How reduction of theta rhythm by medial septum inactivation may covary with disruption of entorhinal grid cell responses due to reduced cholinergic transmission

Grid cells: the missing link in understanding Parkinson's disease?

The mechanisms underlying Parkinson's disease (PD) are complex and not fully understood, and the box-and-arrow model among other current models present significant challenges. This paper explores the potential role of the allocentric brain and especially its grid cells in several PD motor symptoms, including bradykinesia, kinesia paradoxa, freezing of gait, the bottleneck phenomenon, and their dependency on cueing. It is argued that central hubs, like the locus coeruleus and the pedunculopontine nucleus, often narrowly interpreted in the context of PD, play an equally important role in governing the allocentric brain as the basal ganglia. Consequently, the motor and secondary motor (e.g., spatially related) symptoms of PD linked with dopamine depletion may be more closely tied to erroneous computation by grid cells than to the basal ganglia alone. Because grid cells and their associated central hubs introduce both spatial and temporal information to the brain influencing velocity perception they may cause bradykinesia or hyperkinesia as well. In summary, PD motor symptoms may primarily be an allocentric disturbance resulting from virtual faulty computation by grid cells revealed by dopamine depletion in PD.

Precisely timed theta oscillations are selectively required during the encoding phase of memory

Brain oscillations have been hypothesized to support cognitive function by coordinating spike timing within and across brain regions, yet it is often not known when timing is either critical for neural computations or an epiphenomenon. The entorhinal cortex and hippocampus are necessary for learning and memory and exhibit prominent theta oscillations (6-9 Hz), which are controlled by pacemaker cells in the medial septal area. Here we show that entorhinal and hippocampal neuronal activity patterns were strongly entrained by rhythmic optical stimulation of parvalbumin-positive medial septal area neurons in mice. Despite strong entrainment, memory impairments in a spatial working memory task were not observed with pacing frequencies at or below the endogenous theta frequency and only emerged at frequencies ≥10 Hz, and specifically when pacing was targeted to maze segments where encoding occurs. Neural computations during the encoding phase were therefore selectively disrupted by perturbations of the timing of neuronal firing patterns.

A Canonical Laminar Neocortical Circuit Whose Bottom-Up, Horizontal, and Top-Down Pathways Control Attention, Learning, and Prediction

All perceptual and cognitive circuits in the human cerebral cortex are organized into layers. Specializations of a canonical laminar network of bottom-up, horizontal, and top-down pathways carry out multiple kinds of biological intelligence across different neocortical areas. This article describes what this canonical network is and notes that it can support processes as different as 3D vision and figure-ground perception; attentive category learning and decision-making; speech perception; and cognitive working memory (WM), planning, and prediction. These processes take place within and between multiple parallel cortical streams that obey computationally complementary laws. The interstream interactions that are needed to overcome these complementary deficiencies mix cell properties so thoroughly that some authors have noted the difficulty of determining what exactly constitutes a cortical stream and the differences between streams. The models summarized herein explain how these complementary properties arise, and how their interstream interactions overcome their computational deficiencies to support effective goal-oriented behaviors.

Developmental Designs and Adult Functions of Cortical Maps in Multiple Modalities: Perception, Attention, Navigation, Numbers, Streaming, Speech, and Cognition

This article unifies neural modeling results that illustrate several basic design principles and mechanisms that are used by advanced brains to develop cortical maps with multiple psychological functions. One principle concerns how brains use a strip map that simultaneously enables one feature to be represented throughout its extent, as well as an ordered array of another feature at different positions of the strip. Strip maps include circuits to represent ocular dominance and orientation columns, place-value numbers, auditory streams, speaker-normalized speech, and cognitive working memories that can code repeated items. A second principle concerns how feature detectors for multiple functions develop in topographic maps, including maps for optic flow navigation, reinforcement learning, motion perception, and category learning at multiple organizational levels. A third principle concerns how brains exploit a spatial gradient of cells that respond at an ordered sequence of different rates. Such a rate gradient is found along the dorsoventral axis of the entorhinal cortex, whose lateral branch controls the development of time cells, and whose medial branch controls the development of grid cells. Populations of time cells can be used to learn how to adaptively time behaviors for which a time interval of hundreds of milliseconds, or several seconds, must be bridged, as occurs during trace conditioning. Populations of grid cells can be used to learn hippocampal place cells that represent the large spaces in which animals navigate. A fourth principle concerns how and why all neocortical circuits are organized into layers, and how functionally distinct columns develop in these circuits to enable map development. A final principle concerns the role of Adaptive Resonance Theory top-down matching and attentional circuits in the dynamic stabilization of early development and adult learning. Cortical maps are modeled in visual, auditory, temporal, parietal, prefrontal, entorhinal, and hippocampal cortices.

Cannabinoid receptor-mediated disruption of sensory gating and neural oscillations: A translational study in rats and humans

Cannabis use has been associated with altered sensory gating and neural oscillations. However, it is unclear which constituent in cannabis is responsible for these effects, or whether these are cannabinoid receptor 1 (CB1R) mediated. Therefore, the present study in humans and rats examined whether cannabinoid administration would disrupt sensory gating and evoked oscillations utilizing electroencephalography (EEG) and local field potentials (LFPs), respectively. Human subjects (n = 15) completed four test days during which they received intravenous delta-9-tetrahydrocannabinol (Δ⁹-THC), cannabidiol (CBD), Δ⁹-THC + CBD, or placebo. Subjects engaged in a dual-click paradigm, and outcome measures included P50 gating ratio (S2/S1) and evoked power to S1 and S2. In order to examine CB1R specificity, rats (n = 6) were administered the CB1R agonist CP-55940, CP-55940+AM-251 (a CB1R antagonist), or vehicle using the same paradigm. LFPs were recorded from CA3 and entorhinal cortex. Both Δ⁹-THC (p < 0.007) and Δ⁹-THC + CBD (p < 0.004) disrupted P50 gating ratio compared to placebo, while CBD alone had no effect. Δ⁹-THC (p < 0.048) and Δ⁹-THC + CBD (p < 0.035) decreased S1 evoked theta power, and in the Δ⁹-THC condition, S1 theta negatively correlated with gating ratios (r = -0.629, p < 0.012 (p < 0.048 adjusted)). In rats, CP-55940 disrupted gating in both brain regions (p < 0.0001), and this was reversed by AM-251. Further, CP-55940 decreased evoked theta (p < 0.0077) and gamma (p < 0.011) power to S1, which was partially blocked by AM-251. These convergent human/animal data suggest that CB1R agonists disrupt sensory gating by altering neural oscillations in the theta-band. Moreover, this suggests that the endocannabinoid system mediates theta oscillations relevant to perception and cognition.

Single-unit Activity in the in vitro Entorhinal Cortex During Carbachol-induced Field Oscillations

The muscarinic receptor agonist carbachol (CCh) can induce activity in the theta range (4-15 Hz) in the entorhinal cortex (EC), but the underlying network mechanisms remain unclear. Here, we investigated the interplay between interneurons and principal cells in the EC during CCh-induced theta-like field oscillations in an in vitro brain slice preparation using tetrodes. Field oscillations at 10.1 Hz (IQR = 9.5-10.9 Hz) occurred during bath application of CCh (100 μM; n = 32 experiments) and were associated with single-unit (n = 189) firing. Interneuron activity increased before principal cell activity at the onset of the oscillations and both interneurons and principal cells fired at specific oscillation phases with interneurons preceding principal cells, suggesting that interneurons modulate principal cell activity during such oscillations. The regularity of occurrence of CCh-induced oscillations was abolished by applying the GABA_A receptor antagonist picrotoxin (100 μM; n = 13). These effects were accompanied by changes in firing with principal cells discharging action potentials before interneurons, along with a loss of preferred firing phase for principal cells in relation to the oscillation peaks. Blocking ionotropic glutamatergic transmission abolished CCh-induced field oscillations (n = 6), suggesting that ionotropic glutamatergic receptor signaling is necessary for their generation. Our results show that neuronal network interactions leading to CCh-induced theta-like field oscillations rest on the close interplay between interneurons and principal cells and that interneurons modulate principal cell activity during such oscillatory activity. Moreover, they underscore the role of ionotropic glutamatergic transmission in this type of oscillations.

Acetylcholine Neuromodulation in Normal and Abnormal Learning and Memory: Vigilance Control in Waking, Sleep, Autism, Amnesia and Alzheimer's Disease

Adaptive Resonance Theory, or ART, is a neural model that explains how normal and abnormal brains may learn to categorize and recognize objects and events in a changing world, and how these learned categories may be remembered for a long time. This article uses ART to propose and unify the explanation of diverse data about normal and abnormal modulation of learning and memory by acetylcholine (ACh). In ART, vigilance control determines whether learned categories will be general and abstract, or specific and concrete. ART models how vigilance may be regulated by ACh release in layer 5 neocortical cells by influencing after-hyperpolarization (AHP) currents. This phasic ACh release is mediated by cells in the nucleus basalis (NB) of Meynert that are activated by unexpected events. The article additionally discusses data about ACh-mediated tonic control of vigilance. ART proposes that there are often dynamic breakdowns of tonic control in mental disorders such as autism, where vigilance remains high, and medial temporal amnesia, where vigilance remains low. Tonic control also occurs during sleep-wake cycles. Properties of Up and Down states during slow wave sleep arise in ACh-modulated laminar cortical ART circuits that carry out processes in awake individuals of contrast normalization, attentional modulation, decision-making, activity-dependent habituation, and mismatch-mediated reset. These slow wave sleep circuits interact with circuits that control circadian rhythms and memory consolidation. Tonic control properties also clarify how Alzheimer's disease symptoms follow from a massive structural degeneration that includes undermining vigilance control by ACh in cortical layers 3 and 5. Sleep disruptions before and during Alzheimer's disease, and how they contribute to a vicious cycle of plaque formation in layers 3 and 5, are also clarified from this perspective.

An update of the classical and novel methods used for measuring fast neurotransmitters during normal and brain altered function

To understand better the cerebral functions, several methods have been developed to study the brain activity, they could be related with morphological, electrophysiological, molecular and neurochemical techniques. Monitoring neurotransmitter concentration is a key role to know better how the brain works during normal or pathological conditions, as well as for studying the changes in neurotransmitter concentration with the use of several drugs that could affect or reestablish the normal brain activity. Immediate response of the brain to environmental conditions is related with the release of the fast acting neurotransmission by glutamate (Glu), γ-aminobutyric acid (GABA) and acetylcholine (ACh) through the opening of ligand-operated ion channels. Neurotransmitter release is mainly determined by the classical microdialysis technique, this is generally coupled to high performance liquid chromatography (HPLC). Detection of neurotransmitters can be done by fluorescence, optical density, electrochemistry or other detection systems more sophisticated. Although the microdialysis method is the golden technique to monitor the brain neurotransmitters, it has a poor temporal resolution. Recently, with the use of biosensor the drawback of temporal resolution has been improved considerably, however other inconveniences have merged, such as stability, reproducibility and the lack of reliable biosensors mainly for GABA. The aim of this review is to show the important advances in the different ways to measure neurotransmitter concentrations; both with the use of classic techniques as well as with the novel methods and alternant approaches to improve the temporal resolution.

Real-time learning of predictive recognition categories that chunk sequences of items stored in working memory

How are sequences of events that are temporarily stored in a cognitive working memory unitized, or chunked, through learning? Such sequential learning is needed by the brain in order to enable language, spatial understanding, and motor skills to develop. In particular, how does the brain learn categories, or list chunks, that become selectively tuned to different temporal sequences of items in lists of variable length as they are stored in working memory, and how does this learning process occur in real time? The present article introduces a neural model that simulates learning of such list chunks. In this model, sequences of items are temporarily stored in an Item-and-Order, or competitive queuing, working memory before learning categorizes them using a categorization network, called a Masking Field, which is a self-similar, multiple-scale, recurrent on-center off-surround network that can weigh the evidence for variable-length sequences of items as they are stored in the working memory through time. A Masking Field hereby activates the learned list chunks that represent the most predictive item groupings at any time, while suppressing less predictive chunks. In a network with a given number of input items, all possible ordered sets of these item sequences, up to a fixed length, can be learned with unsupervised or supervised learning. The self-similar multiple-scale properties of Masking Fields interacting with an Item-and-Order working memory provide a natural explanation of George Miller's Magical Number Seven and Nelson Cowan's Magical Number Four. The article explains why linguistic, spatial, and action event sequences may all be stored by Item-and-Order working memories that obey similar design principles, and thus how the current results may apply across modalities. Item-and-Order properties may readily be extended to Item-Order-Rank working memories in which the same item can be stored in multiple list positions, or ranks, as in the list ABADBD. Comparisons with other models, including TRACE, MERGE, and TISK, are made.

How does the modular organization of entorhinal grid cells develop?

The entorhinal-hippocampal system plays a crucial role in spatial cognition and navigation. Since the discovery of grid cells in layer II of medial entorhinal cortex (MEC), several types of models have been proposed to explain their development and operation; namely, continuous attractor network models, oscillatory interference models, and self-organizing map (SOM) models. Recent experiments revealing the in vivo intracellular signatures of grid cells (Domnisoru et al., 2013; Schmidt-Heiber and Hausser, 2013), the primarily inhibitory recurrent connectivity of grid cells (Couey et al., 2013; Pastoll et al., 2013), and the topographic organization of grid cells within anatomically overlapping modules of multiple spatial scales along the dorsoventral axis of MEC (Stensola et al., 2012) provide strong constraints and challenges to existing grid cell models. This article provides a computational explanation for how MEC cells can emerge through learning with grid cell properties in modular structures. Within this SOM model, grid cells with different rates of temporal integration learn modular properties with different spatial scales. Model grid cells learn in response to inputs from multiple scales of directionally-selective stripe cells (Krupic et al., 2012; Mhatre et al., 2012) that perform path integration of the linear velocities that are experienced during navigation. Slower rates of grid cell temporal integration support learned associations with stripe cells of larger scales. The explanatory and predictive capabilities of the three types of grid cell models are comparatively analyzed in light of recent data to illustrate how the SOM model overcomes problems that other types of models have not yet handled.

Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales, attention and oscillations

A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells can develop by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. The model's parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same SOM mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus (HC) may use mechanisms homologous to those for temporal learning through lateral entorhinal cortex to HC ('neural relativity'). The model clarifies how top-down HC-to-entorhinal attentional mechanisms may stabilize map learning, simulates how hippocampal inactivation may disrupt grid cells, and explains data about theta, beta and gamma oscillations. The article also compares the three main types of grid cell models in the light of recent data.