The plasticity-pathology continuum: defining a role for the LTP phenomenon.

Long-term potentiation (LTP) is the most widely studied form of neuroplasticity and is believed by many in the field to be the substrate for learning and memory. For this reason, an understanding of the mechanisms underlying LTP is thought to be of fundamental importance to the neurosciences, but a definitive linkage of LTP to learning or memory has not been achieved. Much of the correlational data used to support this claim is ambiguous and controversial, precluding any solid conclusion about the functional relevance of this often artificially induced form of neuroplasticity. In spite of this fact, the belief that LTP is a mechanism subserving learning and/or memory has become so dominant in the field that the investigation of other potential roles or actions of LTP-like phenomena in the nervous system has been seriously hindered. The multiple subtypes of the phenomena and the myriad molecules apparently involved in these subtypes raise the possibility that observed forms of LTP may represent very different types of modification events, with vastly different consequences for neural function and survival. A relationship between LTP and neuropathology is suggested in part by the fact that many of the molecular processes involved in LTP induction or maintenance are the same as those activated during excitotoxic events in neurons. In addition, some LTP subtypes are clearly induced by pathological stimuli, e.g., anoxic LTP. Such data raise the possibility that LTP is part of a continuum of types of neural modification, some leading to beneficial alterations such as may occur in learning and others that may be primarily pathological in nature, as in kindling and excitotoxicity. In this article, we introduce a plasticity-pathology continuum model that is designed to place the various forms of neural modification into proper context. In vitro and kindling receptor regulation studies are used to provide a basis for evaluating the specific synaptic/cellular response modification along the continuum of events, from beneficial to detrimental, that will be induced by a particular stimulus.     

Surprise! Neural correlates of Pearce-Hall and Rescorla-Wagner coexist within the brain

Learning theory and computational accounts suggest that learning depends on errors in outcome prediction as well as changes in processing of or attention to events. These divergent ideas are captured by models, such as Rescorla-Wagner (RW) and temporal difference (TD) learning on the one hand, which emphasize errors as directly driving changes in associative strength, vs. models such as Pearce-Hall (PH) and more recent variants on the other hand, which propose that errors promote changes in associative strength by modulating attention and processing of events. Numerous studies have shown that phasic firing of midbrain dopamine (DA) neurons carries a signed error signal consistent with RW or TD learning theories, and recently we have shown that this signal can be dissociated from attentional correlates in the basolateral amygdala and anterior cingulate. Here we will review these data along with new evidence: (i) implicating habenula and striatal regions in supporting error signaling in midbrain DA neurons; and (ii) suggesting that the central nucleus of the amygdala and prefrontal regions process the amygdalar attentional signal. However, while the neural instantiations of the RW and PH signals are dissociable and complementary, they may be linked. Any linkage would have implications for understanding why one signal dominates learning in some situations and not others, and also for appreciating the potential impact on learning of neuropathological conditions involving altered DA or amygdalar function, such as schizophrenia, addiction or anxiety disorders.     

Memory, synaptic plasticity and neurotoxins

Most neurotoxins induce serious impairments in cognitive or intellectual functioning; therefore, the ability of the individual to learn and remember forms a critical component of neurotoxin assessment. In depth investigations of neurotoxin-induced cognitive deficits have assisted in pinpointing the neural site of toxin activity. However, specific impairments in learning and memory can depend on the developmental stage at which the individual is exposed to the toxin, and certain neurotoxins show cognitive lifespan selectivity. There appears to be a basic sequence of events underlying neural development and information storage, and neurotoxins may disrupt this neuronal sequel critical for the storage or expression of "ancestral" or "environmental" memories. Certain primary rules govern the orderly development of the nervous system; these rules or mechanisms allow the expression of "ancestral memories" concerning neuronal differentiation and primitive behaviors. Following birth, these same mechanisms have been retained in a less robust form to allow learning and information storage, or the retention of "environmental memories." Neurotoxins which disrupt learning and memory capacities appear to interfere in this basic sequence of events, with the specific outcome depending on when during lifespan development the neurotoxin intervenes.     

Controlling the elements: an optogenetic approach to understanding the neural circuits of fear

Neural circuits underlie our ability to interact in the world and to learn adaptively from experience. Understanding neural circuits and how circuit structure gives rise to neural firing patterns or computations is fundamental to our understanding of human experience and behavior. Fear conditioning is a powerful model system in which to study neural circuits and information processing and relate them to learning and behavior. Until recently, technological limitations have made it difficult to study the causal role of specific circuit elements during fear conditioning. However, newly developed optogenetic tools allow researchers to manipulate individual circuit components such as anatomically or molecularly defined cell populations, with high temporal precision. Applying these tools to the study of fear conditioning to control specific neural subpopulations in the fear circuit will facilitate a causal analysis of the role of these circuit elements in fear learning and memory. By combining this approach with in vivo electrophysiological recordings in awake, behaving animals, it will also be possible to determine the functional contribution of specific cell populations to neural processing in the fear circuit. As a result, the application of optogenetics to fear conditioning could shed light on how specific circuit elements contribute to neural coding and to fear learning and memory. Furthermore, this approach may reveal general rules for how circuit structure and neural coding within circuits gives rise to sensory experience and behavior.     

Neural basis of timing and anticipatory behaviors

The ability to anticipate physiological needs and to predict the availability of desirable resources optimizes the likelihood of survival for an organism. The neural basis of the complex behaviors associated with anticipatory responses is now being delineated. Anticipation likely involves learning and memory, reward and punishment, memory and cognition, arousal and feedback associated with changes in internal and external state, homeostatic processes and timing mechanisms. While anticipation can occur on a variety of timescales (seconds to minutes to hours to days to a year), there have been great strides made towards understanding the neural basis timing of events in the circadian realm. Anticipation of daily events, such as scheduled access to food, may serve as a useful model for a more broadly based understanding the neurobiology of anticipation. In this review we examine the historical, conceptual and experimental approaches to understanding the neural basis of anticipation with a focus on anticipation of scheduled daily meals. We also introduce the key topics represented in the papers in this issue. These papers focused on food anticipation, to explore the state of the art in the studies of the neural basis of timing and anticipatory behaviors.     

A common mechanism for adaptive scaling of reward and novelty

Declarative memory is remarkably adaptive in the way it maintains sensitivity to relative novelty in both unknown and highly familiar environments. However, the neural mechanisms underlying this contextual adaptation are poorly understood. On the basis of emerging links between novelty processing and reinforcement learning mechanisms, we hypothesized that responses to novelty will be adaptively scaled according to expected contextual probabilities of new and familiar events, in the same way that responses to prediction errors for rewards are scaled according to their expected range. Using functional magnetic resonance imaging in humans, we show that the influence of novelty and reward on memory formation in an incidental memory task is adaptively scaled and furthermore that the BOLD signal in orbital prefrontal and medial temporal cortices exhibits concomitant scaled adaptive coding. These findings demonstrate a new mechanism for adjusting gain and sensitivity in declarative memory in accordance with contextual probabilities and expectancies of future events. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.     

Network modeling of adult neurogenesis: shifting rates of neuronal turnover optimally gears network learning according to novelty gradient

Apoptotic and neurogenic events in the adult hippocampus are hypothesized to play a role in cognitive responses to new contexts. Corticosteroid-mediated stress responses and other neural processes invoked by substantially novel contextual changes may regulate these processes. Using elementary three-layer neural networks that learn by incremental synaptic plasticity, we explored whether the cognitive effects of differential regimens of neuronal turnover depend on the environmental context in terms of the degree of novelty in the new information to be learned. In "adult" networks that had achieved mature synaptic connectivity upon prior learning of the Roman alphabet, imposition of apoptosis/neurogenesis before learning increasingly novel information (alternate Roman < Russian < Hebrew) reveals optimality of informatic cost benefits when rates of turnover are geared in proportion to the degree of novelty. These findings predict that flexible control of rates of apoptosis-neurogenesis within plastic, mature neural systems optimizes learning attributes under varying degrees of contextual change, and that failures in this regulation may define a role for adult hippocampal neurogenesis in novelty- and stress-responsive psychiatric disorders.     

Filial imprinting in domestic chicks is associated with spine pruning in the associative area, dorsocaudal neostriatum.

Juvenile emotionally modulated learning events are fundamental for the normal development of socio-emotional competence and intellectual capabilities. Filial imprinting in the domestic chick provides a suitable model to investigate the neural mechanisms underlying such juvenile learning events. The forebrain area dorsocaudal neostriatum (Ndc), a multimodal integration area and presumed equivalent to mammalian parietotemporal association cortices, has been shown to be critically involved in this learning process. We investigated whether filial imprinting is associated with changes of synaptic connectivity in the Ndc. Quantitative measurements of spine densities of a large neuron type in the Ndc revealed a massive pruning of spine synapses after filial imprinting. Compared with 7-day-old naive control chicks, imprinted chicks displayed significantly lower spine frequencies on all dendritic segments. Since the average length of the dendritic segments did not change during imprinting, these results can be interpreted as a reduction of the absolute number of spine synapses on this neuron type. In a control region, the primary sensory forebrain area ectostriatum, spine density and dendritic length remained unchanged. These results indicate that synaptic pruning may represent a mechanism of selective synaptic reorganization in higher associative forebrain areas as a fundamental feature of juvenile learning events.     

Understanding the Neural Bases of Implicit and Statistical Learning

Both implicit learning and statistical learning focus on the ability of learners to pick up on patterns in the environment. It has been suggested that these two lines of research may be combined into a single construct of “implicit statistical learning.” However, by comparing the neural processes that give rise to implicit versus statistical learning, we may determine the extent to which these two learning paradigms do indeed describe the same core mechanisms. In this review, we describe current knowledge about neural mechanisms underlying both implicit learning and statistical learning, highlighting converging findings between these two literatures. A common thread across all paradigms is that learning is supported by interactions between the declarative and nondeclarative memory systems of the brain. We conclude by discussing several outstanding research questions and future directions for each of these two research fields. Moving forward, we suggest that the two literatures may interface by defining learning according to experimental paradigm, with “implicit learning” reserved as a specific term to denote learning without awareness, which may potentially occur across all paradigms. By continuing to align these two strands of research, we will be in a better position to characterize the neural bases of both implicit and statistical learning, ultimately improving our understanding of core mechanisms that underlie a wide variety of human cognitive abilities.     

Placing prediction into the fear circuit

Pavlovian fear conditioning depends on synaptic plasticity at amygdala neurons. Here, we review recent electrophysiological, molecular and behavioral evidence suggesting the existence of a distributed neural circuitry regulating amygdala synaptic plasticity during fear learning. This circuitry, which involves projections from the midbrain periaqueductal gray region, can be linked to prediction error and expectation modulation of fear learning, as described by associative and computational learning models. It controls whether, and how much, fear learning occurs by signaling aversive events when they are unexpected. Functional neuroimaging and clinical studies indicate that this prediction circuit is recruited in humans during fear learning and contributes to exposure-based treatments for clinical anxiety. This aversive prediction error circuit might represent a conserved mechanism for regulating fear learning in mammals.     

Memory Reactivation during Learning Simultaneously Promotes Dentate Gyrus/CA2,3 Pattern Differentiation and CA1 Memory Integration

Events that overlap with previous experience may trigger reactivation of existing memories. However, such reactivation may have different representational consequences within the hippocampal circuit. Computational theories of hippocampal function suggest that dentate gyrus and CA_2,3 (DG/CA_2,3) are biased to differentiate highly similar memories, whereas CA₁ may integrate related events by representing them with overlapping neural codes. Here, we tested whether the formation of differentiated or integrated representations in hippocampal subfields depends on the strength of memory reactivation during learning. Human participants of both sexes learned associations (AB pairs, either face-shape or scene-shape), and then underwent fMRI scanning while they encoded overlapping associations (BC shape-object pairs). Both before and after learning, participants were also scanned while viewing indirectly related elements of the overlapping memories (A and C images) in isolation. We used multivariate pattern analyses to measure reactivation of initial pair memories (A items) during overlapping pair (BC) learning, as well as learning-related representational change for indirectly related memory elements in hippocampal subfields. When prior memories were strongly reactivated during overlapping pair encoding, DG/CA_2,3 and subiculum representations for indirectly related images (A and C) became less similar, consistent with pattern differentiation. Simultaneously, memory reactivation during new learning promoted integration in CA₁, where representations for indirectly related memory elements became more similar after learning. Furthermore, memory reactivation and subiculum representation predicted faster and more accurate inference (AC) decisions. These data show that reactivation of related memories during new learning leads to dissociable coding strategies in hippocampal subfields, in line with computational theories.SIGNIFICANCE STATEMENT The flexibility of episodic memory allows us to remember both the details that differentiate similar events and the commonalities among them. Here, we tested how reactivation of past experience during new learning promotes formation of neural representations that might serve these two memory functions. We found that memory reactivation during learning promoted formation of differentiated representations for overlapping memories in the dentate gyrus/CA_2,3 and subiculum subfields of the hippocampus, while simultaneously leading to the formation of integrated representations of related events in subfield CA₁. Furthermore, memory reactivation and subiculum representation predicted success when inferring indirect relationships among events. These findings indicate that memory reactivation is an important learning signal that influences how overlapping events are represented within the hippocampal circuit.     

Synaptic dynamics: Linear model and adaptation algorithm

In this research, temporal processing in brain neural circuitries is addressed by a dynamic model of synaptic connections in which the synapse model accounts for both pre- and post-synaptic processes determining its temporal dynamics and strength. Neurons, which are excited by the post-synaptic potentials of hundred of the synapses, build the computational engine capable of processing dynamic neural stimuli. Temporal dynamics in neural models with dynamic synapses will be analyzed, and learning algorithms for synaptic adaptation of neural networks with hundreds of synaptic connections are proposed.The paper starts by introducing a linear approximate model for the temporal dynamics of synaptic transmission. The proposed linear model substantially simplifies the analysis and training of spiking neural networks. Furthermore, it is capable of replicating the synaptic response of the non-linear facilitation–depression model with an accuracy better than 92.5%. In the second part of the paper, a supervised spike-in-spike-out learning rule for synaptic adaptation in dynamic synapse neural networks (DSNN) is proposed. The proposed learning rule is a biologically plausible process, and it is capable of simultaneously adjusting both pre- and post-synaptic components of individual synapses. The last section of the paper starts with presenting the rigorous analysis of the learning algorithm in a system identification task with hundreds of synaptic connections which confirms the learning algorithm’s accuracy, repeatability and scalability. The DSNN is utilized to predict the spiking activity of cortical neurons and pattern recognition tasks. The DSNN model is demonstrated to be a generative model capable of producing different cortical neuron spiking patterns and CA1 Pyramidal neurons recordings. A single-layer DSNN classifier on a benchmark pattern recognition task outperforms a 2-Layer Neural Network and GMM classifiers while having fewer numbers of free parameters and decides with a shorter observation of data. DSNN performance in the benchmark pattern recognition problem shows 96.7% accuracy in classifying three classes of spiking activity.     

Mechanisms of Visual Perceptual Learning in Macaque Visual Cortex

The neural mechanisms underlying behavioral improvement in the detection or discrimination of visual stimuli following learning are still ill understood. Studies in nonhuman primates have shown relatively small and, across studies, variable effects of fine discrimination learning in primary visual cortex when tested outside the context of the learned task. At later stages, such as extrastriate area V4, extensive practice in fine discrimination produces more consistent effects upon responses and neural tuning. In V1 and V4, the effects of learning were most prominent in those neurons that can contribute the most reliable information about the trained stimuli. I suggest that, depending on the particulars of the task demands, neurons at various stages of stimulus and task processing can change their tuning and responses, so that execution of the task will produce a higher frequency of reward. I speculate that the sort of changes that will occur depend on the task and on stimulus analysis requirements, and they may vary from changes in bottom‐up stimulus processing/tuning within early visual areas or more efficient readout of early visual areas to top‐down driven changes in response properties of these areas.     

Auditory training improves neural timing in the human brainstem

The auditory brainstem response reflects neural encoding of the acoustic characteristic of a speech syllable with remarkable precision. Some children with learning impairments demonstrate abnormalities in this preconscious measure of neural encoding especially in background noise. This study investigated whether auditory training targeted to remediate perceptually-based learning problems would alter the neural brainstem encoding of the acoustic sound structure of speech in such children. Nine subjects, clinically diagnosed with a language-based learning problem (e.g., dyslexia), worked with auditory perceptual training software. Prior to beginning and within three months after completing the training program, brainstem responses to the syllable /da/ were recorded in quiet and background noise. Subjects underwent additional auditory neurophysiological, perceptual, and cognitive testing. Ten control subjects, who did not participate in any remediation program, underwent the same battery of tests at time intervals equivalent to the trained subjects. Transient and sustained (frequency-following response) components of the brainstem response were evaluated. The primary pathway afferent volley -- neural events occurring earlier than 11 ms after stimulus onset -- did not demonstrate plasticity. However, quiet-to-noise inter-response correlations of the sustained response ( approximately 11-50 ms) increased significantly in the trained children, reflecting improved stimulus encoding precision, whereas control subjects did not exhibit this change. Thus, auditory training can alter the preconscious neural encoding of complex sounds by improving neural synchrony in the auditory brainstem. Additionally, several measures of brainstem response timing were related to changes in cortical physiology, as well as perceptual, academic, and cognitive measures from pre- to post-training.     

Appetitive learning in snails shows characteristics of conditioning in vertebrates

The snail Lymnaea stagnalis can be rapidly conditioned to produce feeding response to a previously neutral tactile stimulus, repeatedly and specifically paired with positive reinforcement (food). The conditioned response has many of the same characteristics seen in appetitive learning in vertebrates. The relative simplicity of the molluscan CNS and the detailed knowledge of the neural circuitry underlying feeding in Lymnaea will facilitate the neurophysiological analysis of appetitive learning as well as stimulus generalization, discriminative learning and generalization of extinction all described in the present paper.     

Learning-related patterns of CA1 spike trains parallel stimulation parameters optimal for inducing hippocampal long-term potentiation.

Recent studies have revealed 3 stimulation parameters that together comprise the temporal pattern of neuronal activation optimal for the induction of hippocampal LTP: high-frequency bursts, activity 100-200 ms prior to a burst, and burst delivery in phase with the ongoing hippocampal theta rhythm. The present paper reports that these 3 aspects of patterned neural activity, collectively referred to as "theta-bursting," are characteristic of the spike trains of CA1 pyramidal cells in rats during the sampling and analysis of learning cues in an odor discrimination task and during performances of a spatial memory task. In contrast, theta-bursting occurs relatively infrequently during behavioral events less directly related to task-relevant mnemonic processing. These findings suggest that the optimal conditions for the induction of LTP occur naturally in behaving animals, time-locked to behavioral events critical to learning.     

Boosted ARTMAP: modifications to fuzzy ARTMAP motivated by boosting theory.

In this paper, several modifications to the Fuzzy ARTMAP neural network architecture are proposed for conducting classification in complex, possibly noisy, environments. The goal of these modifications is to improve upon the generalization performance of Fuzzy ART-based neural networks, such as Fuzzy ARTMAP, in these situations. One of the major difficulties of employing Fuzzy ARTMAP on such learning problems involves over-fitting of the training data. Structural risk minimization is a machine-learning framework that addresses the issue of over-fitting by providing a backbone for analysis as well as an impetus for the design of better learning algorithms. The theory of structural risk minimization reveals a trade-off between training error and classifier complexity in reducing generalization error, which will be exploited in the learning algorithms proposed in this paper. Boosted ART extends Fuzzy ART by allowing the spatial extent of each cluster formed to be adjusted independently. Boosted ARTMAP generalizes upon Fuzzy ARTMAP by allowing non-zero training error in an effort to reduce the hypothesis complexity and hence improve overall generalization performance. Although Boosted ARTMAP is strictly speaking not a boosting algorithm, the changes it encompasses were motivated by the goals that one strives to achieve when employing boosting. Boosted ARTMAP is an on-line learner, it does not require excessive parameter tuning to operate, and it reduces precisely to Fuzzy ARTMAP for particular parameter values. Another architecture described in this paper is Structural Boosted ARTMAP, which uses both Boosted ART and Boosted ARTMAP to perform structural risk minimization learning. Structural Boosted ARTMAP will allow comparison of the capabilities of off-line versus on-line learning as well as empirical risk minimization versus structural risk minimization using Fuzzy ARTMAP-based neural network architectures. Both empirical and theoretical results are presented to enhance the understanding of these architectures.     

Overlap between hippocampal pre‐encoding and encoding patterns supports episodic memory

It is well‐established that whether the information will be remembered or not depends on the extent to which the learning context is reinstated during post‐encoding rest and/or at retrieval. It has yet to be determined, however, if the fundamental importance of contextual reinstatement to memory extends to periods of spontaneous neurocognitive activity prior to learning. We thus asked whether memory performance can be predicted by the extent to which spontaneous pre‐encoding neural patterns resemble patterns elicited during encoding. Individuals studied and retrieved lists of words while undergoing fMRI‐scanning. Multivoxel hippocampal patterns during resting periods prior to encoding resembled hippocampal patterns at encoding most strongly for items that were subsequently remembered. Furthermore, across subjects, the magnitude of similarity correlated with a behavioral measure of episodic recall. The results indicate that the neural context before learning is an important determinant of memory.