Temporal Map Formation in the Barn Owl’s Brain

Barn owls provide an experimentally well-specified example of a temporal map, a neuronal representation of the outside world in the brain by means of time. Their laminar nucleus exhibits a place code of interaural time differences, a cue which is used to determine the azimuthal location of a sound stimulus, e.g., prey. We analyze a model of synaptic plasticity that explains the formation of such a representation in the young bird and show how in a large parameter regime a combination of local and nonlocal synaptic plasticity yields the temporal map as found experimentally. Our analysis includes the effect of nonlinearities as well as the influence of neuronal noise

Computational models of neurophysiological correlates of tinnitus

The understanding of tinnitus has progressed considerably in the past decade, but the details of the mechanisms that give rise to this phantom perception of sound without a corresponding acoustic stimulus have not yet been pinpointed. It is now clear that tinnitus is generated in the brain, not in the ear, and that it is correlated with pathologically altered spontaneous activity of neurons in the central auditory system. Both increased spontaneous firing rates and increased neuronal synchrony have been identified as putative neuronal correlates of phantom sounds in animal models, and both phenomena can be triggered by damage to the cochlea. Various mechanisms could underlie the generation of such aberrant activity. At the cellular level, decreased synaptic inhibition and increased neuronal excitability, which may be related to homeostatic plasticity, could lead to an over-amplification of natural spontaneous activity. At the network level, lateral inhibition could amplify differences in spontaneous activity, and structural changes such as reorganization of tonotopic maps could lead to self-sustained activity in recurrently connected neurons. However, it is difficult to disentangle the contributions of different mechanisms in experiments, especially since not all changes observed in animal models of tinnitus are necessarily related to tinnitus. Computational modeling presents an opportunity of evaluating these mechanisms and their relation to tinnitus. Here we review the computational models for the generation of neurophysiological correlates of tinnitus that have been proposed so far, and evaluate predictions and compare them to available data. We also assess the limits of their explanatory power, thus demonstrating where an understanding is still lacking and where further research may be needed. Identifying appropriate models is important for finding therapies, and we therefore, also summarize the implications of the models for approaches to treat tinnitus

Learning to Discriminate Through Long-Term Changes of Dynamical Synaptic Transmission

Short-term synaptic plasticity is modulated by long-term synaptic changes. There is, however, no general agreement on the computational role of this interaction. Here, we derive a learning rule for the release probability and the maximal synaptic conductance in a circuit model with combined recurrent and feedforward connections that allows learning to discriminate among natural inputs. Short-term synaptic plasticity thereby provides a nonlinear expansion of the input space of a linear classifier, whereas the random recurrent network serves to decorrelate the expanded input space. Computer simulations reveal that the twofold increase in the number of input dimensions through short-term synaptic plasticity improves the performance of a standard perceptron up to 100%. The distributions of release probabilities and maximal synaptic conductances at the capacity limit strongly depend on the balance between excitation and inhibition. The model also suggests a new computational interpretation of spikes evoked by stimuli outside the classical receptive field. These neuronal activitiesmay reflect decorrelation of the expanded stimulus space by intracortical synaptic connections

Single-Trial Phase Precession in the Hippocampus

During the crossing of the place field of a pyramidal cell in the rat hippocampus, the firing phase of the cell decreases with respect to the local theta rhythm. This phase precession is usually studied on the basis of data in which many place field traversals are pooled together. Here we study properties of phase precession in single trials. We found that single-trial and pooled-trial phase precession were different with respect to phase-position correlation, phase-time correlation, and phase range. Whereas pooled-trial phase precession may span 360°, the most frequent single-trial phase range was only ∼180°. In pooled trials, the correlation between phase and position (r = −0.58) was stronger than the correlation between phase and time (r = −0.27), whereas in single trials these correlations (r = −0.61 for both) were not significantly different. Next, we demonstrated that phase precession exhibited a large trial-to-trial variability. Overall, only a small fraction of the trial-to-trial variability in measures of phase precession (e.g., slope or offset) could be explained by other single-trial properties (such as running speed or firing rate), whereas the larger part of the variability remains to be explained. Finally, we found that surrogate single trials, created by randomly drawing spikes from the pooled data, are not equivalent to experimental single trials: pooling over trials therefore changes basic measures of phase precession. These findings indicate that single trials may be better suited for encoding temporally structured events than is suggested by the pooled data

Learning accurate path integration in a ring attractor model of the head direction system

Ring attractor models for angular path integration have recently received strong experimental support. To function as integrators, head-direction (HD) circuits require precisely tuned connectivity, but it is currently unknown how such tuning could be achieved. Here, we propose a network model in which a local, biologically plausible learning rule adjusts synaptic efficacies during development, guided by supervisory allothetic cues. Applied to the Drosophila HD system, the model learns to path-integrate accurately and develops a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading, and where the network remaps to integrate with different gains. Our model predicts that path integration requires supervised learning during a developmental phase. The model setting is general and also applies to architectures that lack the physical topography of a ring, like the mammalian HD system

Early Cortical Changes in Gamma Oscillations in Alzheimer’s Disease

The entorhinal cortices in the temporal lobe of the brain are key structures relaying memory related information between the neocortex and the hippocampus. The medial entorhinal cortex (MEC) routes spatial information, whereas the lateral entorhinal cortex (LEC) routes predominantly olfactory information to the hippocampus. Gamma oscillations are known to coordinate information transfer between brain regions by precisely timing population activity of neuronal ensembles. Here, we studied the organization of in vitro gamma oscillations in the MEC and LEC of the transgenic (tg) amyloid precursor protein (APP)-presenilin 1 (PS1) mouse model of Alzheimer’s Disease (AD) at 4–5 months of age. In vitro gamma oscillations using the kainate model peaked between 30–50 Hz and therefore we analyzed the oscillatory properties in the 20–60 Hz range. Our results indicate that the LEC shows clear alterations in frequency and power of gamma oscillations at an early stage of AD as compared to the MEC. The gamma-frequency oscillation slows down in the LEC and also the gamma power in dorsal LEC is decreased as early as 4–5 months in the tg APP-PS1 mice. The results of this study suggest that the timing of olfactory inputs from LEC to the hippocampus might be affected at an early stage of AD, resulting in a possible erroneous integration of the information carried by the two input pathways to the hippocampal subfields.Peer Reviewe

Cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus

Neuronal oscillations allow for temporal segmentation of neuronal spikes. Interdependent oscillators can integrate multiple layers of information. We examined phase–phase coupling of theta and gamma oscillators in the CA1 region of rat hippocampus during maze exploration and rapid eye movement sleep. Hippocampal theta waves were asymmetric, and estimation of the spatial position of the animal was improved by identifying the waveform-based phase of spiking, compared to traditional methods used for phase estimation. Using the waveform-based theta phase, three distinct gamma bands were identified: slow gammaS (gammaS; 30–50 Hz), midfrequency gammaM (gammaM; 50–90 Hz), and fast gammaF (gammaF; 90–150 Hz or epsilon band). The amplitude of each sub-band was modulated by the theta phase. In addition, we found reliable phase–phase coupling between theta and both gammaS and gammaM but not gammaF oscillators. We suggest that cross-frequency phase coupling can support multiple time-scale control of neuronal spikes within and across structures.Fil: Belluscio, Mariano Andres. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mizuseki, Kenji. Rutgers University; Estados UnidosFil: Schmidt, Robert. Rutgers University; Estados UnidosFil: Kempter, Richard. Rutgers University; Estados UnidosFil: Buzsáki, György. Rutgers University; Estados Unido

Memory replay in balanced recurrent networks

Complex patterns of neural activity appear during up-states in the neocortex and sharp waves in the hippocampus, including sequences that resemble those during prior behavioral experience. The mechanisms underlying this replay are not well understood. How can small synaptic footprints engraved by experience control large-scale network activity during memory retrieval and consolidation? We hypothesize that sparse and weak synaptic connectivity between Hebbian assemblies are boosted by pre-existing recurrent connectivity within them. To investigate this idea, we connect sequences of assemblies in randomly connected spiking neuronal networks with a balance of excitation and inhibition. Simulations and analytical calculations show that recurrent connections within assemblies allow for a fast amplification of signals that indeed reduces the required number of inter-assembly connections. Replay can be evoked by small sensory-like cues or emerge spontaneously by activity fluctuations. Global—potentially neuromodulatory—alterations of neuronal excitability can switch between network states that favor retrieval and consolidation.BMBF, 01GQ1001A, Verbundprojekt: Bernstein Zentrum für Computational Neuroscience, Berlin - "Präzision und Variabilität" - Teilprojekt A2, A3, A4, A8, B6, Zentralprojekt und ProfessurBMBF, 01GQ0972, Verbundprojekt: Bernstein Fokus Lernen - Zustandsabhängigkeit des Lernens, TP 2 und 3BMBF, 01GQ1201, Lernen und Gedächtnis in balancierten SystemenDFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen Systeme

Movement Dependence and Layer Specificity of Entorhinal Phase Precession in Two-Dimensional Environments

As a rat moves, grid cells in its entorhinal cortex (EC) discharge at multiple locations of the external world, and the firing fields of each grid cell span a hexagonal lattice. For movements on linear tracks, spikes tend to occur at successively earlier phases of the theta-band filtered local field potential during the traversal of a firing field - a phenomenon termed phase precession. The complex movement patterns observed in two-dimensional (2D) open-field environments may fundamentally alter phase precession. To study this question at the behaviorally relevant single-run level, we analyzed EC spike patterns as a function of the distance traveled by the rat along each trajectory. This analysis revealed that cells across all EC layers fire spikes that phase-precess;indeed, the rate and extent of phase precession were the same, only the correlation between spike phase and path length was weaker in EC layer III. Both slope and correlation of phase precession were surprisingly similar on linear tracks and in 2D open-field environments despite strong differences in the movement statistics, including running speed. While the phase-precession slope did not correlate with the average running speed, it did depend on specific properties of the animal's path. The longer a curving path through a grid-field in a 2D environment, the shallower was the rate of phase precession, while runs that grazed a grid field tangentially led to a steeper phase-precession slope than runs through the field center. Oscillatory interference models for grid cells do not reproduce the observed phenomena

How spiking neurons give rise to a temporal-feature map

A temporal-feature map is a topographic neuronal representation of temporal attributes of phenomena or objects that occur in the outside world. We explain the evolution of such maps by means of a spike-based Hebbian learning rule in conjunction with a presynaptically unspecific contribution in that, if a synapse changes, then all other synapses connected to the same axon change by a small fraction as well. The learning equation is solved for the case of an array of Poisson neurons. We discuss the evolution of a temporal-feature map and the synchronization of the single cells’ synaptic structures, in dependence upon the strength of presynaptic unspecific learning. We also give an upper bound for the magnitude of the presynaptic interaction by estimating its impact on the noise level of synaptic growth. Finally, we compare the results with those obtained from a learning equation for nonlinear neurons and show that synaptic structure formation may profit from the nonlinearity