The radish gene reveals a memory component with variable temporal properties

Memory phases, dependent on different neural and molecular mechanisms, strongly influence memory performance. Our understanding, however, of how memory phases interact is far from complete. In Drosophila, aversive olfactory learning is thought to progress from short-term through long-term memory phases. Another memory phase termed anesthesia resistant memory, dependent on the radish gene, influences memory hours after aversive olfactory learning. How does the radish-dependent phase influence memory performance in different tasks? It is found that the radish memory component does not scale with the stability of several memory traces, indicating a specific recruitment of this component to influence different memories, even within minutes of learning.     

New insights in human memory interference and consolidation

Learning new facts and skills in succession can be frustrating because no sooner has new knowledge been acquired than its retention is being jeopardized by learning another set of skills or facts. Interference between memories has recently provided important new insights into the neural and psychological systems responsible for memory processing. For example, interference not only occurs between the same types of memories, but can also occur between different types of memories, which has important implications for our understanding of memory organization. Converging evidence has begun to reveal that the brain produces interference independently from other aspects of memory processing, which suggests that interference may have an important but previously overlooked function. A memory's initial susceptibility to interference and subsequent resistance to interference after its acquisition has revealed that memories continue to be processed 'off-line' during consolidation. Recent work has demonstrated that off-line processing is not limited to just the stabilization of a memory, which was once the defining characteristic of consolidation; instead, off-line processing can have a rich diversity of effects, from enhancing performance to making hidden rules explicit. Off-line processing also occurs after memory retrieval when memories are destabilized and then subsequently restabalized during reconsolidation. Studies are beginning to reveal the function of reconsolidation, its mechanistic relationship to consolidation and its potential as a therapeutic target for the modification of memories.     

Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to alleviate the problem of catastrophic forgetting.     

Prox1 Is Required for Granule Cell Maturation and Intermediate Progenitor Maintenance During Brain Neurogenesis

The dentate gyrus has an important role in learning and memory, and adult neurogenesis in the subgranular zone of the dentate gyrus may play a role in the acquisition of new memories. The homeobox gene Prox1 is expressed in the dentate gyrus during embryonic development and adult neurogenesis. Here we show that Prox1 is necessary for the maturation of granule cells in the dentate gyrus during development and for the maintenance of intermediate progenitors during adult neurogenesis. We also demonstrate that Prox1-expressing intermediate progenitors are required for adult neural stem cell self-maintenance in the subgranular zone; thus, we have identified a previously unknown non-cell autonomous regulatory feedback mechanism that controls adult neurogenesis in this region of the mammalian brain. Finally, we show that the ectopic expression of Prox1 induces premature differentiation of neural stem cells.     

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

While the subject of learning has attracted immense interest from both behavioral and neural scientists, only relatively few investigators have observed single-neuron activity while animals are acquiring an operantly conditioned response, or when that response is extinguished. But even in these cases, observation periods usually encompass only a single stage of learning, i.e. acquisition or extinction, but not both (exceptions include protocols employing reversal learning; see Bingman et al.¹ for an example). However, acquisition and extinction entail different learning mechanisms and are therefore expected to be accompanied by different types and/or loci of neural plasticity.Accordingly, we developed a behavioral paradigm which institutes three stages of learning in a single behavioral session and which is well suited for the simultaneous recording of single neurons'' action potentials. Animals are trained on a single-interval forced choice task which requires mapping each of two possible choice responses to the presentation of different novel visual stimuli (acquisition). After having reached a predefined performance criterion, one of the two choice responses is no longer reinforced (extinction). Following a certain decrement in performance level, correct responses are reinforced again (reacquisition). By using a new set of stimuli in every session, animals can undergo the acquisition-extinction-reacquisition process repeatedly. Because all three stages of learning occur in a single behavioral session, the paradigm is ideal for the simultaneous observation of the spiking output of multiple single neurons. We use pigeons as model systems, but the task can easily be adapted to any other species capable of conditioned discrimination learning.     

Social learning of fear and safety is determined by the demonstrator's racial group

Social learning offers an efficient route through which humans and other animals learn about potential dangers in the environment. Such learning inherently relies on the transmission of social information and should imply selectivity in what to learn from whom. Here, we conducted two observational learning experiments to assess how humans learn about danger and safety from members (‘demonstrators'') of an other social group than their own. We show that both fear and safety learning from a racial in-group demonstrator was more potent than learning from a racial out-group demonstrator.     

Impact of active dendrites and structural plasticity on the memory capacity of neural tissue

We consider the combined effects of active dendrites and structural plasticity on the storage capacity of neural tissue. We compare capacity for two different modes of dendritic integration: (1) linear, where synaptic inputs are summed across the entire dendritic arbor, and (2) nonlinear, where each dendritic compartment functions as a separately thresholded neuron-like summing unit. We calculate much larger storage capacities for cells with nonlinear subunits and show that this capacity is accessible to a structural learning rule that combines random synapse formation with activity-dependent stabilization/elimination. In a departure from the common view that memories are encoded in the overall connection strengths between neurons, our results suggest that long-term information storage in neural tissue could reside primarily in the selective addressing of synaptic contacts onto dendritic subunits.     

Development of a spin-glass model of prebiotic evolution: environmental effects on ensembles of genetic polymers

We develop in two ways an existing spin-glass model of prebiotic polymer evolution. First, by choosing the environment J in a prescribed manner, similar to neural network presciptions, we may create an environment which favors a linearly independent set of evolutionary niches (Ea). That is, we may control which polymer "species" will evolve in our system. Computer simulations confirm this result. We obtain a quantitative value for the sharpness of a niche. Second, we extend the model by allowing a surviving polymer to act upon--to "remold"--its environment; the nature of the environmental action is governed by the "molding" matrix M. When the mold M is the identity matrix, the feedback algorithm reduces to a Hebb learning algorithm form, and a surviving polymer acts to enhance its own survival prospects. Molds having a structure analogous to (temporal) associative memories in neural networks can generate autocatalytic species or can exhibit symbiotic interspecies relationships.     

Hippocampal Lesions Impair Rapid Learning of a Continuous Spatial Alternation Task

The hippocampus is essential for the formation of memories for events, but the specific features of hippocampal neural activity that support memory formation are not yet understood. The ideal experiment to explore this issue would be to monitor changes in hippocampal neural coding throughout the entire learning process, as subjects acquire and use new episodic memories to guide behavior. Unfortunately, it is not clear whether established hippocampally-dependent learning paradigms are suitable for this kind of experiment. The goal of this study was to determine whether learning of the W-track continuous alternation task depends on the hippocampal formation. We tested six rats with NMDA lesions of the hippocampal formation and four sham-operated controls. Compared to controls, rats with hippocampal lesions made a significantly higher proportion of errors and took significantly longer to reach learning criterion. The effect of hippocampal lesion was not due to a deficit in locomotion or motivation, because rats with hippocampal lesions ran well on a linear track for food reward. Rats with hippocampal lesions also exhibited a pattern of perseverative errors during early task experience suggestive of an inability to suppress behaviors learned during pretraining on a linear track. Our findings establish the W-track continuous alternation task as a hippocampally-dependent learning paradigm which may be useful for identifying changes in the neural representation of spatial sequences and reward contingencies as rats learn and apply new task rules.     

Active reversal of motor memories reveals rules governing memory encoding

Learning systems must be able to store memories reliably, yet be able to modify them when new learning is required. At the mechanistic level, new learning may either reverse the cellular events mediating the storage of old memories or mask the old memories with additional cellular changes that preserve the old cellular events in a latent form. Behavioral evidence about whether reversal or masking occurs in a particular circuit can constrain the cellular mechanisms used to store memories. Here we examine these constraints for a simple cerebellum-dependent learning task, motor learning in the vestibulo-ocular reflex (VOR). Learning can change the amplitude of the VOR in two opposite directions. Contrary to previous models about memory encoding by the cerebellum, our results indicate that these behavioral changes are implemented by different plasticity mechanisms, which reverse each other with unequal efficacy.     

A neural network study on the dynamic identification of a fermentation system

The objective of this paper is to propose neural networks for the study of dynamic identification and prediction of a fermentation system which produces mainly 2,3-butanediol (2,3-BDL). The metabolic products of the fermentation, acetic acid, acetoin, ethanol, and 2,3-BDL were measured on-line via a mass spectrometer modified by the insertion of a dimethylvinylsilicone membrane probe. The measured data at different sampling times were included as the input and output nodes, at different learning batches, of the network. A fermentation system is usually nonlinear and dynamic in nature. Measured fermentation data obtained from the complex metabolic pathways are often difficult to be entirely included in a static process model, therefore, a dynamic model was suggested instead. In this work, neural networks were provided by a dynamic learning and prediction process that moved along the time sequence batchwise. In other words, a scheme of two-dimensional moving window (number of input nodes by the number of training data) was proposed for reading in new data while forgetting part of the old data. Proper size of the network including proper number of input/output nodes were determined by trained with the real-time fermentation data. Different number of hidden nodes under the consideration of both learning performance and computation efficiency were tested. The data size for each learning batch was determined. The performance of the learning factors such as the learning coefficient η and the momentum term coefficient α were also discussed. The effect of different dynamic learning intervals, with different starting points and the same ending point, both on the learning and prediction performance were studied. On the other hand, the effect of different dynamic learning intervals, with the same starting point and different ending points, was also investigated. The size of data sampling interval was also discussed. The performance from four different types of transfer functions, x/(1+|x|), sgn(x)·x ²/(1+x ²), 2/(1+e ^{? x} )?1, and 1/(1+e ^{? x} ) was compared. A scaling factor b was added to the transfer function and the effect of this factor on the learning was also evaluated. The prediction results from the time-delayed neural networks were also studied.     

Memory Maintenance in Synapses with Calcium-Based Plasticity in the Presence of Background Activity

Most models of learning and memory assume that memories are maintained in neuronal circuits by persistent synaptic modifications induced by specific patterns of pre- and postsynaptic activity. For this scenario to be viable, synaptic modifications must survive the ubiquitous ongoing activity present in neural circuits in vivo. In this paper, we investigate the time scales of memory maintenance in a calcium-based synaptic plasticity model that has been shown recently to be able to fit different experimental data-sets from hippocampal and neocortical preparations. We find that in the presence of background activity on the order of 1 Hz parameters that fit pyramidal layer 5 neocortical data lead to a very fast decay of synaptic efficacy, with time scales of minutes. We then identify two ways in which this memory time scale can be extended: (i) the extracellular calcium concentration in the experiments used to fit the model are larger than estimated concentrations in vivo. Lowering extracellular calcium concentration to in vivo levels leads to an increase in memory time scales of several orders of magnitude; (ii) adding a bistability mechanism so that each synapse has two stable states at sufficiently low background activity leads to a further boost in memory time scale, since memory decay is no longer described by an exponential decay from an initial state, but by an escape from a potential well. We argue that both features are expected to be present in synapses in vivo. These results are obtained first in a single synapse connecting two independent Poisson neurons, and then in simulations of a large network of excitatory and inhibitory integrate-and-fire neurons. Our results emphasise the need for studying plasticity at physiological extracellular calcium concentration, and highlight the role of synaptic bi- or multistability in the stability of learned synaptic structures.     

A Neural Mechanism for Background Information-Gated Learning Based on Axonal-Dendritic Overlaps

Experiencing certain events triggers the acquisition of new memories. Although necessary, however, actual experience is not sufficient for memory formation. One-trial learning is also gated by knowledge of appropriate background information to make sense of the experienced occurrence. Strong neurobiological evidence suggests that long-term memory storage involves formation of new synapses. On the short time scale, this form of structural plasticity requires that the axon of the pre-synaptic neuron be physically proximal to the dendrite of the post-synaptic neuron. We surmise that such “axonal-dendritic overlap” (ADO) constitutes the neural correlate of background information-gated (BIG) learning. The hypothesis is based on a fundamental neuroanatomical constraint: an axon must pass close to the dendrites that are near other neurons it contacts. The topographic organization of the mammalian cortex ensures that nearby neurons encode related information. Using neural network simulations, we demonstrate that ADO is a suitable mechanism for BIG learning. We model knowledge as associations between terms, concepts or indivisible units of thought via directed graphs. The simplest instantiation encodes each concept by single neurons. Results are then generalized to cell assemblies. The proposed mechanism results in learning real associations better than spurious co-occurrences, providing definitive cognitive advantages.     

Place Cell Rate Remapping by CA3 Recurrent Collaterals

Episodic-like memory is thought to be supported by attractor dynamics in the hippocampus. A possible neural substrate for this memory mechanism is rate remapping, in which the spatial map of place cells encodes contextual information through firing rate variability. To test whether memories are stored as multimodal attractors in populations of place cells, recent experiments morphed one familiar context into another while observing the responses of CA3 cell ensembles. Average population activity in CA3 was reported to transition gradually rather than abruptly from one familiar context to the next, suggesting a lack of attractive forces associated with the two stored representations. On the other hand, individual CA3 cells showed a mix of gradual and abrupt transitions at different points along the morph sequence, and some displayed hysteresis which is a signature of attractor dynamics. To understand whether these seemingly conflicting results are commensurate with attractor network theory, we developed a neural network model of the CA3 with attractors for both position and discrete contexts. We found that for memories stored in overlapping neural ensembles within a single spatial map, position-dependent context attractors made transitions at different points along the morph sequence. Smooth transition curves arose from averaging across the population, while a heterogeneous set of responses was observed on the single unit level. In contrast, orthogonal memories led to abrupt and coherent transitions on both population and single unit levels as experimentally observed when remapping between two independent spatial maps. Strong recurrent feedback entailed a hysteretic effect on the network which diminished with the amount of overlap in the stored memories. These results suggest that context-dependent memory can be supported by overlapping local attractors within a spatial map of CA3 place cells. Similar mechanisms for context-dependent memory may also be found in other regions of the cerebral cortex.     

Learning in honeybees: from molecules to behaviour

Studies in a variety of organisms as diverse as molluscs, insects, birds and mammals have shown that memories can exist in a variety of temporal domains ranging from short-term memories in the range of minutes to long-term memories lasting a lifetime. While transient covalent modifications of proteins underlie short-term memory, the formation of long-term memory requires gene expression and protein synthesis. Different intracellular signalling cascades have been implicated in distinct aspects of learning and memory formation. Little is known however, about how learning in intact animals is related to the modulation of these signalling cascades and how this contributes to distinct neuronal and behavioural changes in vivo. Associative learning in the honeybee provides the opportunity to study processes of memory formation by analysing its progression through different phases, across levels of behaviour, neural circuits, and cellular signalling pathways. The findings reveal evidence that various cellular signalling pathways in the neuronal circuit of distinct brain areas play a role in different processes during learning and memory formation.     

Stimulation of the left dorsolateral prefrontal cortex with slow rTMS enhances verbal memory formation

Encoding of episodic memories relies on stimulus-specific information processing and involves the left prefrontal cortex. We here present an incidental finding from a simultaneous EEG-TMS experiment as well as a replication of this unexpected effect. Our results reveal that stimulating the left dorsolateral prefrontal cortex (DLPFC) with slow repetitive transcranial magnetic stimulation (rTMS) leads to enhanced word memory performance. A total of 40 healthy human participants engaged in a list learning paradigm. Half of the participants (N = 20) received 1 Hz rTMS to the left DLPFC, while the other half (N = 20) received 1 Hz rTMS to the vertex and served as a control group. Participants receiving left DLPFC stimulation demonstrated enhanced memory performance compared to the control group. This effect was replicated in a within-subjects experiment where 24 participants received 1 Hz rTMS to the left DLPFC and vertex. In this second experiment, DLPFC stimulation also induced better memory performance compared to vertex stimulation. In addition to these behavioural effects, we found that 1 Hz rTMS to DLPFC induced stronger beta power modulation in posterior areas, a state that is known to be beneficial for memory encoding. Further analysis indicated that beta modulations did not have an oscillatory origin. Instead, the observed beta modulations were a result of a spectral tilt, suggesting inhibition of these parietal regions. These results show that applying 1 Hz rTMS to DLPFC, an area involved in episodic memory formation, improves memory performance via modulating neural activity in parietal regions.

Encoding of episodic memories relies on stimulus-specific information processing and involves the left prefrontal cortex. An incidental finding from a simultaneous EEG-TMS experiment reveals that applying 1-Hz repetitive transcranial magnetic stimulation to this area of the brain improves memory performance by modulating neural activity in parietal regions.     

An observational learning task using Barnes maze in rats

Observational learning, which modulates one’s own behavior by observing the adaptive behavior of others, is crucial for behaving efficiently in social communities. Although many behavioral experiments have reported observational learning in monkeys and humans, its neural mechanisms are still unknown. In order to conduct neuroscientific researches with recording neural activities, we developed an observational learning task for rats. We designed the task using Barnes circular maze and then tested whether rats (observers) could actually improve their learning by observing the behavior of other rats (models) that had already acquired the task. The result showed that the observer rats, which were located in a metal wire mesh cylinder at the center of the maze and allowed to observe model rats escaping to the goal in the maze, demonstrated significantly faster escape behavior than the model rats. Thus, the present study confirmed that rats can efficiently learn the behavioral task by observing the behavior of other rats; this shows that it is conceivable to elucidate the neural mechanisms of social interaction by analyzing neural activity in observer rats performing the observational learning task.     

Learning multiple routes in homing pigeons

The aerial lifestyle of central-place foraging birds allows wide-ranging movements, raising fundamental questions about their remarkable navigation and memory systems. For example, we know that pigeons (Columba livia), long-standing models for avian navigation, rely on individually distinct routes when homing from familiar sites. But it remains unknown how they cope with the task of learning several routes in parallel. Here, we examined how learning multiple routes influences homing in pigeons. We subjected groups of pigeons to different training protocols, defined by the sequence in which they were repeatedly released from three different sites, either sequentially, in rotation or randomly. We observed that pigeons from all groups successfully developed and applied memories of the different release sites (RSs), irrespective of the training protocol, and that learning several routes in parallel did not impair their capacity to quickly improve their homing efficiency over multiple releases. Our data also indicated that they coped with increasing RS uncertainty by adjusting both their initial behaviour upon release and subsequent homing efficiency. The results of our study broaden our understanding of avian route following and open new possibilities for studying learning and memory in free-flying animals.     

Skill Memory Escaping from Distraction by Sleep��Evidence from Dual-Task Performance

Background

Sleep facilitates off-line consolidation of memories, as shown for learning of motor skills in the absence of concomitant distractors. We often perform complex tasks focusing our attention mostly on one single part of them. However, we are equally able to skillfully perform other concurrent tasks. One may even improve performance on disregarded parts of complex tasks, which were learned implicitly. In the present study we investigated the role of sleep in the off-line consolidation of procedural skills when attention is diverted from the procedural task because of interference from a concurrent task.

Methodology/Principal Findings

We used a dual-task paradigm containing (i) procedural serial reaction time task (SRTT), which was labeled as subordinate and unimportant and (ii) declarative word-pair association task (WPAT), performed concomitantly. The WPAT served as a masked distractor to SRTT and was strongly reinforced by the instructions. One experimental and three control groups were tested. The experimental group was re-tested after two nights of sleep (sleep group, SG). The first control group had sleep deprivation on the first post-learning night (nighttime-awake group, NA), the second control group was tested in the morning and then re-tested after 12-hours (daytime-awake group, DA); the third one had the same assignments as DA but with a subsequent, instead of a concomitant, WPAT (daytime-awake-subsequent-WPAT group, DAs). We found SRTT performance gains in SG but not in NA and DA groups. Furthermore, SG reached similar learning gains in SRTT as the DAs group, which gained in SRTT performance because of post-training interference from the declarative task.

Conclusions/Significance

The results demonstrate that sleep allows off-line consolidation, which is resistant to deteriorating effects of a reinforced distractor on the implicit procedural learning and allowing for gains which are consistent with those produced when inhibited declarative memories of SRTT do not compete with procedural ones.     

Acetylcholine-gated current translates wake neuronal firing rate information into a spike timing-based code in Non-REM sleep,stabilizing neural network dynamics during memory consolidation

Sleep is critical for memory consolidation, although the exact mechanisms mediating this process are unknown. Combining reduced network models and analysis of in vivo recordings, we tested the hypothesis that neuromodulatory changes in acetylcholine (ACh) levels during non-rapid eye movement (NREM) sleep mediate stabilization of network-wide firing patterns, with temporal order of neurons’ firing dependent on their mean firing rate during wake. In both reduced models and in vivo recordings from mouse hippocampus, we find that the relative order of firing among neurons during NREM sleep reflects their relative firing rates during prior wake. Our modeling results show that this remapping of wake-associated, firing frequency-based representations is based on NREM-associated changes in neuronal excitability mediated by ACh-gated potassium current. We also show that learning-dependent reordering of sequential firing during NREM sleep, together with spike timing-dependent plasticity (STDP), reconfigures neuronal firing rates across the network. This rescaling of firing rates has been reported in multiple brain circuits across periods of sleep. Our model and experimental data both suggest that this effect is amplified in neural circuits following learning. Together our data suggest that sleep may bias neural networks from firing rate-based towards phase-based information encoding to consolidate memories.