Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease

This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer

Spike frequency adaptation affects the synchronization properties of networks of cortical oscillators

Oscillations in many regions of the cortex have common temporal characteristics with dominant frequencies centered around the 40 Hz (gamma) frequency range and the 5–10 Hz (theta) frequency range. Experimental results also reveal spatially synchronous oscillations, which are stimulus dependent (Gray&Singer, 1987;Gray, König, Engel, & Singer, 1989; Engel, König, Kreiter, Schillen, & Singer, 1992). This rhythmic activity suggests that the coherence of neural populations is a crucial feature of cortical dynamics (Gray, 1994). Using both simulations and a theoretical coupled oscillator approach, we demonstrate that the spike frequency adaptation seen in many pyramidal cells plays a subtle but important role in the dynamics of cortical networks. Without adaptation, excitatory connections among model pyramidal cells are desynchronizing. However, the slow processes associated with adaptation encourage stable synchronous behavior

Membrane resonance enables stable and robust gamma oscillations

Neuronal mechanisms underlying beta/gamma oscillations (20-80 Hz) are not completely understood. Here, we show that in vivo beta/gamma oscillations in the cat visual cortex sometimes exhibit remarkably stable frequency even when inputs fluctuate dramatically. Enhanced frequency stability is associated with stronger oscillations measured in individual units and larger power in the local field potential. Simulations of neuronal circuitry demonstrate that membrane properties of inhibitory interneurons strongly determine the characteristics of emergent oscillations. Exploration of networks containing either integrator or resonator inhibitory interneurons revealed that: (i) Resonance, as opposed to integration, promotes robust oscillations with large power and stable frequency via a mechanism called RING (Resonance INduced Gamma); resonance favors synchronization by reducing phase delays between interneurons and imposes bounds on oscillation cycle duration; (ii) Stability of frequency and robustness of the oscillation also depend on the relative timing of excitatory and inhibitory volleys within the oscillation cycle; (iii) RING can reproduce characteristics of both Pyramidal INterneuron Gamma (PING) and INterneuron Gamma (ING), transcending such classifications; (iv) In RING, robust gamma oscillations are promoted by slow but are impaired by fast inputs. Results suggest that interneuronal membrane resonance can be an important ingredient for generation of robust gamma oscillations having stable frequency

Working memory revived in older adults by synchronizing rhythmic brain circuits

Published in final edited form as: Nat Neurosci. 2019 May ; 22(5): 820–827. doi:10.1038/s41593-019-0371-x.Understanding normal brain aging and developing methods to maintain or improve cognition in older adults are major goals of fundamental and translational neuroscience. Here we show a core feature of cognitive decline—working-memory deficits—emerges from disconnected local and long-range circuits instantiated by theta–gamma phase–amplitude coupling in temporal cortex and theta phase synchronization across frontotemporal cortex. We developed a noninvasive stimulation procedure for modulating long-range theta interactions in adults aged 60–76 years. After 25 min of stimulation, frequency-tuned to individual brain network dynamics, we observed a preferential increase in neural synchronization patterns and the return of sender–receiver relationships of information flow within and between frontotemporal regions. The end result was rapid improvement in working-memory performance that outlasted a 50 min post-stimulation period. The results provide insight into the physiological foundations of age-related cognitive impairment and contribute to groundwork for future non-pharmacological interventions targeting aspects of cognitive decline.Accepted manuscrip

Complex partial synchronization patterns in networks of delay-coupled neurons

We study the spatio-temporal dynamics of a multiplex network of delay-coupled FitzHugh–Nagumo oscillators with non-local and fractal connectivities. Apart from chimera states, a new regime of coexistence of slow and fast oscillations is found. An analytical explanation for the emergence of such coexisting partial synchronization patterns is given. Furthermore, we propose a control scheme for the number of fast and slow neurons in each layer.DFG, 163436311, SFB 910: Kontrolle selbstorganisierender nichtlinearer Systeme: Theoretische Methoden und Anwendungskonzept

A unified approach to linking experimental, statistical and computational analysis of spike train data

A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data), but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach – linking statistical, computational, and experimental neuroscience – provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.Published versio

Moving in time: simulating how neural circuits enable rhythmic enactment of planned sequences

Many complex actions are mentally pre-composed as plans that specify orderings of simpler actions. To be executed accurately, planned orderings must become active in working memory, and then enacted one-by-one until the sequence is complete. Examples include writing, typing, and speaking. In cases where the planned complex action is musical in nature (e.g. a choreographed dance or a piano melody), it appears to be possible to deploy two learned sequences at the same time, one composed from actions and a second composed from the time intervals between actions. Despite this added complexity, humans readily learn and perform rhythm-based action sequences. Notably, people can learn action sequences and rhythmic sequences separately, and then combine them with little trouble (Ullén & Bengtsson 2003). Related functional MRI data suggest that there are distinct neural regions responsible for the two different sequence types (Bengtsson et al. 2004). Although research on musical rhythm is extensive, few computational models exist to extend and inform our understanding of its neural bases. To that end, this article introduces the TAMSIN (Timing And Motor System Integration Network) model, a systems-level neural network model capable of performing arbitrary item sequences in accord with any rhythmic pattern that can be represented as a sequence of integer multiples of a base interval. In TAMSIN, two Competitive Queuing (CQ) modules operate in parallel. One represents and controls item order (the ORD module) and the second represents and controls the sequence of inter-onset-intervals (IOIs) that define a rhythmic pattern (RHY module). Further circuitry helps these modules coordinate their signal processing to enable performative output consistent with a desired beat and tempo.Accepted manuscrip

Gamma Oscillations in the Mouse Primary Visual Cortex as an Endophenotype of Schizophrenia

Gamma oscillations (20-50 Hz) are a robust component of brain activity associated with information processing, but are also part of the background spontaneous activity during various brain states including sleep and anesthesia. Our goal was to examine the changes in gamma oscillations that result from pharmacological and genetic manipulations of glutamatergic transmission which produce endophenotypes of schizophrenia. We recorded local field potentials (LFP) and single units through the depth of the mouse primary visual cortex in vivo and examined the alterations in gamma frequency activity under both normal and pathological conditions. Our results indicate that both in awake and anesthetized animals, baseline gamma frequency power in the LFP is increased throughout the cortical lamina, and the signal-to-noise ratio of gamma oscillations produced by a visual stimulus is diminished, most notably in the superficial layers. In addition, the entrainment of single units to the local oscillations in the LFP is reduced in the supragranular (L2/3) and infragranular (L5/6) layers. This work supports the hypothesis that alterations in glutamatergic transmission result in changes to gamma oscillations in primary sensory areas and is consistent with the hypothesis that these changes are associated with disrupted sensory perception