The book of Hebb

Synaptic plasticity through a naturalistic lens

From the myriad of studies on neuronal plasticity, investigating its underlying molecular mechanisms up to its behavioral relevance, a very complex landscape has emerged. Recent efforts have been achieved toward more naturalistic investigations as an attempt to better capture the synaptic plasticity underpinning of learning and memory, which has been fostered by the development of in vivo electrophysiological and imaging tools. In this review, we examine these naturalistic investigations, by devoting a first part to synaptic plasticity rules issued from naturalistic in vivo-like activity patterns. We next give an overview of the novel tools, which enable an increased spatio-temporal specificity for detecting and manipulating plasticity expressed at individual spines up to neuronal circuit level during behavior. Finally, we put particular emphasis on works considering brain-body communication loops and macroscale contributors to synaptic plasticity, such as body internal states and brain energy metabolism.

Algebraic approach to spike-time neural codes in the hippocampus

Although temporal coding through spike-time patterns has long been of interest in neuroscience, the specific structures that could be useful for spike-time codes remain highly unclear. Here, we introduce an analytical approach, using techniques from discrete mathematics, to study spike-time codes. As an initial example, we focus on the phenomenon of "phase precession" in the rodent hippocampus. During navigation and learning on a physical track, specific cells in a rodent's brain form a highly structured pattern relative to the oscillation of population activity in this region. Studies of phase precession largely focus on its role in precisely ordering spike times for synaptic plasticity, as the role of phase precession in memory formation is well established. Comparatively less attention has been paid to the fact that phase precession represents one of the best candidates for a spike-time neural code. The precise nature of this code remains an open question. Here, we derive an analytical expression for a function mapping points in physical space to complex-valued spikes by representing individual spike times as complex numbers. The properties of this function make explicit a specific relationship between past and future in spike patterns of the hippocampus. Importantly, this mathematical approach generalizes beyond the specific phenomenon studied here, providing a technique to study the neural codes within precise spike-time sequences found during sensory coding and motor behavior. We then introduce a spike-based decoding algorithm, based on this function, that successfully decodes a simulated animal's trajectory using only the animal's initial position and a pattern of spike times. This decoder is robust to noise in spike times and works on a timescale almost an order of magnitude shorter than typically used with decoders that work on average firing rate. These results illustrate the utility of a discrete approach, based on the structure and symmetries in spike patterns across finite sets of cells, to provide insight into the structure and function of neural systems.

The Memristive Properties and Spike Timing-Dependent Plasticity in Electrodeposited Copper Tungstates and Molybdates

Memristors possess non-volatile memory, adjusting their electrical resistance to the current that flows through them and allowing switching between high and low conducting states. This technology could find applications in fields such as IT, consumer electronics, computing, sensors, and medicine. In this paper, we report successful electrodeposition of thin-film materials consisting of copper tungstate and copper molybdate (CuWO4 and Cu3Mo2O9), which showed notable memristive properties. Material characterisation was performed with techniques such as XRD, XPS, and SEM. The electrodeposited materials exhibited the ability to switch between low and high resistive states during varied cyclic scans and short-term impulses. The retention time of these switched states was also explored. Using these materials, the effects seen in biological systems, specifically spike timing-dependent plasticity, were simulated, being based on analogue operation of the memristors to achieve multiple conductivity states. Bio-inspired simulations performed directly on the material could possibly offer energy and time savings for classical computations. Memristors could be crucial for the advancement of high-efficiency, low-energy neuromorphic electronic devices and technologies in the future.

Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks

In The cognitive-emotional brain, Pessoa overlooks continuum effects on nonlinear brain network connectivity by eschewing neural field theories and physiologically derived constructs representative of neuronal plasticity. The absence of this content, which is so very important for understanding the dynamic structure-function embedding and partitioning of brains, diminishes the rich competitive and cooperative nature of neural networks and trivializes Pessoa’s arguments, and similar arguments by other authors, on the phylogenetic and operational significance of an optimally integrated brain filled with variable-strength neural connections. Riemannian neuromanifolds, containing limit-imposing metaplastic Hebbian- and antiHebbian-type control variables, simulate scalable network behavior that is difficult to capture from the simpler graph-theoretic analysis preferred by Pessoa and other neuroscientists. Field theories suggest the partitioning and performance benefits of embedded cognitive-emotional networks that optimally evolve between exotic classical and quantum computational phases, where matrix singularities and condensations produce degenerate structure-function homogeneities unrealistic of healthy brains. Some network partitioning, as opposed to unconstrained embeddedness, is thus required for effective execution of cognitive-emotional network functions and, in our new era of neuroscience, should be considered a critical aspect of proper brain organization and operation.

Neuronal metabolism in learning and memory: The anticipatory activity perspective

Current research on the molecular mechanisms of learning and memory is based on the "stimulus-response" paradigm, in which the neural circuits connecting environmental events with behavioral responses are strengthened. By contrast, cognitive and systems neuroscience emphasize the intrinsic activity of the brain that integrates information, establishes anticipatory actions, executes adaptive actions, and assesses the outcome via regulatory feedback mechanisms. We believe that the difference in the perspectives of systems and molecular studies is a major roadblock to further progress toward understanding the mechanisms of learning and memory. Here, we briefly overview the current studies in molecular mechanisms of learning and memory and propose that studying the predictive properties of neuronal metabolism will significantly advance our knowledge of how intrinsic, predictive activity of neurons shapes a new learning event. We further suggest that predictive metabolic changes in the brain may also take place in non-neuronal cells, including those of peripheral tissues. Finally, we present a path forward toward more in-depth studies of the role of cell metabolism in learning and memory.

Engram cell connectivity: an evolving substrate for information storage

Understanding memory requires an explanation for how information can be stored in the brain in a stable state. The change in the brain that accounts for a given memory is referred to as an engram. In recent years, the term engram has been operationalized as the cells that are activated by a learning experience, undergoes plasticity, and are sufficient and necessary for memory recall. Using this framework, and a growing toolbox of related experimental techniques, engram manipulation has become a central topic in behavioral, systems, and molecular neuroscience. Recent research on the topic has provided novel insights into the mechanisms of long-term memory storage, and its overlap with instinct. We propose that memory and instinct may be embodied as isomorphic topological structures within the brain's microanatomical circuitry.

The unreasonable effectiveness of deep learning in artificial intelligence

Deep learning networks have been trained to recognize speech, caption photographs, and translate text between languages at high levels of performance. Although applications of deep learning networks to real-world problems have become ubiquitous, our understanding of why they are so effective is lacking. These empirical results should not be possible according to sample complexity in statistics and nonconvex optimization theory. However, paradoxes in the training and effectiveness of deep learning networks are being investigated and insights are being found in the geometry of high-dimensional spaces. A mathematical theory of deep learning would illuminate how they function, allow us to assess the strengths and weaknesses of different network architectures, and lead to major improvements. Deep learning has provided natural ways for humans to communicate with digital devices and is foundational for building artificial general intelligence. Deep learning was inspired by the architecture of the cerebral cortex and insights into autonomy and general intelligence may be found in other brain regions that are essential for planning and survival, but major breakthroughs will be needed to achieve these goals.

Levels of biological plausibility

Notions of mechanism, emergence, reduction and explanation are all tied to levels of analysis. I cover the relationship between lower and higher levels, suggest a level of mechanism approach for neuroscience in which the components of a mechanism can themselves be further decomposed and argue that scientists' goals are best realized by focusing on pragmatic concerns rather than on metaphysical claims about what is ‘real'. Inexplicably, neuroscientists are enchanted by both reduction and emergence. A fascination with reduction is misplaced given that theory is neither sufficiently developed nor formal to allow it, whereas metaphysical claims of emergence bring physicalism into question. Moreover, neuroscience's existence as a discipline is owed to higher-level concepts that prove useful in practice. Claims of biological plausibility are shown to be incoherent from a level of mechanism view and more generally are vacuous. Instead, the relevant findings to address should be specified so that model selection procedures can adjudicate between competing accounts. Model selection can help reduce theoretical confusions and direct empirical investigations. Although measures themselves, such as behaviour, blood-oxygen-level-dependent (BOLD) and single-unit recordings, are not levels of analysis, like levels, no measure is fundamental and understanding how measures relate can hasten scientific progress.

This article is part of the theme issue ‘Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

Formulation of Pruning Maps with Rhythmic Neural Firing

Rhythmic neural firing is thought to underlie the operation of neural function. This triggers the construction of dynamical network models to investigate how the rhythms interact with each other. Recently, an approach concerning neural path pruning has been proposed in a dynamical network system, in which critical neuronal connections are identified and adjusted according to the pruning maps, enabling neurons to produce rhythmic, oscillatory activity in simulation. Here, we construct a sort of homomorphic functions based on different rhythms of neural firing in network dynamics. Armed with the homomorphic functions, the pruning maps can be simply expressed in terms of interactive rhythms of neural firing and allow a concrete analysis of coupling operators to control network dynamics. Such formulation of pruning maps is applied to probe the consolidation of rhythmic patterns between layers of neurons in feedforward neural networks.

Contributions of Parietal Cortex to the Working Memory of an Obstacle Acquired Visually or Tactilely in the Locomoting Cat

A working memory of obstacles is essential for navigating complex, cluttered terrain. In quadrupeds, it has been proposed that parietal cortical areas related to movement planning and working memory may be important for guiding the hindlegs over an obstacle previously cleared by the forelegs. To test this hypothesis, parietal areas 5 and 7 were reversibly deactivated in walking cats. The working memory of an obstacle was assessed in both a visually dependent and tactilely dependent paradigm. Reversible bilateral deactivation of area 5, but not area 7, altered hindleg stepping in a manner indicating that the animals did not recall the obstacle over which their forelegs had stepped. Similar deficits were observed when area 5 deactivation was restricted to the delay during which obstacle memory must be maintained. Furthermore, partial memory recovery observed when area 5 function was deactivated and restored within this maintenance period suggests that the deactivation may suppress, but not eliminate, the working memory of an obstacle. As area 5 deactivations incurred similar memory deficits in both visual and tactile obstacle working memory paradigms, parietal area 5 is critical for maintaining the working memory of an obstacle acquired via vision or touch that is used to modify stepping for avoidance.

Adaptive Tracking Control for Robots With an Interneural Computing Scheme

Adaptive tracking control of mobile robots requires the ability to follow a trajectory generated by a moving target. The conventional analysis of adaptive tracking uses energy minimization to study the convergence and robustness of the tracking error when the mobile robot follows a desired trajectory. However, in the case that the moving target generates trajectories with uncertainties, a common Lyapunov-like function for energy minimization may be extremely difficult to determine. Here, to solve the adaptive tracking problem with uncertainties, we wish to implement an interneural computing scheme in the design of a mobile robot for behavior-based navigation. The behavior-based navigation adopts an adaptive plan of behavior patterns learning from the uncertainties of the environment. The characteristic feature of the interneural computing scheme is the use of neural path pruning with rewards and punishment interacting with the environment. On this basis, the mobile robot can be exploited to change its coupling weights in paths of neural connections systematically, which can then inhibit or enhance the effect of flow elimination in the dynamics of the evolutionary neural network. Such dynamical flow translation ultimately leads to robust sensory-to-motor transformations adapting to the uncertainties of the environment. A simulation result shows that the mobile robot with the interneural computing scheme can perform fault-tolerant behavior of tracking by maintaining suitable behavior patterns at high frequency levels.

Cortical travelling waves: mechanisms and computational principles

Multichannel recording technologies have revealed travelling waves of neural activity in multiple sensory, motor and cognitive systems. These waves can be spontaneously generated by recurrent circuits or evoked by external stimuli. They travel along brain networks at multiple scales, transiently modulating spiking and excitability as they pass. Here, we review recent experimental findings that have found evidence for travelling waves at single-area (mesoscopic) and whole-brain (macroscopic) scales. We place these findings in the context of the current theoretical understanding of wave generation and propagation in recurrent networks. During the large low-frequency rhythms of sleep or the relatively desynchronized state of the awake cortex, travelling waves may serve a variety of functions, from long-term memory consolidation to processing of dynamic visual stimuli. We explore new avenues for experimental and computational understanding of the role of spatiotemporal activity patterns in the cortex.

From connectome to cognition: The search for mechanism in human functional brain networks

Recent developments in functional connectivity research have expanded the scope of human neuroimaging, from identifying changes in regional activation amplitudes to detailed mapping of large-scale brain networks. However, linking network processes to a clear role in cognition demands advances in the theoretical frameworks, algorithms, and experimental approaches applied. This would help evolve the field from a descriptive to an explanatory state, by targeting network interactions that can mechanistically account for cognitive effects. In the present review, we provide an explicit framework to aid this search for "network mechanisms", which anchors recent methodological advances in functional connectivity estimation to a renewed emphasis on careful experimental design. We emphasize how this framework can address specific questions in network neuroscience. These span ambiguity over the cognitive relevance of resting-state networks, how to characterize task-evoked and spontaneous network dynamics, how to identify directed or "effective" connections, and how to apply multivariate pattern analysis at the network level. In parallel, we apply the framework to highlight the mechanistic interaction of network components that remain "stable" across task domains and more "flexible" components associated with on-task reconfiguration. By emphasizing the need to structure the use of diverse analytic approaches with sound experimentation, our framework promotes an explanatory mapping between the workings of the cognitive mind and the large-scale network mechanisms of the human brain.

Memory engram storage and retrieval

A great deal of experimental investment is directed towards questions regarding the mechanisms of memory storage. Such studies have traditionally been restricted to investigation of the anatomical structures, physiological processes, and molecular pathways necessary for the capacity of memory storage, and have avoided the question of how individual memories are stored in the brain. Memory engram technology allows the labeling and subsequent manipulation of components of specific memory engrams in particular brain regions, and it has been established that cell ensembles labeled by this method are both sufficient and necessary for memory recall. Recent research has employed this technology to probe fundamental questions of memory consolidation, differentiating between mechanisms of memory retrieval from the true neurobiology of memory storage.

Plasticity of Neuron-Glial Transmission: Equipping Glia for Long-Term Integration of Network Activity

The capacity of synaptic networks to express activity-dependent changes in strength and connectivity is essential for learning and memory processes. In recent years, glial cells (most notably astrocytes) have been recognized as active participants in the modulation of synaptic transmission and synaptic plasticity, implicating these electrically nonexcitable cells in information processing in the brain. While the concept of bidirectional communication between neurons and glia and the mechanisms by which gliotransmission can modulate neuronal function are well established, less attention has been focussed on the computational potential of neuron-glial transmission itself. In particular, whether neuron-glial transmission is itself subject to activity-dependent plasticity and what the computational properties of such plasticity might be has not been explored in detail. In this review, we summarize current examples of plasticity in neuron-glial transmission, in many brain regions and neurotransmitter pathways. We argue that induction of glial plasticity typically requires repetitive neuronal firing over long time periods (minutes-hours) rather than the short-lived, stereotyped trigger typical of canonical long-term potentiation. We speculate that this equips glia with a mechanism for monitoring average firing rates in the synaptic network, which is suited to the longer term roles proposed for astrocytes in neurophysiology.

From the neuron doctrine to neural networks

For over a century, the neuron doctrine--which states that the neuron is the structural and functional unit of the nervous system--has provided a conceptual foundation for neuroscience. This viewpoint reflects its origins in a time when the use of single-neuron anatomical and physiological techniques was prominent. However, newer multineuronal recording methods have revealed that ensembles of neurons, rather than individual cells, can form physiological units and generate emergent functional properties and states. As a new paradigm for neuroscience, neural network models have the potential to incorporate knowledge acquired with single-neuron approaches to help us understand how emergent functional states generate behaviour, cognition and mental disease.

Operator control of interneural computing machines

A dynamic representation of neural population responses asserts that motor cortex is a flexible pattern generator sending rhythmic, oscillatory signals to generate multiphasic patterns of movement. This raises a question concerning the design and control of new computing machines that mimic the oscillatory patterns and multiphasic patterns seen in neural systems. To address this issue, we design an interneural computing machine (INCM) made of plastic random interneural connections. We develop a mechanical way to measure collective ensemble firing of neurons in INCM. Two sorts of plasticity operators are derived from the measure of synchronous neural activity and the measure of self-sustaining neural activity, respectively. Such plasticity operators conduct activity-dependent operation to modify the network structure of INCM. The activity-dependent operation meets the neurobiological perspective of Hebbian synaptic plasticity and displays the tendency toward circulation breaking aiming to control neural population dynamics. We call such operation operator control of INCM and develop a population analysis of operator control for measuring how well single neurons of INCM can produce rhythmic, oscillatory activity, but at the level of neural ensembles, generate multiphasic patterns of population responses.

Motor control and neural plasticity through interhemispheric interactions

The corpus callosum, which is the largest white matter structure in the human brain, connects the 2 cerebral hemispheres. It plays a crucial role in maintaining the independent processing of the hemispheres and in integrating information between both hemispheres. The functional integrity of interhemispheric interactions can be tested electrophysiologically in humans by using transcranial magnetic stimulation, electroencephalography, and functional magnetic resonance imaging. As a brain structural imaging, diffusion tensor imaging has revealed the microstructural connectivity underlying interhemispheric interactions. Sex, age, and motor training in addition to the size of the corpus callosum influence interhemispheric interactions. Several neurological disorders change hemispheric asymmetry directly by impairing the corpus callosum. Moreover, stroke lesions and unilateral peripheral impairments such as amputation alter interhemispheric interactions indirectly. Noninvasive brain stimulation changes the interhemispheric interactions between both motor cortices. Recently, these brain stimulation techniques were applied in the clinical rehabilitation of patients with stroke by ameliorating the deteriorated modulation of interhemispheric interactions. Here, we review the interhemispheric interactions and mechanisms underlying the pathogenesis of these interactions and propose rehabilitative approaches for appropriate cortical reorganization.

Combined effects of LTP/LTD and synaptic scaling in formation of discrete and line attractors with persistent activity from non-trivial baseline

In this article, we analyze combined effects of LTP/LTD and synaptic scaling and study the creation of persistent activity from a periodic or chaotic baseline attractor. The bifurcations leading to the creation of new attractors have been detailed; this was achieved using a mean field approximation. Attractors encoding persistent activity can notably appear via generalized period-doubling bifurcations, tangent bifurcations of the second iterates or boundary crises, after which the basins of attraction become irregular. Synaptic scaling is shown to maintain the coexistence of a state of persistent activity and the baseline. According to the rate of change of the external inputs, different types of attractors can be formed: line attractors for rapidly changing external inputs and discrete attractors for constant external inputs.

Neural field theory of plasticity in the cerebral cortex

A generalized timing-dependent plasticity rule is incorporated into a recent neural field theory to explore synaptic plasticity in the cerebral cortex, with both excitatory and inhibitory populations included. Analysis in the time and frequency domains reveals that cortical network behavior gives rise to a saddle-node bifurcation and resonant frequencies, including a gamma-band resonance. These system resonances constrain cortical synaptic dynamics and divide it into four classes, which depend on the type of synaptic plasticity window. Depending on the dynamical class, synaptic strengths can either have a stable fixed point, or can diverge in the absence of a separate saturation mechanism. Parameter exploration shows that time-asymmetric plasticity windows, which are signatures of spike-timing dependent plasticity, enable the richest variety of synaptic dynamics to occur. In particular, we predict a zone in parameter space which may allow brains to attain the marginal stability phenomena observed experimentally, although additional regulatory mechanisms may be required to maintain these parameters.

Molecular mechanisms of fear learning and memory

Pavlovian fear conditioning is a particularly useful behavioral paradigm for exploring the molecular mechanisms of learning and memory because a well-defined response to a specific environmental stimulus is produced through associative learning processes. Synaptic plasticity in the lateral nucleus of the amygdala (LA) underlies this form of associative learning. Here, we summarize the molecular mechanisms that contribute to this synaptic plasticity in the context of auditory fear conditioning, the form of fear conditioning best understood at the molecular level. We discuss the neurotransmitter systems and signaling cascades that contribute to three phases of auditory fear conditioning: acquisition, consolidation, and reconsolidation. These studies suggest that multiple intracellular signaling pathways, including those triggered by activation of Hebbian processes and neuromodulatory receptors, interact to produce neural plasticity in the LA and behavioral fear conditioning. Collectively, this body of research illustrates the power of fear conditioning as a model system for characterizing the mechanisms of learning and memory in mammals and potentially for understanding fear-related disorders, such as PTSD and phobias.

A synaptic organizing principle for cortical neuronal groups

Neuronal circuitry is often considered a clean slate that can be dynamically and arbitrarily molded by experience. However, when we investigated synaptic connectivity in groups of pyramidal neurons in the neocortex, we found that both connectivity and synaptic weights were surprisingly predictable. Synaptic weights follow very closely the number of connections in a group of neurons, saturating after only 20% of possible connections are formed between neurons in a group. When we examined the network topology of connectivity between neurons, we found that the neurons cluster into small world networks that are not scale-free, with less than 2 degrees of separation. We found a simple clustering rule where connectivity is directly proportional to the number of common neighbors, which accounts for these small world networks and accurately predicts the connection probability between any two neurons. This pyramidal neuron network clusters into multiple groups of a few dozen neurons each. The neurons composing each group are surprisingly distributed, typically more than 100 μm apart, allowing for multiple groups to be interlaced in the same space. In summary, we discovered a synaptic organizing principle that groups neurons in a manner that is common across animals and hence, independent of individual experiences. We speculate that these elementary neuronal groups are prescribed Lego-like building blocks of perception and that acquired memory relies more on combining these elementary assemblies into higher-order constructs.

Temporal modulation of spike-timing-dependent plasticity

Spike-timing-dependent plasticity (STDP) has attracted considerable experimental and theoretical attention over the last decade. In the most basic formulation, STDP provides a fundamental unit – a spike pair – for quantifying the induction of long-term changes in synaptic strength. However, many factors, both pre- and postsynaptic, can affect synaptic transmission and integration, especially when multiple spikes are considered. Here we review the experimental evidence for multiple types of nonlinear temporal interactions in STDP, focusing on the contributions of individual spike pairs, overall spike rate, and precise spike timing for modification of cortical and hippocampal excitatory synapses. We discuss the underlying processes that determine the specific learning rules at different synapses, such as postsynaptic excitability and short-term depression. Finally, we describe the success of efforts toward building predictive, quantitative models of how complex and natural spike trains induce long-term synaptic modifications.

Modeling the categorical perception of speech sounds: a step toward biological plausibility

Our native language has a lifelong effect on how we perceive speech sounds. Behaviorally, this is manifested as categorical perception, but the neural mechanisms underlying this phenomenon are still unknown. Here, we constructed a computational model of categorical perception, following principles consistent with infant speech learning. A self-organizing network was exposed to a statistical distribution of speech input presented as neural activity patterns of the auditory periphery, resembling the way sound arrives to the human brain. In the resulting neural map, categorical perception emerges from most single neurons of the model being maximally activated by prototypical speech sounds, while the largest variability in activity is produced at category boundaries. Consequently, regions in the vicinity of prototypes become perceptually compressed, and regions at category boundaries become expanded. Thus, the present study offers a unifying framework for explaining the neural basis of the warping of perceptual space associated with categorical perception.

Hebbian reverberations in emotional memory micro circuits

The study of memory in most behavioral paradigms, including emotional memory paradigms, has focused on the feed forward components that underlie Hebb's first postulate, associative synaptic plasticity. Hebb's second postulate argues that activated ensembles of neurons reverberate in order to provide temporal coordination of different neural signals, and thereby facilitate coincidence detection. Recent evidence from our groups has suggested that the lateral amygdala (LA) contains recurrent microcircuits and that these may reverberate. Additionally this reverberant activity is precisely timed with latencies that would facilitate coincidence detection between cortical and sub cortical afferents to the LA. Thus, recent data at the microcircuit level in the amygdala provide some physiological evidence in support of the second Hebbian postulate.

Increased Threshold for Spike-Timing-Dependent Plasticity Is Caused by Unreliable Calcium Signaling in Mice Lacking Fragile X Gene Fmr1

Fragile X syndrome, caused by a mutation in the Fmr1 gene, is characterized by mental retardation. Several studies reported the absence of long-term potentiation (LTP) at neocortical synapses in Fmr1 knockout (FMR1-KO) mice, but underlying cellular mechanisms are unknown. We find that in the prefrontal cortex (PFC) of FMR1-KO mice, spike-timing-dependent LTP (tLTP) is not so much absent, but rather, the threshold for tLTP induction is increased. Calcium signaling in dendrites and spines is compromised. First, dendrites and spines more often fail to show calcium transients. Second, the activity of L-type calcium channels is absent in spines. tLTP could be restored by improving reliability and amplitude of calcium signaling by increasing neuronal activity. In FMR1-KO mice that were raised in enriched environments, tLTP was restored to WT levels. Our results show that mechanisms for synaptic plasticity are in place in the FMR1-KO mouse PFC, but require stronger neuronal activity to be triggered.

Pattern storage in a sparsely coded neural network with cyclic activation

We investigate an artificial neural network model with a modified Hebb rule. It is an auto-associative neural network similar to the Hopfield model and to the Willshaw model. It has properties of both of these models. Another property is that the patterns are sparsely coded and are stored in cycles of synchronous neural activities. The cycles of activity for some ranges of parameter increase the capacity of the model. We discuss basic properties of the model and some of the implementation issues, namely optimizing of the algorithms. We describe the modification of the Hebb learning rule, the learning algorithm, the generation of patterns, decomposition of patterns into cycles and pattern recall.

Dynamic auditory processing, musical experience and language development

Children with language-learning impairments (LLI) form a heterogeneous population with the majority having both spoken and written language deficits as well as sensorimotor deficits, specifically those related to dynamic processing. Research has focused on whether or not sensorimotor deficits, specifically auditory spectrotemporal processing deficits, cause phonological deficit, leading to language and reading impairments. New trends aimed at resolving this question include prospective longitudinal studies of genetically at-risk infants, electrophysiological and neuroimaging studies, and studies aimed at evaluating the effects of auditory training (including musical training) on brain organization for language. Better understanding of the origins of developmental LLI will advance our understanding of the neurobiological mechanisms underlying individual differences in language development and lead to more effective educational and intervention strategies. This review is part of the INMED/TINS special issue "Nature and nurture in brain development and neurological disorders", based on presentations at the annual INMED/TINS symposium (http://inmednet.com/).

Long-term depression of neuron to glial signalling in rat cerebellar cortex

Bergmann glial cells enclose synapses throughout the molecular layer of the cerebellum and express extrasynaptic AMPA receptors and glutamate transporters. Accordingly, stimulation of parallel fibres leads to the generation of inward currents in the glia due to AMPA receptor activation and electrogenic uptake of glutamate. Elimination of AMPA receptor Ca(2+) permeability leads to the withdrawal of glial processes and synaptic dysfunction, suggesting that AMPA receptor-mediated Ca(2+) signalling is essential for glial support of the neuronal network. Here we show that glial extrasynaptic currents (ESCs) exhibit activity-dependent plasticity, specifically, long-term depression during repetitive stimulation of parallel fibres at low frequencies (0.033-1 Hz) -- conditions in which Purkinje neuron excitatory postsynaptic currents (EPSCs) remain stable. Both the rate of onset and the magnitude of ESC depression increased with stimulation frequency. Depression was reversible following brief periods of stimulation, but became increasingly persistent as the duration of repetitive stimulation increased. All glial currents -- AMPA receptors, glutamate transporter and a recently discovered slow 1,2,3,4-tetrahydro-6-nitro-2,3-dioxo-benzo[f]quinoxaline-7-sulphonamide (NBQX)-sensitive current -- were depressed. Increasing presynaptic release probability by doubling external Ca(2+) concentration did not affect the time course of depression, suggesting that neither decreased release probability nor fatigue of release sites contribute to depression. Inhibition of glutamate uptake caused a dramatic enhancement of the rate of depression, implicating glutamate in the underlying mechanism. The strength of neuron to glial signalling in the cerebellum is therefore dynamically regulated, independently of adjacent synapses, by the frequency of parallel fibre activity.

Non-fibrillar β-amyloid abates spike-timing-dependent synaptic potentiation at excitatory synapses in layer 2/3 of the neocortex by targeting postsynaptic AMPA receptors

Cognitive decline in Alzheimer's disease (AD) stems from the progressive dysfunction of synaptic connections within cortical neuronal microcircuits. Recently, soluble amyloid beta protein oligomers (Abeta(ol)s) have been identified as critical triggers for early synaptic disorganization. However, it remains unknown whether a deficit of Hebbian-related synaptic plasticity occurs during the early phase of AD. Therefore, we studied whether age-dependent Abeta accumulation affects the induction of spike-timing-dependent synaptic potentiation at excitatory synapses on neocortical layer 2/3 (L2/3) pyramidal cells in the APPswe/PS1dE9 transgenic mouse model of AD. Synaptic potentiation at excitatory synapses onto L2/3 pyramidal cells was significantly reduced at the onset of Abeta pathology and was virtually absent in mice with advanced Abeta burden. A decreased alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA)/N-methyl-D-aspartate (NMDA) receptor-mediated current ratio implicated postsynaptic mechanisms underlying Abeta synaptotoxicity. The integral role of Abeta(ol)s in these processes was verified by showing that pretreatment of cortical slices with Abeta((25-35)ol)s disrupted spike-timing-dependent synaptic potentiation at unitary connections between L2/3 pyramidal cells, and reduced the amplitude of miniature excitatory postsynaptic currents therein. A robust decrement of AMPA, but not NMDA, receptor-mediated currents in nucleated patches from L2/3 pyramidal cells confirmed that Abeta(ol)s perturb basal glutamatergic synaptic transmission by affecting postsynaptic AMPA receptors. Inhibition of AMPA receptor desensitization by cyclothiazide significantly increased the amplitude of excitatory postsynaptic potentials evoked by afferent stimulation, and rescued synaptic plasticity even in mice with pronounced Abeta pathology. We propose that soluble Abeta(ol)s trigger the diminution of synaptic plasticity in neocortical pyramidal cell networks during early stages of AD pathogenesis by preferentially targeting postsynaptic AMPA receptors.

Bidirectional activity-dependent plasticity at corticostriatal synapses

Corticostriatal projections originate from the entire cerebral cortex and provide the major source of glutamatergic inputs to the basal ganglia. Despite the importance of corticostriatal connections in sensorimotor learning and cognitive functions, plasticity forms at these synapses remain strongly debated. Using a corticostriatal slice preserving the connections between the somatosensory cortex and the target striatal cells, we report the induction of both non-Hebbian and Hebbian forms of long-term potentiation (LTP) and long-term depression (LTD) on striatal output neurons (SONs). LTP and LTD can be induced selectively by different stimulation patterns (high-frequency trains vs low-frequency pulses) and were evoked with similar efficiency in non-Hebbian and Hebbian modes. Combination of LTP-LTD and LTD-LTP sequences revealed that bidirectional plasticity occurs at the same SONs and provides efficient homeostatic mechanisms leading to a resetting of corticostriatal synapses avoiding synaptic saturation. The effect of temporal relationship between cortical stimulation and SON activity was assessed using spike-timing-dependent plasticity (STDP) protocols. An LTP was observed when an action potential was triggered in the striatal neuron before the cortical stimulus, and, conversely, an LTD was induced when the striatal neuron discharge was triggered after the cortical stimulation. Such STDP was reversed when compared with those described so far in other mammalian brain structures. This mechanism may be essential for the role of the striatum in learning of motor sequences in which sensory and motor events are associated in a precise time sequence.

Neural plasticity of mushroom body-extrinsic neurons in the honeybee brain

Central interneurons exiting the alpha lobe of the mushroom bodies were studied with respect to their plasticity by electrically stimulating their presynaptic inputs, the Kenyon cells. Special attention was given to the analysis of a single, identified neuron, the PE1. Three stimulation protocols were tested: double pulses, tetanus (100 Hz for 1 s), and tetanus paired with intracellular de- or hyper-polarization of the recorded cell. Double-pulse stimulations revealed short-term facilitation and depression, tuning the responses of these interneurons to frequencies in the range of 20–40 Hz. The tetanus may lead to augmentation of responses to test stimuli lasting for several minutes, or to depression followed by augmentation. Associative long-term potentiation (LTP) was induced in the PE1 neuron by pairing a presynaptic tetanus with depolarization. This is the first time that associative LTP has been found in an interneuron of the insect nervous system. These data are discussed in the context of spike tuning in the output of the mushroom body, and the potential role of associative LTP in olfactory learning. It is concluded that the honeybee mushroom body output neurons are likely to contribute to the formation of olfactory memory.

Timing in synaptic plasticity: from detection to integration

Timing of cellular and subcellular events contributes to spiking-induced modification of synapses in a variety of ways. Initially, the timing of presynaptic and postsynaptic action potentials must be translated into signals that can initiate intracellular processes. Recent experimental and computational findings suggest that the spatiotemporal details of such signals, in particular the time courses and locations of postsynaptic Ca(2+) transients, might themselves be crucial for driving potentiation and depression modules that interact in a time-dependent way to determine plasticity outcomes. On longer timescales, the effects of multiple spikes are integrated in a nonlinear manner, yielding non-intuitive plasticity results that are likely to be sensitive to local conditions and, finally, additional elements must be called into action to stabilize changes in synaptic strengths. This review is part of the TINS Synaptic Connectivity series.

Timing-dependent plasticity in human primary somatosensory cortex

Animal experiments suggest that cortical sensory representations may be remodelled as a consequence of changing synaptic efficacy by timing-dependent associative neuronal activity. Here we describe a timing-based associative form of plasticity in human somatosensory cortex. Paired associative stimulation (PAS) was performed by combining repetitive median nerve stimulation with transcranial magnetic stimulation (TMS) over the contralateral postcentral region. PAS increased exclusively the amplitude of the P25 component of the median nerve-evoked somatosensory-evoked potential (MN-SSEP), which is probably generated in the superficial cortical layers of area 3b. SSEP components reflecting neuronal activity in deeper cortical layers (N20 component) or subcortical regions (P14 component) remained constant. PAS-induced enhancement of P25 amplitude displayed topographical specificity both for the recording (MN-SSEP versus tibial nerve-SSEP) and the stimulation (magnetic stimulation targeting somatosensory versus motor cortex) arrangements. Modulation of P25 amplitude was confined to a narrow range of interstimulus intervals (ISIs) between the MN pulse and the TMS pulse, and the sign of the modulation changed with ISIs differing by only 15 ms. The function describing the ISI dependence of PAS effects on somatosensory cortex resembled one previously observed in motor cortex, shifted by approximately 7 ms. The findings suggest a simple model of modulation of excitability in human primary somatosensory cortex, possibly by mechanisms related to the spike-timing-dependent plasticity of neuronal synapses located in upper cortical layers.

Long-term potentiation and memory processes in the psychological works of Sigmund Freud and in the formation of neuropsychiatric symptoms

Far from disproving the model of mind functioning proposed by psychoanalysis, the recent advances in neuropsychiatrical research confirmed the crucial ideas of Sigmund Freud. The hypothesis that the origin of mental illnesses lies in the impossibility for a subject to erase the long-term effects of a remote adverse event is in tune with the view that several psychiatric disturbances reflect the activation of aberrant unconscious memory processes. Freud's insights did not stop here, but went on to describe in an extremely precise manner the neural mechanisms of memory formation almost a century before the description of long-term synaptic potentiation.

The Project for a Scientific Psychology (1895): a Freudian anticipation of LTP-memory connection theory

Long-term potentiation (LTP) of synaptic transmission is considered a reliable cellular model of several forms of learning and memory. Described for the first time in 1973, this synaptic phenomenon consists in the enduring facilitation of the communication between two neurons in response to the sustained activation of the synapses by which they are interconnected. In a book of 1895 entitled Project for a Scientific Psychology, Sigmund Freud theorized about the possibility of representing memory at the synaptic level as "a permanent alteration following an event", and anticipated several crucial physiological properties of LTP. In the present article we aim at presenting Freudian theory on the functional organization of the nervous system developed in the Project, with particular respect to his ideas of the cellular bases of memory.

Altered plasticity of the human motor cortex in Parkinson's disease

Interventional paired associative stimulation (IPAS) to the contralateral peripheral nerve and cerebral cortex can enhance the primary motor cortex (M1) excitability with two synchronously arriving inputs. This study investigated whether dopamine contributed to the associative long-term potentiation-like effect in the M1 in Parkinson's disease (PD) patients. Eighteen right-handed PD patients and 11 right-handed age-matched healthy volunteers were studied. All patients were studied after 12 hours off medication with levodopa replacement (PD-off). Ten patients were also evaluated after medication (PD-on). The IPAS comprised a single electric stimulus to the right median nerve at the wrist and subsequent transcranial magnetic stimulation of the left M1 with an interstimulus interval of 25 milliseconds (240 paired stimuli every 5 seconds for 20 minutes). The motor-evoked potential amplitude in the right abductor pollicis brevis muscle was increased by IPAS in healthy volunteers, but not in PD patients. IPAS did not affect the motor-evoked potential amplitude in the left abductor pollicis brevis. The ratio of the motor-evoked potential amplitude before and after IPAS in PD-off patients increased after dopamine replacement. Thus, dopamine might modulate cortical plasticity in the human M1, which could be related to higher order motor control, including motor learning.

Learning and memory

Learning and memory processes are thought to underlie a variety of human psychiatric disorders, including generalised anxiety disorder and post-traumatic stress disorder. Basic research performed in laboratory animals may help to elucidate the aetiology of the respective diseases. This chapter gives a short introduction into theoretical and practical aspects of animal experiments aimed at investigating acquisition, consolidation and extinction of aversive memories. It describes the behavioural paradigms most commonly used as well as neuroanatomical, cellular and molecular correlates of aversive memories. Finally, it discusses clinical implications of the results obtained in animal experiments in respect to the development of novel pharmacotherapeutic strategies for the treatment of human patients.

Improving language and literacy is a matter of time

Developmental deficits that affect speech perception increase the risk of language and literacy problems, which can lead to lowered academic and occupational accomplishment. Normal development and disorders of speech perception have both been linked to temporospectral auditory processing speed. Understanding the role of dynamic auditory processing in speech perception and language comprehension has led to the development of neuroplasticity-based intervention strategies aimed at ameliorating language and literacy problems and their sequelae.

Spike-Timing-Dependent Plasticity: The Relationship to Rate-Based Learning for Models with Weight Dynamics Determined by a Stable Fixed Point

Experimental evidence indicates that synaptic modification depends on the timing relationship between the presynaptic inputs and the output spikes that they generate. In this letter, results are presented for models of spike-timing-dependent plasticity (STDP) whose weight dynamics is determined by a stable fixed point. Four classes of STDP are identified on the basis of the time extent of their input-output interactions. The effect on the potentiation of synapses with different rates of input is investigated to elucidate the relationship of STDP with classical studies of long-term potentiation and depression and rate-based Hebbian learning. The selective potentiation of higher-rate synaptic inputs is found only for models where the time extent of the input-output interactions is input restricted (i.e., restricted to time domains delimited by adjacent synaptic inputs) and that have a time-asymmetric learning window with a longer time constant for depression than for potentiation. The analysis provides an account of learning dynamics determined by an input-selective stable fixed point. The effect of suppressive interspike interactions on STDP is also analyzed and shown to modify the synaptic dynamics.

Excitation-Neurogenesis Coupling in Adult Neural Stem/Progenitor Cells

A wide variety of in vivo manipulations influence neurogenesis in the adult hippocampus. It is not known, however, if adult neural stem/progenitor cells (NPCs) can intrinsically sense excitatory neural activity and thereby implement a direct coupling between excitation and neurogenesis. Moreover, the theoretical significance of activity-dependent neurogenesis in hippocampal-type memory processing networks has not been explored. Here we demonstrate that excitatory stimuli act directly on adult hippocampal NPCs to favor neuron production. The excitation is sensed via Ca(v)1.2/1.3 (L-type) Ca(2+) channels and NMDA receptors on the proliferating precursors. Excitation through this pathway acts to inhibit expression of the glial fate genes Hes1 and Id2 and increase expression of NeuroD, a positive regulator of neuronal differentiation. These activity-sensing properties of the adult NPCs, when applied as an "excitation-neurogenesis coupling rule" within a Hebbian neural network, predict significant advantages for both the temporary storage and the clearance of memories.

The legacy of Donald O. Hebb: more than the Hebb Synapse

Neuroscientists associate the name of Donald O. Hebb with the Hebbian synapse and the Hebbian learning rule, which underlie connectionist theories and synaptic plasticity, but Hebb's work has also influenced developmental psychology, neuropsychology, perception and the study of emotions, as well as learning and memory. Here, we review the work of Hebb and its lasting influence on neuroscience in honour of the 2004 centenary of his birth.