Cellular mechanisms of behavioural plasticity in simple nervous systems

In the present study, we will try to single out several principles of the nervous system functioning essential for describing the mechanisms of learning and memory, basing on our own experimental investigation of cellular mechanisms of memory in the nervous system of gastropod molluscs and literature data as follows: (1) Main changes in functioning due to learning occur in the interneurons; (2) Due to learning some synaptic inputs of command neurons selectively change its effectivity; (3) Reinforcement is not related to activity of the neural chain receptor-sensory neuron-interneuron-motoneuron-effector; reinforcement is mediated via activity of modulatory neurons, and in some cases can be exerted by a single neuron; (4) Activity of modulatory neurons is necessary for development of plastic modifications of behaviour (including associative), but is not needed for recall of conditioned responses. At the same time, the modulatory neurons (in fact they constitute a neural reinforcement system) are necessary for recall of context associative memory; (5) Changes due to learning occur at least in two independent loci in the nervous system.     

The role of synaptic ion channels in synaptic plasticity

The nervous system receives a large amount of information about the environment through elaborate sensory routes. Processing and integration of these wide-ranging inputs often results in long-term behavioural alterations as a result of past experiences. These relatively permanent changes in behaviour are manifestations of the capacity of the nervous system for learning and memory. At the cellular level, synaptic plasticity is one of the mechanisms underlying this process. Repeated neural activity generates physiological changes in the nervous system that ultimately modulate neuronal communication through synaptic transmission. Recent studies implicate both presynaptic and postsynaptic ion channels in the process of synapse strength modulation. Here, we review the role of synaptic ion channels in learning and memory, and discuss the implications and significance of these findings towards deciphering the molecular biology of learning and memory.     

Conditioned reflex: detector and command neuron

Conditioned reflex is characterized by plasticity resulting in a bilateral selective input-output linking. In simple nervous systems, input stimuli are represented by selective detectors connected with command neurons through plastic synapses strengthened during associative learning and weakened during extinction. The process of associative learning is due to temporal coincidence of excitation in both detector and command neurons. Short-term memory within a plastic synapses is mediated by phosphorilation of postsynaptic receptor molecules not requiring protein synthesis. Long-term synaptic memory parallels expression of immediate early genes that mediates structural gene expression and protein synthesis. A simple detector-command neuron association becomes more complex in the course of evolution. Input mechanism is supplemented with predetector interneurons preceding detectors. Detector selectively tuned to specific input stimulus is converging on a command neuron constitute selectivity mechanism for conditioned reflexes to complex stimuli. The complication also concerns the output mechanisms. Command neurons become more specialized, and an additional link of premotor interneurons is incorporated between command neurons and motor neurons. Via synapses, the command neurons can produce excitation in a particular set of premotor neurons controlling a specific set of motor neurons responsible for behavioral act configuration. Specialization of command neurons in combination with premotor neuron structures increases the variability of outputs. Conditioned reflexes with more complex inputs and more flexible outputs determine the diversity of acquired behaviors.     

A persistent cellular change in a single modulatory neuron contributes to associative long-term memory

Most neuronal models of learning assume that changes in synaptic strength are the main mechanism underlying long-term memory (LTM) formation. However, we show here that a persistent depolarization of membrane potential, a type of cellular change that increases neuronal responsiveness, contributes significantly to a long-lasting associative memory trace. The use of a model invertebrate network with identified neurons and known synaptic connectivity had the advantage that the contribution of this cellular change to memory could be evaluated in a neuron with a known function in the learning circuit. Specifically, we used the well-understood motor circuit underlying molluscan feeding and showed that a key modulatory neuron involved in the initiation of feeding ingestive movements underwent a long-term depolarization following behavioral associative conditioning. This depolarization led to an enhanced single cell and network responsiveness to a previously neutral tactile conditioned stimulus, and the persistence of both matched the time course of behavioral associative memory. The change in the membrane potential of a key modulatory neuron is both sufficient and necessary to initiate a conditioned response in a reduced preparation and underscores its importance for associative LTM.     

Role of delayed nonsynaptic neuronal plasticity in long-term associative memory

BACKGROUND: It is now well established that persistent nonsynaptic neuronal plasticity occurs after learning and, like synaptic plasticity, it can be the substrate for long-term memory. What still remains unclear, though, is how nonsynaptic plasticity contributes to the altered neural network properties on which memory depends. Understanding how nonsynaptic plasticity is translated into modified network and behavioral output therefore represents an important objective of current learning and memory research. RESULTS: By using behavioral single-trial classical conditioning together with electrophysiological analysis and calcium imaging, we have explored the cellular mechanisms by which experience-induced nonsynaptic electrical changes in a neuronal soma remote from the synaptic region are translated into synaptic and circuit level effects. We show that after single-trial food-reward conditioning in the snail Lymnaea stagnalis, identified modulatory neurons that are extrinsic to the feeding network become persistently depolarized between 16 and 24 hr after training. This is delayed with respect to early memory formation but concomitant with the establishment and duration of long-term memory. The persistent nonsynaptic change is extrinsic to and maintained independently of synaptic effects occurring within the network directly responsible for the generation of feeding. Artificial membrane potential manipulation and calcium-imaging experiments suggest a novel mechanism whereby the somal depolarization of an extrinsic neuron recruits command-like intrinsic neurons of the circuit underlying the learned behavior. CONCLUSIONS: We show that nonsynaptic plasticity in an extrinsic modulatory neuron encodes information that enables the expression of long-term associative memory, and we describe how this information can be translated into modified network and behavioral output.     

Learning invariant object recognition in the visual system with continuous transformations

The cerebral cortex utilizes spatiotemporal continuity in the world to help build invariant representations. In vision, these might be representations of objects. The temporal continuity typical of objects has been used in an associative learning rule with a short-term memory trace to help build invariant object representations. In this paper, we show that spatial continuity can also provide a basis for helping a system to self-organize invariant representations. We introduce a new learning paradigm “continuous transformation learning” which operates by mapping spatially similar input patterns to the same postsynaptic neurons in a competitive learning system. As the inputs move through the space of possible continuous transforms (e.g. translation, rotation, etc.), the active synapses are modified onto the set of postsynaptic neurons. Because other transforms of the same stimulus overlap with previously learned exemplars, a common set of postsynaptic neurons is activated by the new transforms, and learning of the new active inputs onto the same postsynaptic neurons is facilitated. We demonstrate that a hierarchical model of cortical processing in the ventral visual system can be trained with continuous transform learning, and highlight differences in the learning of invariant representations to those achieved by trace learning.     

Neuromodulation of STDP through short-term changes in firing causality

Spike timing dependent plasticity (STDP) likely plays an important role in forming and changing connectivity patterns between neurons in our brain. In a unidirectional synaptic connection between two neurons, it uses the causal relation between spiking activity of a presynaptic input neuron and a postsynaptic output neuron to change the strength of this connection. While the nature of STDP benefits unsupervised learning of correlated inputs, any incorporation of value into the learning process needs some form of reinforcement. Chemical neuromodulators such as Dopamine or Acetylcholine are thought to signal changes between external reward and internal expectation to many brain regions, including the basal ganglia. This effect is often modelled through a direct inclusion of the level of Dopamine as a third factor into the STDP rule. While this gives the benefit of direct control over synaptic modification, it does not account for observed instantaneous effects in neuronal activity on application of Dopamine agonists. Specifically, an instant facilitation of neuronal excitability in the striatum can not be explained by the only indirect effect that dopamine-modulated STDP has on a neuron’s firing pattern. We therefore propose a model for synaptic transmission where the level of neuromodulator does not directly influence synaptic plasticity, but instead alters the relative firing causality between pre- and postsynaptic neurons. Through the direct effect on postsynaptic activity, our rule allows indirect modulation of the learning outcome even with unmodulated, two-factor STDP. However, it also does not prohibit joint operation together with three-factor STDP rules.     

Synaptic mechanisms of associative memory in the amygdala

Do associative learning and synaptic long-term potentiation (LTP) depend on the same cellular mechanisms? Recent work in the amygdala reveals that LTP and Pavlovian fear conditioning induce similar changes in postsynaptic AMPA-type glutamate receptors and that occluding these changes by viral-mediated overexpression of a dominant-negative GluR1 construct attenuates both LTP and fear memory in rats. Novel forms of presynaptic plasticity in the lateral nucleus may also contribute to fear memory formation, bolstering the connection between synaptic plasticity mechanisms and associative learning and memory.     

Physiology of synapse: from molecular modules to retrograde modulation

Synapses are highly organized, specific structures assuring rapid and highly selective interactions between cells. Synaptic transmission involves the release of neurotransmitter from presynaptic neurons and its detection by specific ligand-gated ion channels at the surface membrane of postsynaptic neurons. The protenomic analysis shows that for self-formation and functioning of synapses nearly 2000 proteins are involved in mammalian brain. The core complex in excitatory synapses includes glutamate receptors, potassium channels, CaMKII, scaffolding protein and actin. These proteins exist as part of a highly organized protein complex known as the postsynaptic density (PSD). The coordinated functioning of the different PSD components determines the strength of signalling between the pre- and postsynaptic neurons. Synaptic plasticity is regulated by changes in the amount of receptors on the postsynaptic membrane, changes in the shape and size of dendritic spines, posttranslational modification of PSD components, modulation kinetics of synthesis and degradation of proteins. Integration of these processes leads to long-lasting changes in synaptic function and neuronal networks underlying learning-related plasticity, memory and information treatment in nervous system of multicellular organisms.     

Natural patterns of activity and long-term synaptic plasticity

Long-term potentiation (LTP) of synaptic transmission is traditionally elicited by massively synchronous, high-frequency inputs, which rarely occur naturally. Recent in vitro experiments have revealed that both LTP and long-term depression (LTD) can arise by appropriately pairing weak synaptic inputs with action potentials in the postsynaptic cell. This discovery has generated new insights into the conditions under which synaptic modification may occur in pyramidal neurons in vivo. First, it has been shown that the temporal order of the synaptic input and the postsynaptic spike within a narrow temporal window determines whether LTP or LTD is elicited, according to a temporally asymmetric Hebbian learning rule. Second, backpropagating action potentials are able to serve as a global signal for synaptic plasticity in a neuron compared with local associative interactions between synaptic inputs on dendrites. Third, a specific temporal pattern of activity--postsynaptic bursting--accompanies synaptic potentiation in adults.     

Divergent and convergent mechanisms of the integrative activity of the mammalian brain

Convergent intercellular synaptic interaction is actualized, mainly, by two neurotransmitter systems: glutamate- and GABA-ergic (excitatory and inhibitory postsynaptic potentials, respectively). Fast and slow postsynaptic receptors of glutamate- and GABA-ergic synapses are described. All other brain systems are divergent neuromodulators. Modulators are released into the intercellular space and simultaneously interact with a large population of neurons. A hypothesis of divergent modulatory integration is described: the divergently functioning neuromodulators actualize stable functional states of the brain via appropriate long-term modification-inducing receptors. These stable states are a biochemical basis of the motivational and emotional states. Mechanisms of the secondary nuclear signaling triggered by the long-term modification-inducing receptors consolidate the stable states. The hypothesis of divergent modulatory integration is substantiated in the paper on the basis of the evidence obtained by the author and his collaborates. The haloperidol catalepsy and pentile-netetrazole kindling are considered as a behavioral model of the divergent modulatory integration. The experimental data suggest that divergently functioning neuromodulators actualize and consolidate general motivational and emotional states via the appropriate long-term modification-inducing receptors. The consolidation is structurally-specific. The motivational and emotional states concomitant of learning and memory are a specific variation of the general motivational and emotional state depending on the learning situation.     

Synapse formation and remodeling

Synapses are specialized structures that mediate information flow between neurons and target cells,and thus are the basis for neuronal system to execute various functions,including learning and memory.There are around 1011 neurons in the human brain,with each neuron receiving thousands of synaptic inputs,either excitatory or inhibitory.A synapse is an asymmetric structure that is composed of pre-synaptic axon terminals,synaptic cleft,and postsynaptic compartments.Synapse formation involves a number of cell ...     

Disinhibition Mediates a Form of Hippocampal Long-Term Potentiation in Area CA1

The hippocampus plays a central role in memory formation in the mammalian brain. Its ability to encode information is thought to depend on the plasticity of synaptic connections between neurons. In the pyramidal neurons constituting the primary hippocampal output to the cortex, located in area CA1, firing of presynaptic CA3 pyramidal neurons produces monosynaptic excitatory postsynaptic potentials (EPSPs) followed rapidly by feedforward (disynaptic) inhibitory postsynaptic potentials (IPSPs). Long-term potentiation (LTP) of the monosynaptic glutamatergic inputs has become the leading model of synaptic plasticity, in part due to its dependence on NMDA receptors (NMDARs), required for spatial and temporal learning in intact animals. Using whole-cell recording in hippocampal slices from adult rats, we find that the efficacy of synaptic transmission from CA3 to CA1 can be enhanced without the induction of classic LTP at the glutamatergic inputs. Taking care not to directly stimulate inhibitory fibers, we show that the induction of GABAergic plasticity at feedforward inhibitory inputs results in the reduced shunting of excitatory currents, producing a long-term increase in the amplitude of Schaffer collateral-mediated postsynaptic potentials. Like classic LTP, disinhibition-mediated LTP requires NMDAR activation, suggesting a role in types of learning and memory attributed primarily to the former and raising the possibility of a previously unrecognized target for therapeutic intervention in disorders linked to memory deficits, as well as a potentially overlooked site of LTP expression in other areas of the brain.     

A pair of inhibitory neurons are required to sustain labile memory in the Drosophila mushroom body

Labile memory is thought to be held in the brain as persistent neural network activity. However, it is not known how biologically relevant memory circuits are organized and operate. Labile and persistent appetitive memory in Drosophila requires output after training from the α'β' subset of mushroom body (MB) neurons and from a pair of modulatory dorsal paired medial (DPM) neurons. DPM neurons innervate the entire MB lobe region and appear to be pre- and postsynaptic to the MB, consistent with a recurrent network model. Here we identify a role after training for synaptic output from the GABAergic anterior paired lateral (APL) neurons. Blocking synaptic output from APL neurons after training disrupts labile memory but does not affect long-term memory. APL neurons contact DPM neurons most densely in the α'β' lobes, although their processes are intertwined and contact throughout all of the lobes. Furthermore, APL contacts MB neurons in the α' lobe but makes little direct contact with those in the distal α lobe. We propose that APL neurons provide widespread inhibition to stabilize and maintain synaptic specificity of a labile memory trace in a recurrent DPM and MB α'β' neuron circuit.     

Spatio-temporal credit assignment in neuronal population learning

In learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.     

Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations

Humans are able to form internal representations of the information they process—a capability which enables them to perform many different memory tasks. Therefore, the neural system has to learn somehow to represent aspects of the environmental situation; this process is assumed to be based on synaptic changes. The situations to be represented are various as for example different types of static patterns but also dynamic scenes. How are neural networks consisting of mutually connected neurons capable of performing such tasks? Here we propose a new neuronal structure for artificial neurons. This structure allows one to disentangle the dynamics of the recurrent connectivity from the dynamics induced by synaptic changes due to the learning processes. The error signal is computed locally within the individual neuron. Thus, online learning is possible without any additional structures. Recurrent neural networks equipped with these computational units cope with different memory tasks. Examples illustrate how information is extracted from environmental situations comprising fixed patterns to produce sustained activity and to deal with simple algebraic relations.     

Structural homeostasis: compensatory adjustments of dendritic arbor geometry in response to variations of synaptic input

As the nervous system develops, there is an inherent variability in the connections formed between differentiating neurons. Despite this variability, neural circuits form that are functional and remarkably robust. One way in which neurons deal with variability in their inputs is through compensatory, homeostatic changes in their electrical properties. Here, we show that neurons also make compensatory adjustments to their structure. We analysed the development of dendrites on an identified central neuron (aCC) in the late Drosophila embryo at the stage when it receives its first connections and first becomes electrically active. At the same time, we charted the distribution of presynaptic sites on the developing postsynaptic arbor. Genetic manipulations of the presynaptic partners demonstrate that the postsynaptic dendritic arbor adjusts its growth to compensate for changes in the activity and density of synaptic sites. Blocking the synthesis or evoked release of presynaptic neurotransmitter results in greater dendritic extension. Conversely, an increase in the density of presynaptic release sites induces a reduction in the extent of the dendritic arbor. These growth adjustments occur locally in the arbor and are the result of the promotion or inhibition of growth of neurites in the proximity of presynaptic sites. We provide evidence that suggest a role for the postsynaptic activity state of protein kinase A in mediating this structural adjustment, which modifies dendritic growth in response to synaptic activity. These findings suggest that the dendritic arbor, at least during early stages of connectivity, behaves as a homeostatic device that adjusts its size and geometry to the level and the distribution of input received. The growing arbor thus counterbalances naturally occurring variations in synaptic density and activity so as to ensure that an appropriate level of input is achieved.     

Modulation of synaptic events by heavy metals in the central nervous system of mollusks

Summary 1. The effects of heavy metals (Pb²⁺, Hg²⁺, and Zn²⁺) on synaptic transmission in the identified neural network ofHelix pomatia L. andLymnaea stagnalis L. (Gastropoda, Mollusca) were studied, with investigation of effects on inputs and outputs as wells as on interneuronal connections.2. The sensory input running from the cardiorenal system to the central nervous system and the synaptic connections between central neurons were affected by heavy metals.3. Lead and mercury (10^–5–10^–3 M) eliminated first the inhibitory, then the excitatory inputs running from the heart to central neurons. At the onset of action lead increased the amplitude of the excitatory postsynaptic potentials, but blockade of sensory information transfer occurred after 10–20 min of treatment.4. The monosynaptic connections between identified interneurons were inhibited by lead and mercury but not by zinc. Motoneurons were found to be less sensitive to heavy metal treatment than interneurons or sensory pathways.5. The treatment with Pb²⁺ and Hg²⁺ often elicited pacemaker and bursting-type firing in central neurons, accompanied by disconnection of synaptic pathways, manifested by insensitivity to sensory synaptic influences.6. Zn²⁺ treatment also sometimes induced pacemaker activity and burst firing but did not cause disconnection of the synaptic transmission between interneurons.7. A network analysis of heavy metal effects can be a useful tool in understanding the connection between their cellular and their behavioral modulatory influences.     

Mapping molecular memory: navigating the cellular pathways of learning

A consolidated map of the signalling pathways that function in the formation of short- and long-term cellular memory could be considered the ultimate means of defining the molecular basis of learning. Research has established that experience-dependent activation of these complex cellular cascades leads to many changes in the composition and functioning of a neuron's proteome, resulting in the modulation of its synaptic strength and structure. However, although generally accepted that synaptic plasticity is the mechanism whereby memories are stored in the brain, there is much controversy over whether the site of this neuronal memory expression is predominantly pre- or postsynaptic. Much of the early research into the neuromolecular mechanisms of memory performed using the model organism, the marine snail Aplysia, has focused on the associated presynaptic events. Recently however, postsynaptic mechanisms have been shown to contribute definitively to long term memory processes, and are in fact critical for persistent learning-induced synaptic changes. In this review, in which we aimed to integrate many of the early and recent advances concerning coordinated neuronal signaling in both the pre- and postsynaptic neurons, we have provided a detailed account of the diverse cellular events that lead to modifications in synaptic strength. Thus, a comprehensive synaptic model is presented that could explain a few of the shortcomings that arise when the presynaptic and postsynaptic changes are considered separately. Although it is clear that there is still much to be learnt and that the exact nature of many of the signalling cascades and their components are yet to be fully understood, this still incomplete but integrated illustrative map of the cellular pathways involved provides an overview which expands understanding of the neuromolecular mechanisms of learning and memory.     

Network Evolution Induced by Asynchronous Stimuli through Spike-Timing-Dependent Plasticity

In sensory neural system, external asynchronous stimuli play an important role in perceptual learning, associative memory and map development. However, the organization of structure and dynamics of neural networks induced by external asynchronous stimuli are not well understood. Spike-timing-dependent plasticity (STDP) is a typical synaptic plasticity that has been extensively found in the sensory systems and that has received much theoretical attention. This synaptic plasticity is highly sensitive to correlations between pre- and postsynaptic firings. Thus, STDP is expected to play an important role in response to external asynchronous stimuli, which can induce segregative pre- and postsynaptic firings. In this paper, we study the impact of external asynchronous stimuli on the organization of structure and dynamics of neural networks through STDP. We construct a two-dimensional spatial neural network model with local connectivity and sparseness, and use external currents to stimulate alternately on different spatial layers. The adopted external currents imposed alternately on spatial layers can be here regarded as external asynchronous stimuli. Through extensive numerical simulations, we focus on the effects of stimulus number and inter-stimulus timing on synaptic connecting weights and the property of propagation dynamics in the resulting network structure. Interestingly, the resulting feedforward structure induced by stimulus-dependent asynchronous firings and its propagation dynamics reflect both the underlying property of STDP. The results imply a possible important role of STDP in generating feedforward structure and collective propagation activity required for experience-dependent map plasticity in developing in vivo sensory pathways and cortices. The relevance of the results to cue-triggered recall of learned temporal sequences, an important cognitive function, is briefly discussed as well. Furthermore, this finding suggests a potential application for examining STDP by measuring neural population activity in a cultured neural network.