The role of the striatum in aversive learning and aversive prediction errors

Neuroeconomic studies of decision making have emphasized reward learning as critical in the representation of value-driven choice behaviour. However, it is readily apparent that punishment and aversive learning are also significant factors in motivating decisions and actions. In this paper, we review the role of the striatum and amygdala in affective learning and the coding of aversive prediction errors (PEs). We present neuroimaging results showing aversive PE-related signals in the striatum in fear conditioning paradigms with both primary (shock) and secondary (monetary loss) reinforcers. These results and others point to the general role for the striatum in coding PEs across a broad range of learning paradigms and reinforcer types.     

Protein secondary structure: entropy, correlations and prediction

MOTIVATION: Is protein secondary structure primarily determined by local interactions between residues closely spaced along the amino acid backbone or by non-local tertiary interactions? To answer this question, we measure the entropy densities of primary and secondary structure sequences, and the local inter-sequence mutual information density. RESULTS: We find that the important inter-sequence interactions are short ranged, that correlations between neighboring amino acids are essentially uninformative and that only one-fourth of the total information needed to determine the secondary structure is available from local inter-sequence correlations. These observations support the view that the majority of most proteins fold via a cooperative process where secondary and tertiary structure form concurrently. Moreover, existing single-sequence secondary structure prediction algorithms are almost optimal, and we should not expect a dramatic improvement in prediction accuracy. AVAILABILITY: Both the data sets and analysis code are freely available from our Web site at http://compbio.berkeley.edu/     

Cardioviral RNA structure logo analysis: entropy, correlations, and prediction

In recent years, there has been an increased number of sequenced RNAs leading to the development of new RNA databases. Thus, predicting RNA structure from multiple alignments is an important issue to understand its function. Since RNA secondary structures are often conserved in evolution, developing methods to identify covariate sites in an alignment can be essential for discovering structural elements. Structure Logo is a technique established on the basis of entropy and mutual information measured to analyze RNA sequences from an alignment. We proposed an efficient Structure Logo approach to analyze conservations and correlations in a set of Cardioviral RNA sequences. The entropy and mutual information content were measured to examine the conservations and correlations, respectively. The conserved secondary structure motifs were predicted on the basis of the conservation and correlation analyses. Our predictive motifs were similar to the ones observed in the viral RNA structure database, and the correlations between bases also corresponded to the secondary structure in the database.     

GABAergic contributions to gating, timing, and phase precession of hippocampal neuronal activity during theta oscillations

Successful spatial exploration requires gating, storage, and retrieval of spatial memories in the correct order. The hippocampus is known to play an important role in the temporal organization of spatial information. Temporally ordered spatial memories are encoded and retrieved by the firing rate and phase of hippocampal pyramidal cells and inhibitory interneurons with respect to ongoing network theta oscillations paced by intra- and extrahippocampal areas. Much is known about the anatomical, physiological, and molecular characteristics as well as the connectivity and synaptic properties of various cell types in the hippocampal microcircuits, but how these detailed properties of individual neurons give rise to temporal organization of spatial memories remains unclear. We present a model of the hippocampal CA1 microcircuit based on observed biophysical properties of pyramidal cells and six types of inhibitory interneurons: axo-axonic, basket, bistratistified, neurogliaform, ivy, and oriens lacunosum-moleculare cells. The model simulates a virtual rat running on a linear track. Excitatory transient inputs come from the entorhinal cortex (EC) and the CA3 Schaffer collaterals and impinge on both the pyramidal cells and inhibitory interneurons, whereas inhibitory inputs from the medial septum impinge only on the inhibitory interneurons. Dopamine operates as a gate-keeper modulating the spatial memory flow to the PC distal dendrites in a frequency-dependent manner. A mechanism for spike-timing-dependent plasticity in distal and proximal PC dendrites consisting of three calcium detectors, which responds to the instantaneous calcium level and its time course in the dendrite, is used to model the plasticity effects. The model simulates the timing of firing of different hippocampal cell types relative to theta oscillations, and proposes functional roles for the different classes of the hippocampal and septal inhibitory interneurons in the correct ordering of spatial memories as well as in the generation and maintenance of theta phase precession of pyramidal cells (place cells) in CA1. The model leads to a number of experimentally testable predictions that may lead to a better understanding of the biophysical computations in the hippocampus and medial septum.     

Introduction to What are the parietal and hippocampal contributions to spatial cognition?, the proceedings of a Discussion held at The Royal Society

Introduction to What are the parietal and hippocampal contributions to spatial cognition?, the proceedings of a Discussion held at The Royal Society on 19 and 20 March 1997. Organized by N. Burgess and J. O''Keefe and edited by N. Burgess, K. J. Jeffery and J. O''Keefe.
     

Perinatal flavour learning and adaptation to being weaned: all the pig needs is smell

Perinatal flavour learning through the maternal diet is known to enhance flavour preference and acceptance of flavoured food in many species, yet still little is known about the mechanism underlying perinatal flavour learning. Previously we found positive effects of perinatal flavour learning on food intake, growth and behaviour of piglets postweaning, but no increased preference for the flavour. This suggests that flavour learning in pigs works through a reduction of weaning stress by the presence of the familiar flavour instead. The aim of this study was to investigate whether perinatal flavour learning reduces stress at weaning, and whether the effect is stronger when the familiar flavour is present in the food. Sows were offered an anethol-flavoured diet (Flavour treatment) or control diet (Control treatment) during late gestation and lactation. Flavour and Control piglets were provided with anethol either in their food (Food treatment) or in the air (Air treatment) after weaning. Preweaning and postweaning treatments did not affect food intake, preference or growth in the first two weeks postweaning but flavour treatment reduced the latency to eat (24 versus 35 hours, P = 0.02) and within-pen variation in growth (SD within-pen: 0.7 versus 1.2 kg, P<0.001). Salivary cortisol levels tended to be lower four and seven hours postweaning for Flavour piglets compared to Control piglets (4 hours: 2.5 versus 3.0 ng/ml, P = 0.05, 7 hours: 3.1 versus 3.4 ng/ml, P = 0.08). Flavour piglets played more and showed less damaging behaviours than Control piglets, indicating that the familiar flavour reduced stress around weaning. Few interaction effects were found between preweaning and postweaning treatment, and no effects of postweaning treatment. We conclude that in the newly weaned pig, perinatal flavour learning results in a reduction of stress when the familiar flavour is present, regardless of providing the flavour in the food or in the air.     

Noncanonical projections to the hippocampal CA3 regulate spatial learning and memory by modulating the feedforward hippocampal trisynaptic pathway

The hippocampal formation (HF) is well documented as having a feedforward, unidirectional circuit organization termed the trisynaptic pathway. This circuit organization exists along the septotemporal axis of the HF, but the circuit connectivity across septal to temporal regions is less well described. The emergence of viral genetic mapping techniques enhances our ability to determine the detailed complexity of HF circuitry. In earlier work, we mapped a subiculum (SUB) back projection to CA1 prompted by the discovery of theta wave back propagation from the SUB to CA1 and CA3. We reason that this circuitry may represent multiple extended noncanonical pathways involving the subicular complex and hippocampal subregions CA1 and CA3. In the present study, multiple retrograde viral tracing approaches produced robust mapping results, which supports this prediction. We find significant noncanonical synaptic inputs to dorsal hippocampal CA3 from ventral CA1 (vCA1), perirhinal cortex (Prh), and the subicular complex. Thus, CA1 inputs to CA3 run opposite the trisynaptic pathway and in a temporal to septal direction. Our retrograde viral tracing results are confirmed by anterograde-directed viral mapping of projections from input mapped regions to hippocampal dorsal CA3 (dCA3). We find that genetic inactivation of the projection of vCA1 to dCA3 impairs object-related spatial learning and memory but does not modulate anxiety-related behaviors. Our data provide a circuit foundation to explore novel functional roles contributed by these noncanonical hippocampal circuit connections to hippocampal circuit dynamics and learning and memory behaviors.

This study reveals extensive non-canonical synaptic inputs to dorsal hippocampal CA3 from ventral CA1, perirhinal cortex and subicular complex, and shows that genetic inactivation of projection from ventral CA1 to dorsal CA3 impairs object-related spatial learning and memory.     

FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level

The rapid accumulation of gene sequences, many of which are hypothetical proteins with unknown function, has stimulated the development of accurate computational tools for protein function prediction with evolution/structure‐based approaches showing considerable promise. In this article, we present FINDSITE‐metal, a new threading‐based method designed specifically to detect metal‐binding sites in modeled protein structures. Comprehensive benchmarks using different quality protein structures show that weakly homologous protein models provide sufficient structural information for quite accurate annotation by FINDSITE‐metal. Combining structure/evolutionary information with machine learning results in highly accurate metal‐binding annotations; for protein models constructed by TASSER, whose average Cα RMSD from the native structure is 8.9 Å, 59.5% (71.9%) of the best of top five predicted metal locations are within 4 Å (8 Å) from a bound metal in the crystal structure. For most of the targets, multiple metal‐binding sites are detected with the best predicted binding site at rank 1 and within the top two ranks in 65.6% and 83.1% of the cases, respectively. Furthermore, for iron, copper, zinc, calcium, and magnesium ions, the binding metal can be predicted with high, typically 70% to 90%, accuracy. FINDSITE‐metal also provides a set of confidence indexes that help assess the reliability of predictions. Finally, we describe the proteome‐wide application of FINDSITE‐metal that quantifies the metal‐binding complement of the human proteome. FINDSITE‐metal is freely available to the academic community at http://cssb.biology.gatech.edu/findsite‐metal/ . Proteins 2011. © 2010 Wiley‐Liss, Inc.     

Intrinsic and extrinsic contributions to the passive moment at the metacarpophalangeal joint

The purpose of this investigation was to determine whether the passive range of motion at the finger joints is restricted more by intrinsic tissues (cross a single joint) or by extrinsic tissues (cross multiple joints). The passive moment at the metacarpophalangeal (MP) joint of the index finger was modeled as the sum of intrinsic and extrinsic components. The intrinsic component was modeled only as a function of MP joint angle. The extrinsic component was modeled as a function of MP joint angle and wrist angle. With the wrist fixed in seven different positions the passive moment at the MP joint of eight subjects was recorded as the finger was rotated through its range at a constant rate. The moment-angle data were fit by the model and the extrinsic and intrinsic components were calculated for a range of MP joint angles and wrist positions. With the MP joint near its extension limit, the median percent extrinsic contribution was 94% with the wrist extended 60° and 14% with the wrist flexed 60°. These percentages were 40 and 88%, respectively, with the MP joint near its flexion limit. Our findings indicate that at most wrist angles the extrinsic tissues offer greater restraint at the limits of MP joint extension and flexion than the intrinsic tissues. The intrinsic tissues predominate when the wrist is flexed or extended enough to slacken the extrinsic tissues. Additional characteristics of intrinsic and extrinsic tissues can be deduced by examining the parameter values calculated by the model.     

Genotype to phenotype: associations,errors and complexity

The Keystone Symposium on Genotype to Phenotype: Focus on Disease was held in Santa Fe, New Mexico, USA, from 19 to 24 February 2002.     

Characterization of the backbone and side chain dynamics of the CaM-CaMKIp complex reveals microscopic contributions to protein conformational entropy

Calmodulin is a central mediator of calcium-dependent signal transduction pathways and regulates the activity of a large number of diverse targets. Calcium-dependent interactions of calmodulin with regulated proteins are of generally high affinity but of quite variable thermodynamic origins. Here we investigate the influence of the binding of the calmodulin-binding domain of calmodulin kinase I on the fast internal dynamics of calcium-saturated calmodulin. NMR relaxation was used to probe motion on the backbone (viewed through the backbone amide NH group) and the side chains (viewed through methyl groups). The distribution of the amplitudes of side chain dynamics is trimodal. The microscopic details of side chain motion are compared with those of a thermodynamically and structurally similar complex of calmodulin with the calmodulin-binding domain of the smooth muscle myosin light chain kinase. While there are no significant differences in backbone dynamics and no net change in methyl-bearing side chain dynamics, a large redistribution of the amplitude of methyl dynamics is observed between the two complexes. The variation in dynamics was largely localized to the heterogeneously dynamic target-binding interface, suggesting that differential dynamics of the binding surface plays a functional role in the high-affinity binding interactions of calmodulin. These results begin to reveal a fundamental role for residual protein entropy in molecular recognition by calmodulin.     

From view cells and place cells to cognitive map learning: processing stages of the hippocampal system

 The goal of this paper is to propose a model of the hippocampal system that reconciles the presence of neurons that look like “place cells” with the implication of the hippocampus (Hs) in other cognitive tasks (e.g., complex conditioning acquisition and memory tasks). In the proposed model, “place cells” or “view cells” are learned in the perirhinal and entorhinal cortex. The role of the Hs is not fundamentally dedicated to navigation or map building, the Hs is used to learn, store, and predict transitions between multimodal states. This transition prediction mechanism could be important for novelty detection but, above all, it is crucial to merge planning and sensory–motor functions in a single and coherent system. A neural architecture embedding this model has been successfully tested on an autonomous robot, during navigation and planning in an open environment. Received: 28 June 1999 / Accepted in revised form: 26 April 2001     

Learning from inhibition: Functional roles of hippocampal CA1 inhibition in spatial learning and memory

Hippocampal inhibitory interneurons exert a powerful influence on learning and memory. Inhibitory interneurons are known to play a major role in many diseases that affect memory, and to strongly influence brain functions required for memory-related tasks. While previous studies involving genetic, optogenetic, and pharmacological manipulations have shown that hippocampal interneurons play essential roles in spatial and episodic learning and memory, exactly how interneurons affect local circuit computations during spatial navigation is not well understood. Given the significant anatomical, morphological, and functional heterogeneity in hippocampal interneurons, one may suspect cell-type specific roles in circuit computations. Here, we review emerging evidence of CA1 hippocampal interneurons’ role in local circuit computations that support spatial learning and memory and discuss open questions about CA1 interneurons in spatial learning.     

Visualizing everything,everywhere, all at once: Cryo-EM and the new field of structureomics

Twenty years ago, the release of the first draft of the human genome sequence instigated a paradigm shift in genomics and molecular biology. Arguably, structural biology is entering an analogous era, with availability of an experimentally determined or predicted molecular model for almost every protein-coding gene from many genomes—producing a reference “structureome”. Structural predictions require experimental validation and not all proteins conform to a single structure, making any reference structureome necessarily incomplete. Despite these limitations, a reference structureome can be used to characterize cell state in more detail than by quantifying sequence or expression levels alone. Cryogenic electron microscopy (cryo-EM) is a method that can generate atomic resolution views of molecules and cells frozen in place. In this perspective I consider how emerging cryo-EM methods are contributing to the new field of structureomics.