Neuromodulation via muscarinic acetylcholine pathway can facilitate distinct, complementary, and sequential roles for NREM and REM states during sleep-dependent memory consolidation.

Across vertebrate species, sleep consists of repeating cycles of NREM followed by REM. However, the respective functions of NREM, REM, and their stereotypic cycling pattern are not well understood. Using a simplified biophysical network model, we show that NREM and REM sleep can play differential and critical roles in memory consolidation primarily regulated, based on state-specific changes in cholinergic signaling. Within this network, decreasing and increasing muscarinic acetylcholine (ACh) signaling during bouts of NREM and REM, respectively, differentially alters neuronal excitability and excitatory/inhibitory balance. During NREM, deactivation of inhibitory neurons leads to network-wide disinhibition and bursts of synchronized activity led by firing in engram neurons. These features strengthen connections from the original engram neurons to less-active network neurons. In contrast, during REM, an increase in network inhibition suppresses firing in all but the most-active excitatory neurons, leading to competitive strengthening/pruning of the memory trace. We tested the predictions of the model against in vivo recordings from mouse hippocampus during active sleep-dependent memory storage. Consistent with modeling results, we find that functional connectivity between CA1 neurons changes differentially at transition from NREM to REM sleep during learning. Returning to the model, we find that an iterative sequence of state-specific activations during NREM/REM cycling is essential for memory storage in the network, serving a critical role during simultaneous consolidation of multiple memories. Together these results provide a testable mechanistic hypothesis for the respective roles of NREM and REM sleep, and their universal relative timing, in memory consolidation. Significance statement: Using a simplified computational model and in vivo recordings from mouse hippocampus, we show that NREM and REM sleep can play differential roles in memory consolidation. The specific neurophysiological features of the two sleep states allow for expansion of memory traces (during NREM) and prevention of overlap between different memory traces (during REM). These features are likely essential in the context of storing more than one new memory simultaneously within a brain network.

Acetylcholine Mediates Dynamic Switching Between Information Coding Schemes in Neuronal Networks.

Rate coding and phase coding are the two major coding modes seen in the brain. For these two modes, network dynamics must either have a wide distribution of frequencies for rate coding, or a narrow one to achieve stability in phase dynamics for phase coding. Acetylcholine (ACh) is a potent regulator of neural excitability. Acting through the muscarinic receptor, ACh reduces the magnitude of the potassium M-current, a hyperpolarizing current that builds up as neurons fire. The M-current contributes to several excitability features of neurons, becoming a major player in facilitating the transition between Type 1 (integrator) and Type 2 (resonator) excitability. In this paper we argue that this transition enables a dynamic switch between rate coding and phase coding as levels of ACh release change. When a network is in a high ACh state variations in synaptic inputs will lead to a wider distribution of firing rates across the network and this distribution will reflect the network structure or pattern of external input to the network. When ACh is low, network frequencies become narrowly distributed and the structure of a network or pattern of external inputs will be represented through phase relationships between firing neurons. This work provides insights into how modulation of neuronal features influences network dynamics and information processing across brain states.

Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks.

Network oscillations across and within brain areas are critical for learning and performance of memory tasks. While a large amount of work has focused on the generation of neural oscillations, their effect on neuronal populations' spiking activity and information encoding is less known. Here, we use computational modeling to demonstrate that a shift in resonance responses can interact with oscillating input to ensure that networks of neurons properly encode new information represented in external inputs to the weights of recurrent synaptic connections. Using a neuronal network model, we find that due to an input current-dependent shift in their resonance response, individual neurons in a network will arrange their phases of firing to represent varying strengths of their respective inputs. As networks encode information, neurons fire more synchronously, and this effect limits the extent to which further "learning" (in the form of changes in synaptic strength) can occur. We also demonstrate that sequential patterns of neuronal firing can be accurately stored in the network; these sequences are later reproduced without external input (in the context of subthreshold oscillations) in both the forward and reverse directions (as has been observed following learning in vivo). To test whether a similar mechanism could act in vivo, we show that periodic stimulation of hippocampal neurons coordinates network activity and functional connectivity in a frequency-dependent manner. We conclude that resonance with subthreshold oscillations provides a plausible network-level mechanism to accurately encode and retrieve information without overstrengthening connections between neurons.

Connexin 43 gap junctions contribute to brain endothelial barrier hyperpermeability in familial cerebral cavernous malformations type III by modulating tight junction structure.

Familial cerebral cavernous malformations type III (fCCM3) is a disease of the cerebrovascular system caused by loss-of-function mutations in ccm3 that result in dilated capillary beds that are susceptible to hemorrhage. Before hemorrhage, fCCM3 lesions are characterized by a hyperpermeable blood-brain barrier (BBB), the key pathologic feature of fCCM3. We demonstrate that connexin 43 (Cx43), a gap junction (GJ) protein that is incorporated into the BBB junction complex, is up-regulated in lesions of a murine model of fCCM3. Small interfering RNA-mediated ccm3 knockdown (CCM3KD) in brain endothelial cells in vitro increased Cx43 protein expression, GJ plaque size, GJ intracellular communication (GJIC), and barrier permeability. CCM3KD hyperpermeability was rescued by GAP27, a peptide gap junction and hemichannel inhibitor of Cx43 GJIC. Tight junction (TJ) protein, zonula occludens 1 (ZO-1), accumulated at Cx43 GJs in CCM3KD cells and displayed fragmented staining at TJs. The GAP27-mediated inhibition of Cx43 GJs in CCM3KD cells restored ZO-1 to TJ structures and reduced plaque accumulation at Cx43 GJs. The TJ protein, Claudin-5, was also fragmented at TJs in CCM3KD cells, and GAP27 treatment lengthened TJ-associated fragments and increased Claudin 5-Claudin 5 transinteraction. Overall, we demonstrate that Cx43 GJs are aberrantly increased in fCCM3 and regulate barrier permeability by a TJ-dependent mechanism.-Johnson, A. M., Roach, J. P., Hu, A., Stamatovic, S. M., Zochowski, M. R., Keep, R. F., Andjelkovic, A. V. Connexin 43 gap junctions contribute to brain endothelial barrier hyperpermeability in familial cerebral cavernous malformations type III by modulating tight junction structure.

Interplay between excitability type and distributions of neuronal connectivity determines neuronal network synchronization.

While the interplay between neuronal excitability properties and global properties of network topology is known to affect network propensity for synchronization, it is not clear how detailed characteristics of these properties affect spatiotemporal pattern formation. Here we study mixed networks, composed of neurons having type I and/or type II phase response curves, with varying distributions of local and random connections and show that not only average network properties, but also the connectivity distribution statistics, significantly affect network synchrony. Namely, we study networks with fixed networkwide properties, but vary the number of random connections that nodes project. We show that varying node excitability (type I vs type II) influences network synchrony most dramatically for systems with long-tailed distributions of the number of random connections per node. This indicates that a cluster of even a few highly rewired cells with a high propensity for synchronization can alter the degree of synchrony in the network as a whole. We show this effect generally on a network of coupled Kuramoto oscillators and investigate the impact of this effect more thoroughly in pulse-coupled networks of biophysical neurons.

Memory recall and spike-frequency adaptation.

The brain can reproduce memories from partial data; this ability is critical for memory recall. The process of memory recall has been studied using autoassociative networks such as the Hopfield model. This kind of model reliably converges to stored patterns that contain the memory. However, it is unclear how the behavior is controlled by the brain so that after convergence to one configuration, it can proceed with recognition of another one. In the Hopfield model, this happens only through unrealistic changes of an effective global temperature that destabilizes all stored configurations. Here we show that spike-frequency adaptation (SFA), a common mechanism affecting neuron activation in the brain, can provide state-dependent control of pattern retrieval. We demonstrate this in a Hopfield network modified to include SFA, and also in a model network of biophysical neurons. In both cases, SFA allows for selective stabilization of attractors with different basins of attraction, and also for temporal dynamics of attractor switching that is not possible in standard autoassociative schemes. The dynamics of our models give a plausible account of different sorts of memory retrieval.

Formation and Dynamics of Waves in a Cortical Model of Cholinergic Modulation.

Acetylcholine (ACh) is a regulator of neural excitability and one of the neurochemical substrates of sleep. Amongst the cellular effects induced by cholinergic modulation are a reduction in spike-frequency adaptation (SFA) and a shift in the phase response curve (PRC). We demonstrate in a biophysical model how changes in neural excitability and network structure interact to create three distinct functional regimes: localized asynchronous, traveling asynchronous, and traveling synchronous. Our results qualitatively match those observed experimentally. Cortical activity during slow wave sleep (SWS) differs from that during REM sleep or waking states. During SWS there are traveling patterns of activity in the cortex; in other states stationary patterns occur. Our model is a network composed of Hodgkin-Huxley type neurons with a M-current regulated by ACh. Regulation of ACh level can account for dynamical changes between functional regimes. Reduction of the magnitude of this current recreates the reduction in SFA the shift from a type 2 to a type 1 PRC observed in the presence of ACh. When SFA is minimal (in waking or REM sleep state, high ACh) patterns of activity are localized and easily pinned by network inhomogeneities. When SFA is present (decreasing ACh), traveling waves of activity naturally arise. A further decrease in ACh leads to a high degree of synchrony within traveling waves. We also show that the level of ACh determines how sensitive network activity is to synaptic heterogeneity. These regimes may have a profound functional significance as stationary patterns may play a role in the proper encoding of external input as memory and traveling waves could lead to synaptic regularization, giving unique insights into the role and significance of ACh in determining patterns of cortical activity and functional differences arising from the patterns.

From network structure to network reorganization: implications for adult neurogenesis.

Networks can be dynamical systems that undergo functional and structural reorganization. One example of such a process is adult hippocampal neurogenesis, in which new cells are continuously born and incorporate into the existing network of the dentate gyrus region of the hippocampus. Many of these introduced cells mature and become indistinguishable from established neurons, joining the existing network. Activity in the network environment is known to promote birth, survival and incorporation of new cells. However, after epileptogenic injury, changes to the connectivity structure around the neurogenic niche are known to correlate with aberrant neurogenesis. The possible role of network-level changes in the development of epilepsy is not well understood. In this paper, we use a computational model to investigate how the structural and functional outcomes of network reorganization, driven by addition of new cells during neurogenesis, depend on the original network structure. We find that there is a stable network topology that allows the network to incorporate new neurons in a manner that enhances activity of the persistently active region, but maintains global network properties. In networks having other connectivity structures, new cells can greatly alter the distribution of firing activity and destroy the initial activity patterns. We thus find that new cells are able to provide focused enhancement of network only for small-world networks with sufficient inhibition. Network-level deviations from this topology, such as those caused by epileptogenic injury, can set the network down a path that develops toward pathological dynamics and aberrant structural integration of new cells.

Synthesis, spectra, delivery and potentiometric responses of new styryl dyes with extended spectral ranges.

Styryl dyes have been among the most widely used probes for mapping membrane potential changes in excitable cells. However, their utility has been somewhat limited because their excitation wavelengths have been restricted to the 450-550 nm range. Longer wavelength probes can minimize interference from endogenous chromophores and, because of decreased light scattering, improve recording from deep within tissue. In this paper we report on our efforts to develop new potentiometric styryl dyes that have excitation wavelengths ranging above 700 nm and emission spectra out to 900 nm. We have prepared and characterized dyes based on 47 variants of the styryl chromophores. Voltage-dependent spectral changes have been recorded for these dyes in a model lipid bilayer and from lobster nerves. The voltage sensitivities of the fluorescence of many of these new potentiometric indicators are as good as those of the widely used ANEP series of probes. In addition, because some of the dyes are often poorly water soluble, we have developed cyclodextrin complexes of the dyes to serve as efficient delivery vehicles. These dyes promise to enable new experimental paradigms for in vivo imaging of membrane potential.

Oscillations in the olfactory bulb carry information about odorant history.

While odorant-evoked oscillations in the vertebrate olfactory bulb have been studied extensively, information about their possible cognitive role has been missing. Using voltage-sensitive dye imaging, we show that repeated odorant presentations with interstimulus intervals of 2-12 s had dramatic and diverse effects on the three oscillations that occur in the turtle olfactory bulb. Two of the oscillations are strikingly depressed in response to the second stimulation even of a new odorant was presented. The third oscillation is enhanced if the odorant is the same but suppressed if the odorant is new. The effects suggest that the oscillations carry information about odorant novelty and consistency.

Odorant specificity of three oscillations and the DC signal in the turtle olfactory bulb.

The odour-induced population response in the in vivo turtle (Terepene sp.) olfactory bulb consists of three oscillatory components (rostral, middle and caudal) that ride on top of a DC signal. In an initial step to determine the functional role of these four signals, we compared the signals elicited by different odorants. Most experiments compared isoamyl acetate and cineole, odorants which have very different maps of input to olfactory bulb glomeruli in the turtle and a different perceptual quality for humans. We found substantial differences in the response to the two odours in the rise-time of the DC signal and in the latency of the middle oscillation. The rate of rise for cineole was twice as fast as that for isoamyl acetate. Similarly, the latency for the middle oscillation was about twice as long for isoamyl acetate as it was for cineole. On the other hand, a number of characteristics of the signals were not substantially different for the two odorants. These included the latency of the rostral and caudal oscillation, the frequency and envelope of all three oscillations and their locations and spatial extents. A smaller number of experiments were carried out with hexanone and hexanal; the oscillations elicited by these odorants did not appear to be different from those elicited by isoamyl acetate and cineole. Qualitative differences between the oscillations in the turtle and those in two invertebrate phyla suggest that different odour processing strategies may be used.

Imaging nervous system activity with voltage-sensitive dyes.

Optical recording with a voltage-sensitive dye is advantageous where membrane potential must be recorded in many sites at once. This unit describes methods for making voltage-sensitive dye measurements on different preparations to study (1) how a neuron integrates its synaptic input into its action potential output by measuring membrane potential everywhere synaptic input occurs and where spikes are initiated; (2) how a nervous system generates a behavior in Aplysia abdominal ganglion; and (3) responses to sensory stimuli and generation of motor output in the vertebrate brain by simultaneous measurement of population signals from many areas. The approach is three-pronged: (1) find the dye with the largest signal-to-noise ratio; (2) reduce extraneous sources of noise; and (3) maximize the number of photons measured to reduce the relative shot noise. A discussion of optical recording methods including the choice of dyes, light sources, optics, cameras, and minimizing noise is also provided.

Distributed and concentration-invariant spatial representations of odorants by receptor neuron input to the turtle olfactory bulb.

We sought to characterize how odorants are represented at the level of afferent input to the olfactory bulb of the box turtle, a terrestrial reptile that, like mammals, detects airborne odorants. Using methods developed first in zebrafish, we selectively labeled olfactory receptor neurons with Calcium Green-1 dextran and imaged odorant-evoked input to glomeruli in vivo. Odorant representations were imaged at a glomerular level of resolution over a portion of the dorsal olfactory bulb and at a regional level of resolution over the entire dorsal surface. We report two new findings. First, even at low concentrations, odorants typically elicited input to a large fraction of all imaged glomeruli. Second, while the amplitude of the odorant-evoked input to glomeruli was concentration dependent, the relative pattern of input to the bulb changed only slightly over a concentration range of up to three log units. These results suggest the hypothesis that odorant representations in the turtle involve differential levels of input to many glomeruli, and that detecting relative patterns of distributed glomerular activation may be an important strategy for encoding odor quality independent of intensity.