a cortical pyramidal cell. Specifically, we modeled a situation where a pyramidal cell receives inputs from γ-synchronized fast-spiking inhibitory interneurons. This model successfully reproduced several important aspects of a recent experimental result regarding the γ-inhibitory regulation of pyramidal cellular firing that is presumably associated with the sensation of whisker stimuli. Through an in-depth analysis of this model system, we show that there is an obvious rhythmic gating effect of the γ-oscillated interneuron networks on the pyramidal neuron's signal transmission. This effect is further illustrated by the interactions of this interneuron network and the pyramidal neuron. Prominent power in the γ frequency range can emerge provided that there are appropriate delays on the excitatory connections and inhibitory synaptic conductance between interneurons. These results indicate that interactions between excitation and inhibition are critical for the modulation of coherence and oscillation frequency of network activities.

Synchronization of firing in cortical fast-spiking interneurons at gamma frequencies: a phase-resetting analysis.

Fast-spiking (FS) cells in the neocortex are interconnected both by inhibitory chemical synapses and by electrical synapses, or gap-junctions. Synchronized firing of FS neurons is important in the generation of gamma oscillations, at frequencies between 30 and 80 Hz. To understand how these synaptic interactions control synchronization, artificial synaptic conductances were injected in FS cells, and the synaptic phase-resetting function (SPRF), describing how the compound synaptic input perturbs the phase of gamma-frequency spiking as a function of the phase at which it is applied, was measured. GABAergic and gap junctional conductances made distinct contributions to the SPRF, which had a surprisingly simple piecewise linear form, with a sharp midcycle break between phase delay and advance. Analysis of the SPRF showed how the intrinsic biophysical properties of FS neurons and their interconnections allow entrainment of firing over a wide gamma frequency band, whose upper and lower frequency limits are controlled by electrical synapses and GABAergic inhibition respectively.

Grouping and classifying electrophysiologically-defined classes of neocortical neurons by single cell, whole-genome expression profiling.

The diversity of neuronal cell types and how to classify them are perennial questions in neuroscience. The advent of global gene expression analysis raised the possibility that comprehensive transcription profiling will resolve neuronal cell types into groups that reflect some or all aspects of their phenotype. This approach has been successfully used to compare gene expression between groups of neurons defined by a common property. Here we extend this approach to ask whether single neuron gene expression profiling can prospectively resolve neuronal subtypes into groups, independent of any phenotypic information, and whether those groups reflect meaningful biological properties of those neurons. We applied methods we have developed to compare gene expression among single neural stem cells to study global gene expression in 18 randomly picked neurons from layer II/III of the early postnatal mouse neocortex. Cells were selected by morphology and by firing characteristics and electrical properties, enabling the definition of each cell as either fast- or regular-spiking, corresponding to a class of inhibitory interneurons or excitatory pyramidal cells. Unsupervised clustering of young neurons by global gene expression resolved the cells into two groups and those broadly corresponded with the two groups of fast- and regular-spiking neurons. Clustering of the entire, diverse group of 18 neurons of different developmental stages also successfully grouped neurons in accordance with the electrophysiological phenotypes, but with more cells misassigned among groups. Genes specifically enriched in regular spiking neurons were identified from the young neuron expression dataset. These results provide a proof of principle that single-cell gene expression profiling may be used to group and classify neurons in a manner reflecting their known biological properties and may be used to identify cell-specific transcripts.

Diversity of intrinsic frequency encoding patterns in rat cortical neurons--mechanisms and possible functions.

Extracellular recordings of single neurons in primary and secondary somatosensory cortices of monkeys in vivo have shown that their firing rate can increase, decrease, or remain constant in different cells, as the external stimulus frequency increases. We observed similar intrinsic firing patterns (increasing, decreasing or constant) in rat somatosensory cortex in vitro, when stimulated with oscillatory input using conductance injection (dynamic clamp). The underlying mechanism of this observation is not obvious, and presents a challenge for mathematical modelling. We propose a simple principle for describing this phenomenon using a leaky integrate-and-fire model with sinusoidal input, an intrinsic oscillation and Poisson noise. Additional enhancement of the gain of encoding could be achieved by local network connections amongst diverse intrinsic response patterns. Our work sheds light on the possible cellular and network mechanisms underlying these opposing neuronal responses, which serve to enhance signal detection.

Uncovering bifurcation patterns in cortical synapses.

Individual cortical synapses are known to exhibit a very complex short-time dynamic behaviour in response to simple "naturalistic" stimulation. We describe a computational study of the experimentally obtained excitatory post-synaptic potential trains of individual cortical synapses. By adopting a new nonlinear modelling scheme we construct robust and repeatable models of the underlying dynamics. These models suggest that cortical synapses exhibit a wide range of either periodic or chaotic dynamics. For stimulus at a fixed rate our models predict that the response of the individual synapse will vary from a fixed point to periodic and chaotic, depending on the frequency of stimulus. Dynamics for individual synapses vary widely, suggesting that the individual behaviour of synapses is highly tuned and that the dynamic behaviour of even a small network of synapse-coupled neurons could be extremely varied.