Dynamic clamp with StdpC software

Dynamic clamp is a powerful method that allows the introduction of artificial electrical components into target cells to simulate ionic conductances and synaptic inputs. This method is based on a fast cycle of measuring the membrane potential of a cell, calculating the current of a desired simulated component using an appropriate model and injecting this current into the cell. Here we present a dynamic clamp protocol using free, fully integrated, open-source software (StdpC, for spike timing-dependent plasticity clamp). Use of this protocol does not require specialist hardware, costly commercial software, experience in real-time operating systems or a strong programming background. The software enables the configuration and operation of a wide range of complex and fully automated dynamic clamp experiments through an intuitive and powerful interface with a minimal initial lead time of a few hours. After initial configuration, experimental results can be generated within minutes of establishing cell recording

The Natural Variation of a Neural Code

The way information is represented by sequences of action potentials of spiking neurons is determined by the input each neuron receives, but also by its biophysics, and the specifics of the circuit in which it is embedded. Even the “code” of identified neurons can vary considerably from individual to individual. Here we compared the neural codes of the identified H1 neuron in the visual systems of two families of flies, blow flies and flesh flies, and explored the effect of the sensory environment that the flies were exposed to during development on the H1 code. We found that the two families differed considerably in the temporal structure of the code, its content and energetic efficiency, as well as the temporal delay of neural response. The differences in the environmental conditions during the flies' development had no significant effect. Our results may thus reflect an instance of a family-specific design of the neural code. They may also suggest that individual variability in information processing by this specific neuron, in terms of both form and content, is regulated genetically

Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties

The thick-tufted layer 5b pyramidal cell extends its dendritic tree to all six layers of the mammalian neocortex and serves as a major building block for the cortical column. L5b pyramidal cells have been the subject of extensive experimental and modeling studies, yet conductance-based models of these cells that faithfully reproduce both their perisomatic Na+-spiking behavior as well as key dendritic active properties, including Ca2+ spikes and back-propagating action potentials, are still lacking. Based on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics. We used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca2+ spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties. Furthermore, we show a useful way to analyze model parameters with our sets of models, which enabled us to identify some of the mechanisms responsible for the dynamic properties of L5b pyramidal cells as well as mechanisms that are sensitive to morphological changes. This automated framework can be used to develop a database of faithful models for other neuron types. The models we present provide several experimentally-testable predictions and can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities

Innate Synchronous Oscillations in Freely-Organized Small Neuronal Circuits

BACKGROUND: Information processing in neuronal networks relies on the network's ability to generate temporal patterns of action potentials. Although the nature of neuronal network activity has been intensively investigated in the past several decades at the individual neuron level, the underlying principles of the collective network activity, such as the synchronization and coordination between neurons, are largely unknown. Here we focus on isolated neuronal clusters in culture and address the following simple, yet fundamental questions: What is the minimal number of cells needed to exhibit collective dynamics? What are the internal temporal characteristics of such dynamics and how do the temporal features of network activity alternate upon crossover from minimal networks to large networks? METHODOLOGY/PRINCIPAL FINDINGS: We used network engineering techniques to induce self-organization of cultured networks into neuronal clusters of different sizes. We found that small clusters made of as few as 40 cells already exhibit spontaneous collective events characterized by innate synchronous network oscillations in the range of 25 to 100 Hz. The oscillation frequency of each network appeared to be independent of cluster size. The duration and rate of the network events scale with cluster size but converge to that of large uniform networks. Finally, the investigation of two coupled clusters revealed clear activity propagation with master/slave asymmetry. CONCLUSIONS/SIGNIFICANCE: The nature of the activity patterns observed in small networks, namely the consistent emergence of similar activity across networks of different size and morphology, suggests that neuronal clusters self-regulate their activity to sustain network bursts with internal oscillatory features. We therefore suggest that clusters of as few as tens of cells can serve as a minimal but sufficient functional network, capable of sustaining oscillatory activity. Interestingly, the frequencies of these oscillations are similar those observed in vivo

Timescales of Multineuronal Activity Patterns Reflect Temporal Structure of Visual Stimuli

The investigation of distributed coding across multiple neurons in the cortex remains to this date a challenge. Our current understanding of collective encoding of information and the relevant timescales is still limited. Most results are restricted to disparate timescales, focused on either very fast, e.g., spike-synchrony, or slow timescales, e.g., firing rate. Here, we investigated systematically multineuronal activity patterns evolving on different timescales, spanning the whole range from spike-synchrony to mean firing rate. Using multi-electrode recordings from cat visual cortex, we show that cortical responses can be described as trajectories in a high-dimensional pattern space. Patterns evolve on a continuum of coexisting timescales that strongly relate to the temporal properties of stimuli. Timescales consistent with the time constants of neuronal membranes and fast synaptic transmission (5–20 ms) play a particularly salient role in encoding a large amount of stimulus-related information. Thus, to faithfully encode the properties of visual stimuli the brain engages multiple neurons into activity patterns evolving on multiple timescales

Activity-Dependent Presynaptic Effect of Serotonin 1B Receptors on the Somatosensory Thalamocortical Transmission in Neonatal Mice

The disruptive effect of excessive serotonin (5-HT) levels on the development of cortical sensory maps is mediated by 5-HT1B receptors, as shown in barrelless monoamine oxidase A knockout mice, in which the additional inactivation of 5-HT1B receptors restores the barrels. However, it is unclear whether 5-HT1B receptors mediate their effect on barrel formation by a trophic action or an activity-dependent effect. To test for a possible effect of 5-HT1B receptors on activity, we studied the influence of 5-HT on the thalamocortical (TC) synaptic transmission in layer IV cortical neurons. In TC slices of postnatal day 5 (P5)–P9 neonate mice, we show that 5-HT reduces monosynaptic TC EPSCs evoked by low-frequency internal capsule stimulation and relieves the short-term depression of the EPSC evoked by high-frequency stimulation. We provide evidence that 5-HT decreases the presynaptic releas

Serotonin suppresses the slow afterhyperpolarization in rat intralaminar and midline thalamic neurones by activating 5-HT7 receptors

While the highest expression level of 5-HT7 receptors in the brain is observed in intralaminar and midline thalamic neurones, the physiological role of these receptors in this structure is unknown. In vivo recordings have shown that stimulation of the serotonergic raphe nuclei can alter the response of these neurones to a nociceptive stimulus, suggesting that serotonin modulates their firing properties. Using the patch-clamp technique in rat thalamic brain slices, we demonstrate that activation of 5-HT7 receptors can strongly modulate the excitability of intralaminar and midline thalamic neurones by inhibiting the calcium-activated potassium conductance that is responsible for the slow afterhyperpolarization (sAHP) following a spike discharge. This sAHP was inhibited after activation of the cAMP pathway, either by bath application of forskolin or intracellular perfusion with 8-bromo-cAMP. The inhibitory effect of 5-HT7 receptors on sAHPs was blocked by the protein kinase A antagonist RP-cAMPS. Calcium-imaging experiments showed no change in intracellular calcium levels during the 5-HT7 response, indicating that in these neurones, a global calcium signal was not necessary to activate the cAMP cascade. Finally, bath application of serotonin produced a strong increase in cytosolic cAMP concentration, as measured using the fluorescent probe FlCRhR, and an inhibition of the sAHP. Taken together, these results suggest that 5-HT7 receptors are implicated in the effect of 5-HT on sAHP in intralaminar and midline thalamic neurones, an effect that is mediated by the cAMP second-messenger cascade

Quantitative expression profiling of identified neurons reveals cell-specific constraints on highly variable levels of gene expression

The postdevelopmental basis of cellular identity and the unique cellular output of a particular neuron type are of particular interest in the nervous system because a detailed understanding of circuits responsible for complex processes in the brain is impeded by the often ambiguous classification of neurons in these circuits. Neurons have been classified by morphological, electrophysiological, and neurochemical techniques. More recently, molecular approaches, particularly microarray, have been applied to the question of neuronal identity. With the realization that proteins expressed exclusively in only one type of neuron are rare, expression profiles obtained from neuronal subtypes are analyzed to search for diagnostic patterns of gene expression. However, this expression profiling hinges on one critical and implicit assumption: that neurons of the same type in different animals achieve their conserved functional output via conserved levels and quantitative relationships of gene expression. Here we exploit the unambiguously identifiable neurons in the crab stomatogastric ganglion to investigate the precise quantitative expression profiling of neurons at the level of single-cell ion channel expression. By measuring absolute mRNA levels of six different channels in the same individually identified neurons, we demonstrate that not only do individual cell types possess highly variable levels of channel expression but that this variability is constrained by unique patterns of correlated channel expression