Predictive learning by a burst-dependent learning rule.

Humans and other animals are able to quickly generalize latent dynamics of spatiotemporal sequences, often from a minimal number of previous experiences. Additionally, internal representations of external stimuli must remain stable, even in the presence of sensory noise, in order to be useful for informing behavior. In contrast, typical machine learning approaches require many thousands of samples, and generalize poorly to unexperienced examples, or fail completely to predict at long timescales. Here, we propose a novel neural network module which incorporates hierarchy and recurrent feedback terms, constituting a simplified model of neocortical microcircuits. This microcircuit predicts spatiotemporal trajectories at the input layer using a temporal error minimization algorithm. We show that this module is able to predict with higher accuracy into the future compared to traditional models. Investigating this model we find that successive predictive models learn representations which are increasingly removed from the raw sensory space, namely as successive temporal derivatives of the positional information. Next, we introduce a spiking neural network model which implements the rate-model through the use of a recently proposed biological learning rule utilizing dual-compartment neurons. We show that this network performs well on the same tasks as the mean-field models, by developing intrinsic dynamics that follow the dynamics of the external stimulus, while coordinating transmission of higher-order dynamics. Taken as a whole, these findings suggest that hierarchical temporal abstraction of sequences, rather than feed-forward reconstruction, may be responsible for the ability of neural systems to quickly adapt to novel situations.

Adaptive integration of self-motion and goals in posterior parietal cortex.

Rats readily switch between foraging and more complex navigational behaviors such as pursuit of other rats or prey. These tasks require vastly different tracking of multiple behaviorally significant variables including self-motion state. To explore whether navigational context modulates self-motion tracking, we examined self-motion tuning in posterior parietal cortex neurons during foraging versus visual target pursuit. Animals performing the pursuit task demonstrate predictive processing of target trajectories by anticipating and intercepting them. Relative to foraging, pursuit yields multiplicative gain modulation of self-motion tuning and enhances self-motion state decoding. Self-motion sensitivity in parietal cortex neurons is, on average, history dependent regardless of behavioral context, but the temporal window of self-motion integration extends during target pursuit. Finally, many self-motion-sensitive neurons conjunctively track the visual target position relative to the animal. Thus, posterior parietal cortex functions to integrate the location of navigationally relevant target stimuli into an ongoing representation of past, present, and future locomotor trajectories.

Neural responses in retrosplenial cortex associated with environmental alterations.

The retrosplenial cortex (RSC) is an area interconnected with regions of the brain that display spatial correlates. Neurons in connected regions may encode an animal's position in the environment and location or proximity to objects or boundaries. RSC has also been shown to be important for spatial memory, such as tracking distance from and between landmarks, contextual information, and orientation within an environment. For these reasons, it is important to determine how neurons in RSC represent cues such as objects or boundaries and their relationship to the environment. In the current work, we performed electrophysiological recordings in RSC, whereas rats foraged in arenas that could contain an object or in which the environment was altered. We report RSC neurons display changes in mean firing rate responding to alterations of the environment. These alterations include the arena rotating, changing size or shape, or an object being introduced into the arena.

The Unexplored Territory of Neural Models: Potential Guides for Exploring the Function of Metabotropic Neuromodulation.

The space of possible neural models is enormous and under-explored. Single cell computational neuroscience models account for a range of dynamical properties of membrane potential, but typically do not address network function. In contrast, most models focused on network function address the dimensions of excitatory weight matrices and firing thresholds without addressing the complexities of metabotropic receptor effects on intrinsic properties. There are many under-explored dimensions of neural parameter space, and the field needs a framework for representing what has been explored and what has not. Possible frameworks include maps of parameter spaces, or efforts to categorize the fundamental elements and molecules of neural circuit function. Here we review dimensions that are under-explored in network models that include the metabotropic modulation of synaptic plasticity and presynaptic inhibition, spike frequency adaptation due to calcium-dependent potassium currents, and afterdepolarization due to calcium-sensitive non-specific cation currents and hyperpolarization activated cation currents. Neuroscience research should more effectively explore possible functional models incorporating under-explored dimensions of neural function.

Neurophysiological coding of space and time in the hippocampus, entorhinal cortex, and retrosplenial cortex.

Neurophysiological recordings in behaving rodents demonstrate neuronal response properties that may code space and time for episodic memory and goal-directed behaviour. Here, we review recordings from hippocampus, entorhinal cortex, and retrosplenial cortex to address the problem of how neurons encode multiple overlapping spatiotemporal trajectories and disambiguate these for accurate memory-guided behaviour. The solution could involve neurons in the entorhinal cortex and hippocampus that show mixed selectivity, coding both time and location. Some grid cells and place cells that code space also respond selectively as time cells, allowing differentiation of time intervals when a rat runs in the same location during a delay period. Cells in these regions also develop new representations that differentially code the context of prior or future behaviour allowing disambiguation of overlapping trajectories. Spiking activity is also modulated by running speed and head direction, supporting the coding of episodic memory not as a series of snapshots but as a trajectory that can also be distinguished on the basis of speed and direction. Recent data also address the mechanisms by which sensory input could distinguish different spatial locations. Changes in firing rate reflect running speed on long but not short time intervals, and few cells code movement direction, arguing against path integration for coding location. Instead, new evidence for neural coding of environmental boundaries in egocentric coordinates fits with a modelling framework in which egocentric coding of barriers combined with head direction generates distinct allocentric coding of location. The egocentric input can be used both for coding the location of spatiotemporal trajectories and for retrieving specific viewpoints of the environment. Overall, these different patterns of neural activity can be used for encoding and disambiguation of prior episodic spatiotemporal trajectories or for planning of future goal-directed spatiotemporal trajectories.

Egocentric boundary vector tuning of the retrosplenial cortex.

The retrosplenial cortex is reciprocally connected with multiple structures implicated in spatial cognition, and damage to the region itself produces numerous spatial impairments. Here, we sought to characterize spatial correlates of neurons within the region during free exploration in two-dimensional environments. We report that a large percentage of retrosplenial cortex neurons have spatial receptive fields that are active when environmental boundaries are positioned at a specific orientation and distance relative to the animal itself. We demonstrate that this vector-based location signal is encoded in egocentric coordinates, is localized to the dysgranular retrosplenial subregion, is independent of self-motion, and is context invariant. Further, we identify a subpopulation of neurons with this response property that are synchronized with the hippocampal theta oscillation. Accordingly, the current work identifies a robust egocentric spatial code in retrosplenial cortex that can facilitate spatial coordinate system transformations and support the anchoring, generation, and utilization of allocentric representations.

Neuronal representation of environmental boundaries in egocentric coordinates.

Movement through space is a fundamental behavior for all animals. Cognitive maps of environments are encoded in the hippocampal formation in an allocentric reference frame, but motor movements that comprise physical navigation are represented within an egocentric reference frame. Allocentric navigational plans must be converted to an egocentric reference frame prior to implementation as overt behavior. Here we describe an egocentric spatial representation of environmental boundaries in the dorsomedial striatum.

Systemic administration of two different anxiolytic drugs decreases local field potential theta frequency in the medial entorhinal cortex without affecting grid cell firing fields.

Neurons coding spatial location (grid cells) are found in medial entorhinal cortex (MEC) and demonstrate increasing size of firing fields and spacing between fields (grid scale) along the dorsoventral axis. This change in grid scale correlates with differences in theta frequency, a 6-10Hz rhythm in the local field potential (LFP) and rhythmic firing of cells. A relationship between theta frequency and grid scale can be found when examining grid cells recorded in different locations along the dorsoventral axis of MEC. When describing the relationship between theta frequency and grid scale, it is important to account for the strong positive correlation between theta frequency and running speed. Plotting LFP theta frequency across running speeds dissociates two components of this relationship: slope and intercept of the linear fit. Change in theta frequency through a change in the slope component has been modeled and shown experimentally to affect grid scale, but the prediction that change in the intercept component would not affect grid scale has not been tested experimentally. This prediction about the relationship of intercept to grid scale is the primary hypothesis tested in the experiments presented here. All known anxiolytic drugs decrease hippocampal theta frequency despite their differing mechanisms of action. Specifically, anxiolytics decrease the intercept of the theta frequency-running speed relationship in the hippocampus. Here we demonstrate that anxiolytics decrease the intercept of the theta frequency-running speed relationship in the MEC, similar to hippocampus, and the decrease in frequency through this change in intercept does not affect grid scale.

Multiple Running Speed Signals in Medial Entorhinal Cortex.

Grid cells in medial entorhinal cortex (MEC) can be modeled using oscillatory interference or attractor dynamic mechanisms that perform path integration, a computation requiring information about running direction and speed. The two classes of computational models often use either an oscillatory frequency or a firing rate that increases as a function of running speed. Yet it is currently not known whether these are two manifestations of the same speed signal or dissociable signals with potentially different anatomical substrates. We examined coding of running speed in MEC and identified these two speed signals to be independent of each other within individual neurons. The medial septum (MS) is strongly linked to locomotor behavior, and removal of MS input resulted in strengthening of the firing rate speed signal, while decreasing the strength of the oscillatory speed signal. Thus, two speed signals are present in MEC that are differentially affected by disrupted MS input.

Rebound spiking in layer II medial entorhinal cortex stellate cells: Possible mechanism of grid cell function.

Rebound spiking properties of medial entorhinal cortex (mEC) stellate cells induced by inhibition may underlie their functional properties in awake behaving rats, including the temporal phase separation of distinct grid cells and differences in grid cell firing properties. We investigated rebound spiking properties using whole cell patch recording in entorhinal slices, holding cells near spiking threshold and delivering sinusoidal inputs, superimposed with realistic inhibitory synaptic inputs to test the capacity of cells to selectively respond to specific phases of inhibitory input. Stellate cells showed a specific phase range of hyperpolarizing inputs that elicited spiking, but non-stellate cells did not show phase specificity. In both cell types, the phase range of spiking output occurred between the peak and subsequent descending zero crossing of the sinusoid. The phases of inhibitory inputs that induced spikes shifted earlier as the baseline sinusoid frequency increased, while spiking output shifted to later phases. Increases in magnitude of the inhibitory inputs shifted the spiking output to earlier phases. Pharmacological blockade of h-current abolished the phase selectivity of hyperpolarizing inputs eliciting spikes. A network computational model using cells possessing similar rebound properties as found in vitro produces spatially periodic firing properties resembling grid cell firing when a simulated animal moves along a linear track. These results suggest that the ability of mEC stellate cells to fire rebound spikes in response to a specific range of phases of inhibition could support complex attractor dynamics that provide completion and separation to maintain spiking activity of specific grid cell populations.

Head direction is coded more strongly than movement direction in a population of entorhinal neurons.

The spatial firing pattern of entorhinal grid cells may be important for navigation. Many different computational models of grid cell firing use path integration based on movement direction and the associated movement speed to drive grid cells. However, the response of neurons to movement direction has rarely been tested, in contrast to multiple studies showing responses of neurons to head direction. Here, we analyzed the difference between head direction and movement direction during rat movement and analyzed cells recorded from entorhinal cortex for their tuning to movement direction. During foraging behavior, movement direction differs significantly from head direction. The analysis of neuron responses shows that only 5 out of 758 medial entorhinal cells show significant coding for both movement direction and head direction when evaluating periods of rat behavior with speeds above 10 cm/s and ±30° angular difference between movement and head direction. None of the cells coded movement direction alone. In contrast, 21 cells in this population coded only head direction during behavioral epochs with these constraints, indicating much stronger coding of head direction in this population. This suggests that the movement direction signal required by most grid cell models may arise from other brain structures than the medial entorhinal cortex. This article is part of a Special Issue entitled SI: Brain and Memory.

Gray & white matter tissue contrast differentiates Mild Cognitive Impairment converters from non-converters.

The clinical relevance of gray/white matter contrast ratio (GWR) in mild cognitive impairment (MCI) remains unknown. This study examined baseline GWR and 3-year follow-up diagnostic status in MCI. Alzheimer's Disease Neuroimaging Initiative MCI participants with baseline 1.5 T MRI and 3-year follow-up clinical data were included. Participants were categorized into two groups based on 3-year follow-up diagnoses: 1) non-converters (n = 69, 75 ± 7, 26 % female), and 2) converters (i.e., dementia at follow-up; n = 69, 75 ± 7, 30 % female) who were matched on baseline age and Mini-Mental State Examination scores. Groups were compared on FreeSurfer generated baseline GWR from structural images in which higher values represent greater tissue contrast. A general linear model, adjusting for APOE-status, scanner type, hippocampal volume, and cortical thickness, revealed that converters evidenced lower GWR values than non-converters (i.e., more degradation in tissue contrast; p = 0.03). Individuals with MCI who convert to dementia have lower baseline GWR values than individuals who remain diagnostically stable over a 3-year period, statistically independent of cortical thickness or hippocampal volume.