Consequences of converting graded to action potentials upon neural information coding and energy efficiency

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a ‘footprint’ in the generator potential that obscures incoming signals. These three processes reduce information rates by ~50% in generator potentials, to ~3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation

Genome analysis and comparative genomics of a Giardia intestinalis assemblage E isolate

<p>Abstract</p> <p>Background</p> <p><it>Giardia intestinalis </it>is a protozoan parasite that causes diarrhea in a wide range of mammalian species. To further understand the genetic diversity between the <it>Giardia intestinalis </it>species, we have performed genome sequencing and analysis of a wild-type <it>Giardia intestinalis </it>sample from the assemblage E group, isolated from a pig.</p> <p>Results</p> <p>We identified 5012 protein coding genes, the majority of which are conserved compared to the previously sequenced genomes of the WB and GS strains in terms of microsynteny and sequence identity. Despite this, there is an unexpectedly large number of chromosomal rearrangements and several smaller structural changes that are present in all chromosomes. Novel members of the VSP, NEK Kinase and HCMP gene families were identified, which may reveal possible mechanisms for host specificity and new avenues for antigenic variation. We used comparative genomics of the three diverse <it>Giardia intestinalis </it>isolates P15, GS and WB to define a core proteome for this species complex and to identify lineage-specific genes. Extensive analyses of polymorphisms in the core proteome of <it>Giardia </it>revealed differential rates of divergence among cellular processes.</p> <p>Conclusions</p> <p>Our results indicate that despite a well conserved core of genes there is significant genome variation between <it>Giardia </it>isolates, both in terms of gene content, gene polymorphisms, structural chromosomal variations and surface molecule repertoires. This study improves the annotation of the <it>Giardia </it>genomes and enables the identification of functionally important variation.</p

Cellular and Network Contributions to Excitability of Layer 5 Neocortical Pyramidal Neurons in the Rat

There is a considerable gap between investigating the dynamics of single neurons and the computational aspects of neural networks. A growing number of studies have attempted to overcome this gap using the excitation in brain slices elicited by various chemical manipulations of the bath solution. However, there has been no quantitative study on the effects of these manipulations on the cellular and network factors controlling excitability. Using the whole-cell configuration of the patch-clamp technique we recorded the membrane potential from the soma of layer 5 pyramidal neurons in acute brain slices from the somatosensory cortex of young rats at 22°C and 35°C. Using blockers of synaptic transmission, we show distinct changes in cellular properties following modification of the ionic composition of the artificial cerebrospinal fluid (ACSF). Thus both cellular and network changes may contribute to the observed effects of slice excitation solutions on the physiology of single neurons. Furthermore, our data suggest that the difference in the ionic composition of current standard ACSF from that of CSF measured in vivo cause ACSF to depress network activity in acute brain slices. This may affect outcomes of experiments investigating biophysical and physiological properties of neurons in such preparations. Our results strongly advocate the necessity of redesigning experiments routinely carried out in the quiescent acute brain slice preparation

Time-Warp–Invariant Neuronal Processing

A biophysical mechanism acting in auditory neurons allows the brain to process the high variability of speaking rates in natural speech in a time-warp-invariant manner

Synaptic Transmission and Plasticity in an Active Cortical Network

BACKGROUND: The cerebral cortex is permanently active during both awake and sleep states. This ongoing cortical activity has an impact on synaptic transmission and short-term plasticity. An activity pattern generated by the cortical network is a slow rhythmic activity that alternates up (active) and down (silent) states, a pattern occurring during slow wave sleep, anesthesia and even in vitro. Here we have studied 1) how network activity affects short term synaptic plasticity and, 2) how synaptic transmission varies in up versus down states. METHODOLOGY/PRINCIPAL FINDINGS: Intracellular recordings obtained from cortex in vitro and in vivo were used to record synaptic potentials, while presynaptic activation was achieved either with electrical or natural stimulation. Repetitive activation of layer 4 to layer 2/3 synaptic connections from ferret visual cortex slices displayed synaptic augmentation that was larger and longer lasting in active than in silent slices. Paired-pulse facilitation was also significantly larger in an active network and it persisted for longer intervals (up to 200 ms) than in silent slices. Intracortical synaptic potentials occurring during up states in vitro increased their amplitude while paired-pulse facilitation disappeared. Both intracortical and thalamocortical synaptic potentials were also significantly larger in up than in down states in the cat visual cortex in vivo. These enhanced synaptic potentials did not further facilitate when pairs of stimuli were given, thus paired-pulse facilitation during up states in vivo was virtually absent. Visually induced synaptic responses displayed larger amplitudes when occurring during up versus down states. This was further tested in rat barrel cortex, where a sensory activated synaptic potential was also larger in up states. CONCLUSIONS/SIGNIFICANCE: These results imply that synaptic transmission in an active cortical network is more secure and efficient due to larger amplitude of synaptic potentials and lesser short term plasticity

Spike Timing and Reliability in Cortical Pyramidal Neurons: Effects of EPSC Kinetics, Input Synchronization and Background Noise on Spike Timing

In vivo studies have shown that neurons in the neocortex can generate action potentials at high temporal precision. The mechanisms controlling timing and reliability of action potential generation in neocortical neurons, however, are still poorly understood. Here we investigated the temporal precision and reliability of spike firing in cortical layer V pyramidal cells at near-threshold membrane potentials. Timing and reliability of spike responses were a function of EPSC kinetics, temporal jitter of population excitatory inputs, and of background synaptic noise. We used somatic current injection to mimic population synaptic input events and measured spike probability and spike time precision (STP), the latter defined as the time window (Δt) holding 80% of response spikes. EPSC rise and decay times were varied over the known physiological spectrum. At spike threshold level, EPSC decay time had a stronger influence on STP than rise time. Generally, STP was highest (≤2.45 ms) in response to synchronous compounds of EPSCs with fast rise and decay kinetics. Compounds with slow EPSC kinetics (decay time constants>6 ms) triggered spikes at lower temporal precision (≥6.58 ms). We found an overall linear relationship between STP and spike delay. The difference in STP between fast and slow compound EPSCs could be reduced by incrementing the amplitude of slow compound EPSCs. The introduction of a temporal jitter to compound EPSCs had a comparatively small effect on STP, with a tenfold increase in jitter resulting in only a five fold decrease in STP. In the presence of simulated synaptic background activity, precisely timed spikes could still be induced by fast EPSCs, but not by slow EPSCs

Motoneuron membrane potentials follow a time inhomogeneous jump diffusion process

Stochastic leaky integrate-and-fire models are popular due to their simplicity and statistical tractability. They have been widely applied to gain understanding of the underlying mechanisms for spike timing in neurons, and have served as building blocks for more elaborate models. Especially the Ornstein–Uhlenbeck process is popular to describe the stochastic fluctuations in the membrane potential of a neuron, but also other models like the square-root model or models with a non-linear drift are sometimes applied. Data that can be described by such models have to be stationary and thus, the simple models can only be applied over short time windows. However, experimental data show varying time constants, state dependent noise, a graded firing threshold and time-inhomogeneous input. In the present study we build a jump diffusion model that incorporates these features, and introduce a firing mechanism with a state dependent intensity. In addition, we suggest statistical methods to estimate all unknown quantities and apply these to analyze turtle motoneuron membrane potentials. Finally, simulated and real data are compared and discussed. We find that a square-root diffusion describes the data much better than an Ornstein–Uhlenbeck process with constant diffusion coefficient. Further, the membrane time constant decreases with increasing depolarization, as expected from the increase in synaptic conductance. The network activity, which the neuron is exposed to, can be reasonably estimated to be a threshold version of the nerve output from the network. Moreover, the spiking characteristics are well described by a Poisson spike train with an intensity depending exponentially on the membrane potential

Burst-Time-Dependent Plasticity Robustly Guides ON/OFF Segregation in the Lateral Geniculate Nucleus

Spontaneous retinal activity (known as “waves”) remodels synaptic connectivity to the lateral geniculate nucleus (LGN) during development. Analysis of retinal waves recorded with multielectrode arrays in mouse suggested that a cue for the segregation of functionally distinct (ON and OFF) retinal ganglion cells (RGCs) in the LGN may be a desynchronization in their firing, where ON cells precede OFF cells by one second. Using the recorded retinal waves as input, with two different modeling approaches we explore timing-based plasticity rules for the evolution of synaptic weights to identify key features underlying ON/OFF segregation. First, we analytically derive a linear model for the evolution of ON and OFF weights, to understand how synaptic plasticity rules extract input firing properties to guide segregation. Second, we simulate postsynaptic activity with a nonlinear integrate-and-fire model to compare findings with the linear model. We find that spike-time-dependent plasticity, which modifies synaptic weights based on millisecond-long timing and order of pre- and postsynaptic spikes, fails to segregate ON and OFF retinal inputs in the absence of normalization. Implementing homeostatic mechanisms results in segregation, but only with carefully-tuned parameters. Furthermore, extending spike integration timescales to match the second-long input correlation timescales always leads to ON segregation because ON cells fire before OFF cells. We show that burst-time-dependent plasticity can robustly guide ON/OFF segregation in the LGN without normalization, by integrating pre- and postsynaptic bursts irrespective of their firing order and over second-long timescales. We predict that an LGN neuron will become ON- or OFF-responsive based on a local competition of the firing patterns of neighboring RGCs connecting to it. Finally, we demonstrate consistency with ON/OFF segregation in ferret, despite differences in the firing properties of retinal waves. Our model suggests that diverse input statistics of retinal waves can be robustly interpreted by a burst-based rule, which underlies retinogeniculate plasticity across different species

Coupling changes in cell shape to chromosome segregation

Animal cells undergo dramatic changes in shape, mechanics and polarity as they progress through the different stages of cell division. These changes begin at mitotic entry, with cell–substrate adhesion remodelling, assembly of a cortical actomyosin network and osmotic swelling, which together enable cells to adopt a near spherical form even when growing in a crowded tissue environment. These shape changes, which probably aid spindle assembly and positioning, are then reversed at mitotic exit to restore the interphase cell morphology. Here, we discuss the dynamics, regulation and function of these processes, and how cell shape changes and sister chromatid segregation are coupled to ensure that the daughter cells generated through division receive their fair inheritance

Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields

A fundamental question in understanding neuronal computations is how dendritic events influence the output of the neuron. Different forms of integration of neighbouring and distributed synaptic inputs, isolated dendritic spikes and local regulation of synaptic efficacy suggest that individual dendritic branches may function as independent computational subunits. In the present paper, we study how these local computations influence the output of the neuron. Using a simple cascade model, we demonstrate that triggering somatic firing by a relatively small dendritic branch requires the amplification of local events by dendritic spiking and synaptic plasticity. The moderately branching dendritic tree of granule cells seems optimal for this computation since larger dendritic trees favor local plasticity by isolating dendritic compartments, while reliable detection of individual dendritic spikes in the soma requires a low branch number. Finally, we demonstrate that these parallel dendritic computations could contribute to the generation of multiple independent place fields of hippocampal granule cells