Li+ and muscarine cooperatively enhance the cationic tail current in rat cortical pyramidal cells

Recurrent network models for perfect temporal integration of fluctuating correlated inputs

Temporal integration of input is essential to the accumulation of information in various cognitive and behavioral processes, and gradually increasing neuronal activity, typically occurring within a range of seconds, is considered to reflect such computation by the brain. Some psychological evidence suggests that temporal integration by the brain is nearly perfect, that is, the integration is non-leaky, and the output of a neural integrator is accurately proportional to the strength of input. Neural mechanisms of perfect temporal integration, however, remain largely unknown. Here, we propose a recurrent network model of cortical neurons that perfectly integrates partially correlated, irregular input spike trains. We demonstrate that the rate of this temporal integration changes proportionately to the probability of spike coincidences in synaptic inputs. We analytically prove that this highly accurate integration of synaptic inputs emerges from integration of the variance of the fluctuating synaptic inputs, when their mean component is kept constant. Highly irregular neuronal firing and spike coincidences are the major features of cortical activity, but they have been separately addressed so far. Our results suggest that the efficient protocol of information integration by cortical networks essentially requires both features and hence is heterotic.

Spikes are the words that neurons use for communicating with one another through their networks. While individual cortical neurons generate highly irregular spike trains, coincidently arriving spikes are considered to exert a strong impact on postsynaptic-cell firing and hence to play an active role in neural information processing. However, little is known about whether computations by the brain benefit from such coincident spikes. Here, we show in a recurrent network model that coincident spikes embedded in random spike trains provide a neural code useful for highly accurate temporal integration of external input. In fact, the proposed neural integration is almost perfectly accurate in the mathematical sense. A wide range of cognitive behavior relies on temporal integration. For instance, it is a central player in sensory discrimination tasks and interval timing perception. Our model provides the neural basis for quantitative understanding of animal's decision behavior. In addition, it may account for why cortical activity shows a heterotic feature with irregular firing and synchronous spikes.

Temporal integration by stochastic recurrent network dynamics with bimodal neurons

Temporal integration of externally or internally driven information is required for a variety of cognitive processes. This computation is generally linked with graded rate changes in cortical neurons, which typically appear during a delay period of cognitive task in the prefrontal and other cortical areas. Here, we present a neural network model to produce graded (climbing or descending) neuronal activity. Model neurons are interconnected randomly by AMPA-receptor-mediated fast excitatory synapses and are subject to noisy background excitatory and inhibitory synaptic inputs. In each neuron, a prolonged afterdepolarizing potential follows every spike generation. Then, driven by an external input, the individual neurons display bimodal rate changes between a baseline state and an elevated firing state, with the latter being sustained by regenerated afterdepolarizing potentials. When the variance of background input and the uniform weight of recurrent synapses are adequately tuned, we show that stochastic noise and reverberating synaptic input organize these bimodal changes into a sequence that exhibits graded population activity with a nearly constant slope. To test the validity of the proposed mechanism, we analyzed the graded activity of anterior cingulate cortex neurons in monkeys performing delayed conditional Go/No-go discrimination tasks. The delay-period activities of cingulate neurons exhibited bimodal activity patterns and trial-to-trial variability that are similar to those predicted by the proposed model.

A distinct form of calcium release down-regulates membrane excitability in neocortical pyramidal cells

We reported a novel type of calcium release from inositol-1,4,5-trisphosphate (IP(3))-sensitive calcium stores synergistically induced by muscarinic acetylcholine receptor (mAchR)-mediated increase in IP(3) and action potential-induced calcium influx (IP(3)-assisted calcium-induced calcium release, IP(3)-assisted CICR). To clarify its functional significance, the effects of IP(3)-assisted CICR on spike-frequency adaptation were examined in layer II/III neurons from rat visual cortex slices. IP(3)-assisted CICR was enabled with a high concentration of the mAchR agonist carbachol (10 microM). The magnitude of this CICR was the more augmented at higher firing frequencies. With 10 microM carbachol, spike-frequency adaptation was reduced for spike trains at 'low' firing frequencies (6-10 Hz), but was rather enhanced at 'high' firing rates (16-22 Hz): excitability was down-regulated at 'high' frequencies. With 1 microM carbachol, by contrast, IP(3)-assisted CICR failed to occur, and spike-frequency adaptation was always reduced at any spike frequencies. Intracellular injection of the IP(3) receptor blocker heparin prevented both the mAchR-mediated occurrence of IP(3)-assisted CICR and enhancement of spike-frequency adaptation with 10 microM carbachol. Both of these mAchR-mediated effects were reproduced by intracellular IP(3) injection, and were shown to be associated with each other by simultaneous recordings of membrane potential and intracellular calcium increase. We propose that IP(3)-assisted CICR offers a novel way to protect these cortical neurons from hyperexcitability and presumably from excitotoxic cell death.

Synchronized population oscillation of excitatory synaptic potentials dependent of calcium-induced calcium release in rat neocortex layer II/III neurons

We examined the roles played by calcium-induced calcium release from ryanodine-sensitive calcium stores in induction of neocortical membrane potential oscillation by using caffeine, an agonist of ryanodine receptors. Intracellular recordings were made from neurons in layer II/III of rat visual cortex slices in a caffeine-containing medium. White matter stimulation initially evoked monophasic synaptic potentials. As low-frequency stimulation continued for over 10 min, an oscillating synaptic potential gradually became evoked, in which a paroxysmal depolarization shift was followed by a 8-10-Hz train of several depolarizing wavelets. This oscillating potential was not induced in a medium containing no caffeine with 2 or 0.5 mM [Mg2+](o). Under blockade of N-methyl-D-aspartate receptors, induction of this oscillating potential failed even with caffeine application. Experiments with the calcium store depletor, thapsigargin, revealed that this oscillating potential is induced in a manner dependent on intracellular calcium release. Dual intracellular recordings revealed that the oscillation was synchronized in pairs of layer II/III neurons. The oscillating potential was detectable by field potential recordings also, suggesting that the present oscillation seems to reflect a network property.