The possible role of spike patterns in cortical information processing

The dyslexia-associated gene DCDC2 is required for spike-timing precision in mouse neocortex

BACKGROUND

Variants in dyslexia-associated genes, including DCDC2, have been linked to altered neocortical activation, suggesting that dyslexia associated genes might play as yet unspecified roles in neuronal physiology.

METHODS

Whole-cell patch clamp recordings were used to compare the electrophysiological properties of regular spiking pyramidal neurons of neocortex in Dcdc2 knockout (KO) and wild-type mice. Ribonucleic acid sequencing and reverse transcriptase polymerase chain reaction were performed to identify and characterize changes in gene expression in Dcdc2 KOs.

RESULTS

Neurons in KOs showed increased excitability and decreased temporal precision in action potential firing. The RNA sequencing screen revealed that the N-methyl-D-aspartate receptor (NMDAR) subunit Grin2B was elevated in Dcdc2 KOs, and an electrophysiological assessment confirmed a functional increase in spontaneous NMDAR-mediated activity. Remarkably, the decreased action potential temporal precision could be restored in mutants by treatment with either the NMDAR antagonist (2R)-amino-5-phosphonovaleric acid or the NMDAR 2B subunit-specific antagonist Ro 25-6981.

CONCLUSIONS

These results link the function of the dyslexia-associated gene Dcdc2 to spike timing through activity of NMDAR.

A "spike-based" grammar underlies directional modification in network connectivity: effect on bursting activity and implications for bio-hybrids systems

Developed biological systems are endowed with the ability of interacting with the environment; they sense the external state and react to it by changing their own internal state. Many attempts have been made to build ‘hybrids’ with the ability of perceiving, modifying and reacting to external modifications. Investigation of the rules that govern network changes in a hybrid system may lead to finding effective methods for ‘programming’ the neural tissue toward a desired task. Here we show a new perspective in the use of cortical neuronal cultures from embryonic mouse as a working platform to study targeted synaptic modifications. Differently from the common timing-based methods applied in bio-hybrids robotics, here we evaluated the importance of endogenous spike timing in the information processing. We characterized the influence of a spike-patterned stimulus in determining changes in neuronal synchronization (connectivity strength and precision) of the evoked spiking and bursting activity in the network. We show that tailoring the stimulation pattern upon a neuronal spike timing induces the network to respond stronger and more precisely to the stimulation. Interestingly, the induced modifications are conveyed more consistently in the burst timing. This increase in strength and precision may be a key in the interaction of the network with the external world and may be used to induce directional changes in bio-hybrid systems.

Upregulation of transmitter release probability improves a conversion of synaptic analogue signals into neuronal digital spikes

Action potentials at the neurons and graded signals at the synapses are primary codes in the brain. In terms of their functional interaction, the studies were focused on the influence of presynaptic spike patterns on synaptic activities. How the synapse dynamics quantitatively regulates the encoding of postsynaptic digital spikes remains unclear. We investigated this question at unitary glutamatergic synapses on cortical GABAergic neurons, especially the quantitative influences of release probability on synapse dynamics and neuronal encoding. Glutamate release probability and synaptic strength are proportionally upregulated by presynaptic sequential spikes. The upregulation of release probability and the efficiency of probability-driven synaptic facilitation are strengthened by elevating presynaptic spike frequency and Ca²⁺. The upregulation of release probability improves spike capacity and timing precision at postsynaptic neuron. These results suggest that the upregulation of presynaptic glutamate release facilitates a conversion of synaptic analogue signals into digital spikes in postsynaptic neurons, i.e., a functional compatibility between presynaptic and postsynaptic partners.

Pre & postsynaptic tuning of action potential timing by spontaneous GABAergic activity

Frequency and timing of action potential discharge are key elements for coding and transfer of information between neurons. The nature and location of the synaptic contacts, the biophysical parameters of the receptor-operated channels and their kinetics of activation are major determinants of the firing behaviour of each individual neuron. Ultimately the intrinsic excitability of each neuron determines the input-output function. Here we evaluate the influence of spontaneous GABAergic synaptic activity on the timing of action potentials in Layer 2/3 pyramidal neurones in acute brain slices from the somatosensory cortex of young rats. Somatic dynamic current injection to mimic synaptic input events was employed, together with a simple computational model that reproduce subthreshold membrane properties. Besides the well-documented control of neuronal excitability, spontaneous background GABAergic activity has a major detrimental effect on spike timing. In fact, GABA(A) receptors tune the relationship between the excitability and fidelity of pyramidal neurons via a postsynaptic (the reversal potential for GABA(A) activity) and a presynaptic (the frequency of spontaneous activity) mechanism. GABAergic activity can decrease or increase the excitability of pyramidal neurones, depending on the difference between the reversal potential for GABA(A) receptors and the threshold for action potential. In contrast, spike time jitter can only be increased proportionally to the difference between these two membrane potentials. Changes in excitability by background GABAergic activity can therefore only be associated with deterioration of the reliability of spike timing.

Finding the event structure of neuronal spike trains

Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004 ). Finding events in data with realistic spiking statistics is challenging because events belonging to different spike patterns may overlap. We propose a method for finding spiking events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near-synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.

How synaptic release probability shapes neuronal transmission: information-theoretic analysis in a cerebellar granule cell

A nerve cell receives multiple inputs from upstream neurons by way of its synapses. Neuron processing functions are thus influenced by changes in the biophysical properties of the synapse, such as long-term potentiation (LTP) or depression (LTD). This observation has opened new perspectives on the biophysical basis of learning and memory, but its quantitative impact on the information transmission of a neuron remains partially elucidated. One major obstacle is the high dimensionality of the neuronal input-output space, which makes it unfeasible to perform a thorough computational analysis of a neuron with multiple synaptic inputs. In this work, information theory was employed to characterize the information transmission of a cerebellar granule cell over a region of its excitatory input space following synaptic changes. Granule cells have a small dendritic tree (on average, they receive only four mossy fiber afferents), which greatly bounds the input combinatorial space, reducing the complexity of information-theoretic calculations. Numerical simulations and LTP experiments quantified how changes in neurotransmitter release probability (p) modulated information transmission of a cerebellar granule cell. Numerical simulations showed that p shaped the neurotransmission landscape in unexpected ways. As p increased, the optimality of the information transmission of most stimuli did not increase strictly monotonically; instead it reached a plateau at intermediate p levels. Furthermore, our results showed that the spatiotemporal characteristics of the inputs determine the effect of p on neurotransmission, thus permitting the selection of distinctive preferred stimuli for different p values. These selective mechanisms may have important consequences on the encoding of cerebellar mossy fiber inputs and the plasticity and computation at the next circuit stage, including the parallel fiber-Purkinje cell synapses.

Intracellular Ca2+ regulates spike encoding at cortical GABAergic neurons and cerebellar Purkinje cells differently

Spike encoding at GABAergic neurons plays an important role in maintaining the homeostasis of brain functions for well-organized behaviors. The rise of intracellular Ca2+ in GABAergic neurons causes synaptic plasticity. It is not clear how intracellular Ca2+ influences their spike encoding. We have investigated this issue at GFP-labeled GABAergic cortical neurons and cerebellar Purkinje cells by whole-cell recording in mouse brain slices. Our results show that an elevation of intracellular Ca2+ by infusing adenophostin-A lowers spike encoding at GABAergic cortical neurons and enhances encoding ability at cerebellar Purkinje cells. These differential effects of cytoplasmic Ca2+ on spike encoding are mechanistically associated with Ca2+-induced changes in the refractory periods and threshold potentials of sequential spikes, as well as with various expression ratios of CaM-KII to calcineurin in GABAergic cortical neurons and cerebellar Purkinje cells.

The postnatal development of intrinsic properties and spike encoding at cortical GABAergic neurons

GABAergic neurons play a critical role in maintaining the homeostasis of brain functions for well-organized behaviors. It is not known about the dynamical change in signal encoding at these neurons during postnatal development. We investigated this issue at GFP-labeled GABAergic neurons by whole-cell recording in cortical slices of mice. Our results show that the ability of spike encoding at GABAergic neurons is improved during postnatal development. This change is associated with the reduction of refractory periods and threshold potentials of sequential spikes, as well as the improvement of linear correlations between intrinsic properties and spike capacity. Therefore, the postnatal maturation of the spike encoding capacity at GABAergic neurons will stabilize the excitatory state of cerebral cortex.

Gain and fidelity of transmission patterns at cortical excitatory unitary synapses improve spike encoding

Neuronal spike encoding and synaptic transmission in the brain need be precise and reliable for well-organized behavior and cognition. Little is known about how a unitary synapse reliably transmits presynaptic sequential spikes and how multiple unitary synapses precisely drive their postsynaptic neurons to encode spikes. To address these questions, we investigated the dynamics of glutamatergic unitary synapses as well as their role in driving the encoding of cortical fast-spiking neurons. Synaptic transmission patterns randomly fluctuate among facilitation, depression and parallel over time. The postsynaptic calmodulin-signaling pathway enhances initial responses and converts this fluctuation to a synaptic depression. We integrated current pulses mathematically based on synaptic plasticity and found that they improve spike capacity and timing precision by shortening the spike refractory period at postsynaptic neurons. Our results indicate that the gain and fidelity of synaptic patterns enable reliable transmission of presynaptic signals by the synapse and precise encoding of spikes by postsynaptic neurons. These reproducible neural codes may be involved in controlling well-organized behavior.

Regulation of spike timing in visual cortical circuits

A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role.

The Firing of an Excitable Neuron in the Presence of Stochastic Trains of Strong Synaptic Inputs

We consider a fast-slow excitable system subject to a stochastic excitatory input train and show that under general conditions, its long-term behavior is captured by an irreducible Markov chain with a limiting distribution. This limiting distribution allows for the analytical calculation of the system's probability of firing in response to each input, the expected number of response failures between firings, and the distribution of slow variable values between firings. Moreover, using this approach, it is possible to understand why the system will not have a stationary distribution and why Monte Carlo simulations do not converge under certain conditions. The analytical calculations involved can be performed whenever the distribution of interexcitation intervals and the recovery dynamics of the slow variable are known. The method can be extended to other models that feature a single variable that builds up to a threshold where an instantaneous spike and reset occur. We also discuss how the Markov chain analysis generalizes to any pair of input trains, excitatory or inhibitory and synaptic or not, such that the frequencies of the two trains are sufficiently different from each other. We illustrate this analysis on a model thalamocortical (TC) cell subject to two example distributions of excitatory synaptic inputs in the cases of constant and rhythmic inhibition. The analysis shows a drastic drop in the likelihood of firing just after inhibitory onset in the case of rhythmic inhibition, relative even to the case of elevated but constant inhibition. This observation provides support for a possible mechanism for the induction of motor symptoms in Parkinson's disease and for their relief by deep brain stimulation, analyzed in Rubin and Terman (2004).

Afterhyperpolarization improves spike programming through lowering threshold potentials and refractory periods mediated by voltage-gated sodium channels

Neurons program various patterns of sequential spikes as neural codes to guide animal behavior. Studies show that spike programming (capacity and timing precision) is influenced by inhibitory synaptic inputs and membrane afterhyperpolarization (AHP). Less is clear about how these inhibitory components regulate spike programming, which we investigated at the cortical neurons. Whole-cell current-clamp recording for action potentials and single channel recording for voltage-gated sodium channels (VGSC) were conducted at regular-spiking and fast-spiking neurons in the cortical slices. With quantifying the threshold potentials and refractory periods of sequential spikes, we found that fast-spiking neurons expressing AHP possess lower threshold potentials and shorter refractory periods, and the hyperpolarization pulse immediately after each of spikes lowers threshold potentials and shortens refractory periods at regular-spiking neurons. Moreover, the hyperpolarization pulses shorten the refractory periods for VGSC reactivation and threshold potentials for its sequential activation. Our data indicate that inhibitory components immediately after spikes, such as AHP and recurrent inhibition, improve spike capacity and timing precision via lowering the refractory periods and threshold potentials mediated by voltage-gated sodium channels.

Sodium channel-mediated intrinsic mechanisms underlying the differences of spike programming among GABAergic neurons

Neural codes to guide well-organized behavior are thought to be the programmed patterns of sequential spikes at central neurons, in which the coordinative activities of voltage-gated ion channels are involved. The attention has been paid to study the role of potassium channels in spike pattern; but it is not clear how the intrinsic mechanism mediated by voltage-gated sodium channels (VGSC) influences the programming of sequential spikes, which we investigated at GABAergic cerebellar Purkinje cells and hippocampal interneurons by patch-clamp recording in brain slices. Spike capacity is higher at Purkinje cells than interneurons in response to the given intensities of inputs, and is dependent on input intensity. Compared to interneurons, Purkinje cells express the lower threshold potentials and the shorter refractory periods of sequential spikes. The increases of input intensities shorten spike refractory periods significantly. The threshold potentials for VGSC activation and the refractory periods for its reactivation are lower at Purkinje cells, and are reduced by the strong depolarization. We suggest that the VGSC-mediated threshold potentials and refractory periods are regulated by synaptic inputs, and navigate the programming of sequential spikes at the neurons.

Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model

Sensory encoding in spiking neurons depends on both the integration of sensory inputs and the intrinsic dynamics and variability of spike generation. We show that the stimulus selectivity, reliability, and timing precision of primate retinal ganglion cell (RGC) light responses can be reproduced accurately with a simple model consisting of a leaky integrate-and-fire spike generator driven by a linearly filtered stimulus, a postspike current, and a Gaussian noise current. We fit model parameters for individual RGCs by maximizing the likelihood of observed spike responses to a stochastic visual stimulus. Although compact, the fitted model predicts the detailed time structure of responses to novel stimuli, accurately capturing the interaction between the spiking history and sensory stimulus selectivity. The model also accounts for the variability in responses to repeated stimuli, even when fit to data from a single (nonrepeating) stimulus sequence. Finally, the model can be used to derive an explicit, maximum-likelihood decoding rule for neural spike trains, thus providing a tool for assessing the limitations that spiking variability imposes on sensory performance.

The refractory periods and threshold potentials of sequential spikes measured by whole-cell recording

Neurons in the central nervous system are thought to program neural language via firing sequential spikes for guiding animal behaviors. The quantitative profiles of spike intrinsic properties are critically important to understand spike programming. We developed approaches with whole-cell recordings to measure the threshold potentials and refractory periods (RPs) of sequential spikes, and to analyze the relationships of these factors with spike timing precision and capacity at the regular-spiking and fast-spiking neurons in cortical slice. The RPs and threshold potentials of sequential spikes at these two groups of neurons are different and are linearly correlated with spike timing precision and capacity. These data suggest that RPs and threshold potentials essentially navigate the spike programming for the precise and loyal encoding of meaningful neural signals. Our study provides the avenues for decoding the spectrum of the neural signals quantitatively.