Andrey V. Olypher

Attention-Like Modulation of Hippocampus Place Cell Discharge

Hippocampus place cell discharge is an important model system for understanding cognition, but evidence is missing that the place code is under the kind of dynamic attentional control characterized in primates as selective activation of one neural representation and suppression of another, competing representation. We investigated the apparent noise (“overdispersion”) in the CA1 place code, hypothesizing that overdispersion results from discharge fluctuations as spatial attention alternates between distal cues and local/self-motion cues. The hypothesis predicts that: (1) preferential use of distal cues will decrease overdispersion; (2) global, attention-like states can be decoded from ensemble discharge such that both the discharge rates and the spatial firing patterns of individual cells will be distinct in the two states; (3) identifying attention-like states improves reconstructions of the rats path from ensemble discharge. These predictions were confirmed, implying that a covert, dynamic attention-like process modulates discharge on a ∼1 s time scale. We conclude the hippocampus place code is a dynamic representation of the spatial information in the immediate focus of attention.

Unmasking the CA1 Ensemble Place Code by Exposures to Small and Large Environments: More Place Cells and Multiple, Irregularly Arranged, and Expanded Place Fields in the Larger Space

In standard experimental environments, a constant proportion of CA1 principal cells are place cells, each with a spatial receptive field called a place field. Although the properties of place cells are a basis for understanding the mammalian representation of spatial knowledge, there is no consensus on which of the two fundamental neural-coding hypotheses correctly accounts for how place cells encode spatial information. Within the dedicated-coding hypothesis, the current activity of each cell is an independent estimate of the location with respect to its place field. The average of the location estimates from many cells represents current location, so a dedicated place code would degrade if single cells had multiple place fields. Within the alternative, ensemble-coding hypothesis, the concurrent discharge of many place cells is a vector that represents current location. An ensemble place code is not degraded if single cells have multiple place fields as long as the discharge vector at each location is unique. Place cells with multiple place fields might be required to represent the substantially larger space in more natural environments. To distinguish between the dedicated-coding and ensemble-coding hypotheses, we compared the characteristics of CA1 place fields in a standard cylinder and an approximately six times larger chamber. Compared with the cylinder, in the chamber, more CA1 neurons were place cells, each with multiple, irregularly arranged, and enlarged place fields. The results indicate that multiple place fields is a fundamental feature of CA1 place cell activity and that, consequently, an ensemble place code is required for CA1 discharge to accurately signal location.

Cognitive Disorganization in Hippocampus: A Physiological Model of the Disorganization in Psychosis

Cognitive coordination refers to processes that organize the timing of activity among neurons without altering individual discharge properties. Coordinating processes allow neural networks to coactivate related representations and prevent the coactivation of unrelated representations. Impaired cognitive coordination, also called cognitive disorganization, is hypothesized to be the core deficit in the disorganized syndrome of schizophrenia (Phillips and Silverstein, 2003), a condition characterized by hallucinations, disorganization, and thought disorder. This disorganization hypothesis is based on the observation that schizophrenic subjects are impaired at segregating relevant and irrelevant stimuli and selectively using associations between relevant cues. We report that injecting the neural activity blocker tetrodotoxin (TTX) into one hippocampus persistently coactivated pyramidal cells in the uninjected hippocampus that initially discharged independently. In accord with the definition of cognitive disorganization, pyramidal cell firing rates only changed for 15 min and did not accompany the coactivation. The TTX-induced coactivity was maximal at gamma periods, consistent with altered gamma oscillations and disorganization in schizophrenia. A network model confirmed that increasing the coupling of weakly associated cells impairs the selective activation and inhibition of stored spatial representations. This TTX-induced cognitive disorganization correctly predicted that the same TTX injection selectively impaired the ability of rats to segregate relevant associations among distal spatial stimuli from irrelevant local stimuli (Wesierska et al., 2005). The TTX-induced coactivity of hippocampal pyramidal cell discharge has construct and predictive validity as a physiological model of psychosis-related disorganization.

Measuring the Quality of Neuronal Identification in Ensemble Recordings

Technological advances in electrode construction and digital signal processing now allow recording simultaneous extracellular action potential discharges from many single neurons, with the potential to revolutionize understanding of the neural codes for sensory, motor, and cognitive variables. Such studies have revealed the importance of ensemble neural codes, encoding information in the dynamic relationships among the action potential spike trains of multiple single neurons. Although the success of this research depends on the accurate classification of extracellular action potentials to individual neurons, there are no widely used quantitative methods for assessing the quality of the classifications. Here we describe information theoretic measures of action potential waveform isolation applicable to any dataset that have an intuitive, universal interpretation, that are not dependent on the methods or choice of parameters for single-unit isolation, and that have been validated using a dataset of simultaneous intracellular and extracellular neuronal recordings from Sprague Dawley rats.

How does maintenance of network activity depend on endogenous dynamics of isolated neurons

Robust activity of some networks, such as central pattern generators, suggests the existence of physiological mechanisms that maintain the most important characteristics, for example, the period and spike frequency of the pattern. Whatever these mechanisms are, they change the appropriate model parameters to or along the isomanifolds on which the characteristics of the pattern are constant, while their sensitivities to parameters may be different. Setting synaptic connections to zero at the points of isomanifolds allows for dissecting the maintenance mechanisms into components involving synaptic transmission and components involving intrinsic currents. The physiological meaning of the intrinsic current changes might be revealed by analysis of their impact on endogenous neuronal dynamics. Here, we sought answers to two questions: (1) Do parameter variations in insensitive directions (along isomanifolds) change endogenous dynamics of the network neurons? (2) Do sensitive and insensitive directions for network pattern characteristics depend on endogenous dynamics of the network neurons We considered a leech heartbeat half-center oscillator model network and analyzed isomanifolds on which the burst period or spike frequency of the model, or both, are constant. Based on our analysis, we hypothesize that the dependence on endogenous dynamics of the isolated neurons is the stronger the more characteristics of the pattern have to be maintained. We also found that in general, the network was more flexible when it consisted of endogenously tonically spiking rather than bursting or silent neurons. Finally, we discuss the physiological implications of our findings.

Input-to-output transformation in a model of the rat hippocampal CA1 network

Here we use computational modeling to gain new insights into the transformation of inputs in hippocampal field CA1. We considered input-output transformation in CA1 principal cells of the rat hippocampus, with activity synchronized by population gamma oscillations. Prior experiments have shown that such synchronization is especially strong for cells within one millimeter of each other. We therefore simulated a one-millimeter patch of CA1 with 23,500 principal cells. We used morphologically and biophysically detailed neuronal models, each with more than 1000 compartments and thousands of synaptic inputs. Inputs came from binary patterns of spiking neurons from field CA3 and entorhinal cortex (EC). On average, each presynaptic pattern initiated action potentials in the same number of CA1 principal cells in the patch. We considered pairs of similar and pairs of distinct patterns. In all the cases CA1 strongly separated input patterns. However, CA1 cells were considerably more sensitive to small alterations in EC patterns compared to CA3 patterns. Our results can be used for comparison of input-to-output transformations in normal and pathological hippocampal networks.

Variations of neuronal parameters that do not change network output

Neuronal network modeling and experiments indicate that the same physiologically relevant patterns of the network activity can be observed for quite different sets of neuronal parameters. These findings imply that parameters, each of which affects network functionality, co-vary in real networks; i.e. the variations of these parameters must be concordant. Finding such concordant variations can advance our understanding on how the properties of individual neurons determine network functionality. In particular, they may explain variability of neuronal parameters observed in living systems, and show possible paths for homeostatic regulation. In this study, we sought local interrelations between neuronal parameters that did not change network output by using the implicit function theorem. This theorem, under certain conditions, establishes the existence and uniqueness of such interrelations, and specifies them in linear approximation. By assessing such interrelations at different points in the parameter space of a model of the leech heartbeat central pattern generator (CPG) [1], we found a linear correlation between neuronal parameters that preserve a primary output characteristic of this CPG, the cycle period. The correlated parameters were the maximal conductance of the spike-mediated synaptic current, and of the hyper-polarization activated inward current, Ih. We also found that this linear correlation was different for model neurons with different endogenous activity: silence, bursting or tonic spiking. For neurons of one type, however, the correlation was similar.

Transformation of inputs in a model of the rat hippocampal CA1 network.

The hippocampus is one of the brain structures critically implicated in schizophrenia. Known schizophrenia-specific alterations in the hippocampal neurons are subtle. Their impact on the information processing in the hippocampus is poorly understood. A core difficulty is the lack of understanding of normal hippocampal functioning. Here, we focus on some simple but potentially fundamental characteristics of that functioning, and assess them in a model of the CA1 hippocampal network. In our study, network effects resulted from overlapping inputs to modeled CA1 principal cells; we did not consider explicit interactions between the cells. The overlap was determined on the basis of the known hippocampal anatomy [1]. We assumed that all the cells received their excitatory and inhibitory inputs synchronously, as if strongly modulated by ongoing theta and gamma rhythm [2]. Each cell was modeled by biophysically realistic multicompartment models of reconstructed CA1 principal cells using NEURON [3,4]. Our analysis of the network performance was motivated by our earlier model of the hippocampal network under normal and psychotic conditions [5]. When “normal”, that attractor model network was able to generalize similar input patterns and discriminate distinct ones. Such behavior required time sufficient for the network dynamics to converge to appropriate steady states. In the present study, we complement that approach by assessing similar abilities of generalizing and discriminating of inputs. However here we used much more realistic neuronal models and did not take into account associative properties of the network. We characterized the network performance by its response to synchronous inputs within 40 milliseconds. If a modeled CA1cell spiked within this interval it contributed “1” to the output of the network, otherwise the cell contributed “0”. In particular, we determined how the average distance between the output patterns depended on the average distance between the input patterns. We performed this analysis for CA3, entorhinal, and mixed input patterns.