Grid Cell Firing Patterns Maintain their Hexagonal Firing Patterns on a Circular Track.

In an open two-dimensional environment, grid cells in the medial entorhinal cortex are known to be active in multiple locations, displaying a striking periodic hexagonal firing pattern covering the entire space. Both modeling and experimental data suggest that such periodic spatial representations may emerge from a continuous attractor network. According to this theory, grid cell activity in any stable 1D environment is a slice through an underlying 2D hexagonal pattern, which is supported by some experimental studies but challenged by others. Grid cells are believed to play a fundamental role in path integration, and so understanding their behavior in various environments is crucial for understanding the flow of information through the entorhinal-hippocampal system. To this end, we analyzed the activity of grid cells when rats traversed a circular track. A previous study involving this data set analyzed individual grid cell activity patterns separately, but we found that individual grid cells do not provide sufficient data for determining the underlying spatial activity pattern. To circumvent this, we compute the population autocorrelation, which pools together population responses from all grid cells within the same module. This novel approach recovers the underlying six-peak hexagonal pattern that was not observable in the individual autocorrelations. We also use the population autocorrelation to infer the spacing and orientation of the population lattice, revealing how the lattice differs across environments. Furthermore, the population autocorrelation of the linearized track reveals that at the level of the population, grid cells have an allocentric code for space. These results are strong support for the attractor network theory for grid cells, and our novel approach can be used to analyze grid cell activity in any undersampled environment.

Place-cell capacity and volatility with grid-like inputs.

What factors constrain the arrangement of the multiple fields of a place cell? By modeling place cells as perceptrons that act on multiscale periodic grid-cell inputs, we analytically enumerate a place cell's repertoire - how many field arrangements it can realize without external cues while its grid inputs are unique - and derive its capacity - the spatial range over which it can achieve any field arrangement. We show that the repertoire is very large and relatively noise-robust. However, the repertoire is a vanishing fraction of all arrangements, while capacity scales only as the sum of the grid periods so field arrangements are constrained over larger distances. Thus, grid-driven place field arrangements define a large response scaffold that is strongly constrained by its structured inputs. Finally, we show that altering grid-place weights to generate an arbitrary new place field strongly affects existing arrangements, which could explain the volatility of the place code.

Drug Side-Effect Profiles Prediction: From Empirical to Structural Risk Minimization.

The identification of drug side-effects is considered to be an important step in drug design, which could not only shorten the time but also reduce the cost of drug development. In this paper, we investigate the relationship between the potential side-effects of drug candidates and their chemical structures. The preliminary Regularized Regression (RR) model for drug side-effects prediction has promising features in the efficiency of model training and the existence of a closed form solution. It performs better than other state-of-the-art methods, in terms of minimum accuracy and average accuracy. In order to dig inside how drug structure will associate with side effect, we further propose weighted GTS (Generalized T-Student Kernel: WGTS) SVM model from a structural risk minimization perspective. The SVM model proposed in this paper provides a better understanding of drug side-effects in the process of drug development. The usefulness of the WGTS model lies in the superior performance in a cross validation setting on 888 approved drugs with 1385 side-effects profiling from SIDER database. This work is expected to shed light on intriguing studies that predict potential un-identifying side-effects and suggest how we can avoid drug side-effects by the removal of some distinguished chemical structures.

Transforming the Choice Outcome to an Action Plan in Monkey Lateral Prefrontal Cortex: A Neural Circuit Model.

In economic decisions, we make a good-based choice first, then we transform the outcome into an action to obtain the good. To elucidate the network mechanisms for such transformation, we constructed a neural circuit model consisting of modules representing choice, integration of choice with target locations, and the final action plan. We examined three scenarios regarding how the final action plan could emerge in the neural circuit and compared their implications with experimental data. Our model with heterogeneous connectivity predicts the coexistence of three types of neurons with distinct functions, confirmed by analyzing the neural activity in the lateral prefrontal cortex (LPFC) of behaving monkeys. We obtained a much more distinct classification of functional neuron types in the ventral than the dorsal region of LPFC, suggesting that the action plan is initially generated in ventral LPFC. Our model offers a biologically plausible neural circuit architecture that implements good-to-action transformation during economic choice.

Intrinsic rescaling of granule cells restores pattern separation ability of a dentate gyrus network model during epileptic hyperexcitability.

The dentate gyrus (DG) is thought to enable efficient hippocampal memory acquisition via pattern separation. With patterns defined as spatiotemporally distributed action potential sequences, the principal DG output neurons (granule cells, GCs), presumably sparsen and separate similar input patterns from the perforant path (PP). In electrophysiological experiments, we have demonstrated that during temporal lobe epilepsy (TLE), GCs downscale their excitability by transcriptional upregulation of "leak" channels. Here we studied whether this cell type-specific intrinsic plasticity is in a position to homeostatically adjust DG network function. We modified an established conductance-based computer model of the DG network such that it realizes a spatiotemporal pattern separation task, and quantified its performance with and without the experimentally constrained leaky GC phenotype. Two proposed TLE seizure mechanisms were implemented in various degrees and combinations: recurrent GC excitation via mossy fiber sprouting and increased PP input. While increasing PP strength degraded pattern separation only gradually, already the slight elevation of sprouting drastically (non-linearly) impaired pattern separation. In most tested hyperexcitable networks, leaky GCs ameliorated pattern separation. However, in some sprouting situations with all-or-none seizure behavior, pattern separation was disabled with and without leaky GCs. In the mild sprouting (and PP increase) region of non-linear impairment, leaky GCs were particularly effective in restoring pattern separation performance. These results are compatible with the hypothesis that the experimentally observed intrinsic rescaling of GCs serves to maintain the physiological function of the DG network.

Impact of correlated inputs to neurons: modeling observations from in vivo intracellular recordings.

In vivo recordings in rat somatosensory cortex suggest that excitatory and inhibitory inputs are often correlated during spontaneous and sensory-evoked activity. Using a computational approach, we study how the interplay of input correlations and timing observed in experiments controls the spiking probability of single neurons. Several correlation-based mechanisms are identified, which can effectively switch a neuron on and off. In addition, we investigate the transfer of input correlation to output correlation in pairs of neurons, at the spike train and the membrane potential levels, by considering spike-driving and non-spike-driving inputs separately. In particular, we propose a plausible explanation for the in vivo finding that membrane potentials in neighboring neurons are correlated, but the spike-triggered averages of membrane potentials preceding a spike are not: Neighboring neurons possibly receive an ongoing bombardment of correlated subthreshold background inputs, and occasionally uncorrelated spike-driving inputs.

Significance of input correlations in striatal function.

The striatum is the main input station of the basal ganglia and is strongly associated with motor and cognitive functions. Anatomical evidence suggests that individual striatal neurons are unlikely to share their inputs from the cortex. Using a biologically realistic large-scale network model of striatum and cortico-striatal projections, we provide a functional interpretation of the special anatomical structure of these projections. Specifically, we show that weak pairwise correlation within the pool of inputs to individual striatal neurons enhances the saliency of signal representation in the striatum. By contrast, correlations among the input pools of different striatal neurons render the signal representation less distinct from background activity. We suggest that for the network architecture of the striatum, there is a preferred cortico-striatal input configuration for optimal signal representation. It is further enhanced by the low-rate asynchronous background activity in striatum, supported by the balance between feedforward and feedback inhibitions in the striatal network. Thus, an appropriate combination of rates and correlations in the striatal input sets the stage for action selection presumably implemented in the basal ganglia.

Neuronal encoding of meaning: establishing category-selective response patterns in the avian 'prefrontal cortex'.

Forebrain association areas interweave perceived stimuli with acquired representations of own actions and their outcome. Often, relevant stimuli come in a bewildering variety of shapes and sizes and we slowly have to learn to group them into meaningful categories. Therefore, the aim of the present study was twofold: First, to reveal how single units in the pigeon's nidopallium caudolaterale (NCL), a functional analogue of the mammalian prefrontal cortex (PFC), encode stimuli that differ in visual features but not in behavioral relevance. The second aim was to understand how these categorical representations are established during learning. Recordings were made from NCL neurons while pigeons performed a go-nogo categorization paradigm. Responses during presentation of the two S+ stimuli and non-responding during presentation of the two S- stimuli were followed by reward. We recorded from two pigeons at different learning stages. In the beginning of the learning process, neurons were active during and shortly before reward, but only in go trials. These data suggest that during the early phase of learning avian 'prefrontal' neurons code for rewards associated with the same behavioral demand, while ignoring feature differences of stimuli within one category. When learning progressed, (1) category selectivity became stronger, (2) responses selective for nogo stimuli appeared, and (3) reward-related responses disappeared in favor of category-selective responses during the stimulus phase. This backward shift in time resembles response patterns assumed by the temporal difference (TD) model of reinforcement learning, but goes beyond it, since it reflects the neuronal correlate of functional categories.