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The apical dendrites of pyramidal neurons in sensory cortex receive primarily top-down signals from associative and motor regions, while cell bodies and nearby dendrites are heavily targeted by locally recurrent or bottom-up inputs from the sensory periphery. Based on these differences, a number of theories in computational neuroscience postulate a unique role for apical dendrites in learning. However, due to technical challenges in data collection, little data is available for comparing the responses of apical dendrites to cell bodies over multiple days. Here we present a dataset collected through the Allen Institute Mindscope’s OpenScope program that addresses this need. This dataset comprises high-quality two-photon calcium imaging from the apical dendrites and the cell bodies of visual cortical pyramidal neurons, acquired over multiple days in awake, behaving mice that were presented with visual stimuli. Many of the cell bodies and dendrite segments were tracked over days, enabling analyses of how their responses change over time. This dataset allows neuroscientists to explore the differences between apical and somatic processing and plasticity.

Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysical models capture these processes directly by explicitly modelling physiological variables, such as ion channels, current flow, membrane capacitance, etc. However, another option for capturing the complexities of real neural computation is to use cascade models, which treat individual neurons as a cascade of linear and non-linear operations, akin to a multi-layer artificial neural network. Recent research has shown that cascade models can capture single-cell computation well, but there are still a number of sub-cellular, regenerative dendritic phenomena that they cannot capture, such as the interaction between sodium, calcium, and NMDA spikes in different compartments. Here, we propose that it is possible to capture these additional phenomena usingparallel, recurrentcascade models, wherein an individual neuron is modelled as a cascade of parallel linear and non-linear operations that can be connected recurrently, akin to a multi-layer, recurrent, artificial neural network. Given their tractable mathematical structure, we show that neuron models expressed in terms of parallel recurrent cascades can themselves be integrated into multi-layered artificial neural networks and trained to perform complex tasks. We go on to discuss potential implications and uses of these models for artificial intelligence. Overall, we argue that parallel, recurrent cascade models provide an important, unifying tool for capturing single-cell computation and exploring the algorithmic implications of physiological phenomena.

New neurons are continuously generated in the subgranular zone of the dentate gyrus throughout adulthood. These new neurons gradually integrate into hippocampal circuits, forming new naive synapses. Viewed from this perspective, these new neurons may represent a significant source of “wiring” noise in hippocampal networks. In machine learning, such noise injection is commonly used as a regularization technique. Regularization techniques help prevent overfitting training data and allow models to generalize learning to new, unseen data. Using a computational modeling approach, here we ask whether a neurogenesis-like process similarly acts as a regularizer, facilitating generalization in a category learning task. In a convolutional neural network (CNN) trained on the CIFAR-10 object recognition dataset, we modeled neurogenesis as a replacement/turnover mechanism, where weights for a randomly chosen small subset of hidden layer neurons were reinitialized to new values as the model learned to categorize 10 different classes of objects. We found that neurogenesis enhanced generalization on unseen test data compared to networks with no neurogenesis. Moreover, neurogenic networks either outperformed or performed similarly to networks with conventional noise injection (i.e., dropout, weight decay, and neural noise). These results suggest that neurogenesis can enhance generalization in hippocampal learning through noise injection, expanding on the roles that neurogenesis may have in cognition.

The connection patterns of neural circuits in the brain form a complex network. Collective signalling within the network manifests as patterned neural activity and is thought to support human cognition and adaptive behaviour. Recent technological advances permit macroscale reconstructions of biological brain networks. These maps, termed connectomes, display multiple non-random architectural features, including heavy-tailed degree distributions, segregated communities and a densely interconnected core. Yet, how computation and functional specialization emerge from network architecture remains unknown. Here we reconstruct human brain connectomes using in vivo diffusion-weighted imaging and use reservoir computing to implement connectomes as artificial neural networks. We then train these neuromorphic networks to learn a memory-encoding task. We show that biologically realistic neural architectures perform best when they display critical dynamics. We find that performance is driven by network topology and that the modular organization of intrinsic networks is computationally relevant. We observe a prominent interaction between network structure and dynamics throughout, such that the same underlying architecture can support a wide range of memory capacity values as well as different functions (encoding or decoding), depending on the dynamical regime the network is in. This work opens new opportunities to discover how the network organization of the brain optimizes cognitive capacity. The relationship between brain organization, connectivity and computation is not well understood. The authors construct neuromorphic artificial neural networks endowed with biological connection patterns derived from diffusion-weighted imaging. The neuromorphic networks are trained to perform a memory task, revealing an interaction between network structure and dynamics.

New neurons are continuously generated in the subgranular zone of the dentate gyrus throughout adulthood. These new neurons gradually integrate into hippocampal circuits, forming new naïve synapses. Viewed from this perspective, these new neurons may represent a significant source of ‘wiring’ noise in hippocampal networks. In machine learning, such noise injection is commonly used as a regularization technique. Regularization techniques help prevent overfitting training data, and allow models to generalize learning to new, unseen data. Using a computational modeling approach, here we ask whether a neurogenesis-like process similarly acts as a regularizer, facilitating generalization in a category learning task. In a convolutional neural network (CNN) trained on the CIFAR-10 object recognition dataset, we modeled neurogenesis as a replacement/turnover mechanism, where weights for a randomly chosen small subset of neurons in a chosen hidden layer were re-initialized to new values as the model learned to categorize 10 different classes of objects. We found that neurogenesis enhanced generalization on unseen test data compared to networks with no neurogenesis. Moreover, neurogenic networks either outperformed or performed similarly to networks with conventional noise injection (i.e., dropout, weight decay, and neural noise). These results suggest that neurogenesis can enhance generalization in hippocampal learning through noise-injection, expanding on the roles that neurogenesis may have in cognition.Author SummaryIn deep neural networks, various forms of noise injection are used as regularization techniques to prevent overfitting and promote generalization on unseen test data. Here, we were interested in whether adult neurogenesis– the lifelong production of new neurons in the hippocampus– might similarly function as a regularizer in the brain. We explored this question computationally, assessing whether implementing a neurogenesis-like process in a hidden layer within a convolutional neural network trained in a category learning task would prevent overfitting and promote generalization. We found that neurogenesis regularization was as least as effective as, or more effective than, conventional regularizers (i.e., dropout, weight decay and neural noise) in improving model performance. These results suggest that optimal levels of hippocampal neurogenesis may improve memory-guided decision making by preventing overfitting, thereby promoting the formation of more generalized memories that can be applied in a broader range of circumstances. We outline how these predictions may be evaluated behaviorally in rodents with altered hippocampal neurogenesis.

It is commonly assumed that usage of the word “computer” in the brain sciences reflects a metaphor. However, there is no single definition of the word “computer” in use. In fact, based on the usage of the word “computer” in computer science, a computer is merely some physical machinery that can in theory compute any computable function. According to this definition the brain is literally a computer; there is no metaphor. But, this deviates from how the word “computer” is used in other academic disciplines. According to the definition used outside of computer science, “computers” are human-made devices that engage in sequential processing of inputs to produce outputs. According to this definition, brains are not computers, and arguably, computers serve as a weak metaphor for brains. Thus, we argue that the recurring brain-computer metaphor debate is actually just a semantic disagreement, because brains are either literally computers or clearly not very much like computers at all, depending on one's definitions. We propose that the best path forward is simply to put the debate to rest, and instead, have researchers be clear about which definition they are using in their work. In some circumstances, one can use the definition from computer science and simply ask, what type of computer is the brain? In other circumstances, it is important to use the other definition, and to clarify the ways in which our brains are radically different from the laptops, smartphones, and servers that surround us in modern life.

The representation of “what happened when” is central to encoding episodic and working memories. Recently discovered hippocampal time cells are theorized to provide the neural substrate for such representations by forming distinct sequences that both encode time elapsed and sensory content. However, little work has directly addressed to what extent cognitive demands and temporal structure of experimental tasks affect the emergence and informativeness of these temporal representations. Here, we trained deep reinforcement learning (DRL) agents on a simulated trial-unique nonmatch-to-location (TUNL) task, and analyzed the activities of artificial recurrent units using neuroscience-based methods. We show that, after training, representations resembling both time cells and ramping cells (whose activity increases or decreases monotonically over time) simultaneously emerged in the same population of recurrent units. Furthermore, with simulated variations of the TUNL task that controlled for (1) memory demands during the delay period and (2) the temporal structure of the episodes, we show that memory demands are necessary for the time cells to encode information about the sensory stimuli, while the temporal structure of the task only affected the encoding of “what” and “when” by time cells minimally. Our findings help to reconcile current discrepancies regarding the involvement of time cells in memory-encoding by providing a normative framework. Our modelling results also provide concrete experimental predictions for future studies.

Neurons in the dorsal visual pathway of the mammalian brain are selective for motion stimuli, with the complexity of stimulus representations increasing along the hierarchy. This progression is similar to that of the ventral visual pathway, which is well characterized by artificial neural networks (ANNs) optimized for object recognition. In contrast, there are no image-computable models of the dorsal stream with comparable explanatory power. We hypothesized that the properties of dorsal stream neurons could be explained by a simple learning objective: the need for an organism to orient itself during self-motion. To test this hypothesis, we trained a 3D ResNet to predict an agent’s self-motion parameters from visual stimuli in a simulated environment. We found that the responses in this network accounted well for the selectivity of neurons in a large database of single-neuron recordings from the dorsal visual stream of non-human primates. In contrast, ANNs trained on an action recognition dataset through supervised or self-supervised learning could not explain responses in the dorsal stream, despite also being trained on naturalistic videos with moving objects. These results demonstrate that an ecologically relevant cost function can account for dorsal stream properties in the primate brain.

The visual system of mammals is comprised of parallel, hierarchical specialized pathways. Different pathways are specialized in so far as they use representations that are more suitable for supporting specific downstream behaviours. In particular, the clearest example is the specialization of the ventral (“what”) and dorsal (“where”) pathways of the visual cortex. These two pathways support behaviours related to visual recognition and movement, respectively. To-date, deep neural networks have mostly been used as models of the ventral, recognition pathway. However, it is unknown whether both pathways can be modelled with a single deep ANN. Here, we ask whether a single model with a single loss function can capture the properties of both the ventral and the dorsal pathways. We explore this question using data from mice, who like other mammals, have specialized pathways that appear to support recognition and movement behaviours. We show that when we train a deep neural network architecture with two parallel pathways using a self-supervised predictive loss function, we can outperform other models in fitting mouse visual cortex. Moreover, we can model both the dorsal and ventral pathways. These results demonstrate that a self-supervised predictive learning approach applied to parallel pathway architectures can account for some of the functional specialization seen in mammalian visual systems.

Simulated datasets of neural recordings are a crucial tool in neural engineering for testing the ability of decoding algorithms to recover known ground-truth. In this work, we introduce PNS-GAN, a generative adversarial network capable of producing realistic nerve recordings conditioned on physiological biomarkers. PNS-GAN operates in the wavelet domain to preserve both the timing and frequency of neural events with high resolution. PNS-GAN generates sequences of scaleograms from noise using a recurrent neural network and 2D transposed convolution layers. PNS-GAN discriminates over stacks of scaleograms with a network of 3D convolution layers. We find that our generated signal reproduces a number of characteristics of the real signal, including similarity in a canonical time-series feature-space, and contains physiologically related neural events including respiration modulation and similar distributions of afferent and efferent signalling.

Scientists have long conjectured that the neocortex learns the structure of the environment in a predictive, hierarchical manner. According to this conjecture, expected, predictable features are differentiated from unexpected ones by comparing bottom-up and top-down streams of information. It is theorized that the neocortex then changes the representation of incoming stimuli, guided by differences in the responses to expected and unexpected events. In line with this conjecture, different responses to expected and unexpected sensory features have been observed in spiking and somatic calcium events. However, it remains unknown whether these unexpected event signals occur in the distal apical dendrites where many top-down signals are received, and whether these signals govern subsequent changes in the brain’s stimulus representations. Here, we show that both somata and distal apical dendrites of cortical pyramidal neurons exhibit distinct unexpected event signals that systematically change over days. These findings were obtained by tracking the responses of individual somata and dendritic branches of layer 2/3 and layer 5 pyramidal neurons over multiple days in primary visual cortex of awake, behaving mice using two-photon calcium imaging. Many neurons in both layers 2/3 and 5 showed large differences between their responses to expected and unexpected events. Interestingly, these responses evolved in opposite directions in the somata and distal apical dendrites. These differences between the somata and distal apical dendrites may be important for hierarchical computation, given that these two compartments tend to receive bottom-up and top-down information, respectively.

The connection patterns of neural circuits in the brain form a complex network. Collective signaling within the network manifests as patterned neural activity, and is thought to support human cognition and adaptive behavior. Recent technological advances permit macro-scale reconstructions of biological brain networks. These maps, termed connectomes, display multiple non-random architectural features, including heavy-tailed degree distributions, segregated communities and a densely interconnected core. Yet, how computation and functional specialization emerge from network architecture remains unknown. Here we reconstruct human brain connectomes usingin vivodiffusion-weighted imaging, and use reservoir computing to implement these connectomes as artificial neural networks. We then train these neuromorphic networks to learn a cognitive task. We show that biologically realistic neural architectures perform optimally when they display critical dynamics. We find that performance is driven by network topology, and that the modular organization of large-scale functional systems is computationally relevant. Throughout, we observe a prominent interaction between network structure and dynamics, such that the same underlying architecture can support a wide range of learning capacities across dynamical regimes. This work opens new opportunities to discover how the network organization of the brain optimizes cognitive capacity, conceptually bridging neuroscience and artificial intelligence.

The units in artificial neural networks (ANNs) can be thought of as abstractions of biological neurons, and ANNs are increasingly used in neuroscience research. However, there are many important differences between ANN units and real neurons. One of the most notable is the absence of Dale’s principle, which ensures that biological neurons are either exclusively excitatory or inhibitory. Dale’s principle is typically left out of ANNs because its inclusion impairs learning. This is problematic, because one of the great advantages of ANNs for neuroscience research is their ability to learn complicated, realistic tasks. Here, by taking inspiration from feedforward inhibitory interneurons in the brain we show that we can develop ANNs with separate populations of excitatory and inhibitory units that learn just as well as standard ANNs. We call these networks Dale’s ANNs (DANNs). We present two insights that enable DANNs to learn well: (1) DANNs are related to normalization schemes, and can be initialized such that the inhibition centres and standardizes the excitatory activity, (2) updates to inhibitory neuron parameters should be scaled using corrections based on the Fisher Information matrix. These results demonstrate how ANNs that respect Dale’s principle can be built without sacrificing learning performance, which is important for future work using ANNs as models of the brain. The results may also have interesting implications for how inhibitory plasticity in the real brain operates.

The predominant focus in the neurobiological study of memory has been on remembering (persistence). However, recent studies have considered the neurobiology of forgetting (transience). Here we draw parallels between neurobiological and computational mechanisms underlying transience. We propose that it is the interaction between persistence and transience that allows for intelligent decision-making in dynamic, noisy environments. Specifically, we argue that transience (1) enhances flexibility, by reducing the influence of outdated information on memory-guided decision-making, and (2) prevents overfitting to specific past events, thereby promoting generalization. According to this view, the goal of memory is not the transmission of information through time, per se. Rather, the goal of memory is to optimize decision-making. As such, transience is as important as persistence in mnemonic systems.

Anxiety is an adaptive response to potentially threatening situations. Exaggerated and uncontrolled anxiety responses become maladaptive and lead to anxiety disorders. Anxiety is shaped by a network of forebrain structures, including the hippocampus, septum, and prefrontal cortex. In particular, neural inputs arising from the ventral hippocampus (vHPC) to the lateral septum (LS) and medial prefrontal cortex (mPFC) are thought to serve as principal components of the anxiety circuit. However, the role of vHPC-to-LS and vHPC-to-mPFC signals in anxiety is unclear, as no study has directly compared their behavioral contribution at circuit level. We targeted LS-projecting vHPC cells and mPFC-projecting vHPC cells by injecting the retrogradely propagating canine adenovirus encoding Cre recombinase into the LS or mPFC, and injecting a Cre-responsive AAV (AAV8-hSyn-FLEX-hM3D or hM4D) into the vHPC. Consequences of manipulating these neurons were examined in well-established tests of anxiety. Chemogenetic manipulation of LS-projecting vHPC cells led to bidirectional changes in anxiety: activation of LS-projecting vHPC cells decreased anxiety whereas inhibition of these cells produced opposite anxiety-promoting effects. The observed anxiety-reducing function of LS-projecting cells was in contrast with the function of mPFC-projecting cells, which promoted anxiety. In addition, double retrograde tracing demonstrated that LS- and mPFC-projecting cells represent two largely anatomically distinct cell groups. Altogether, our findings suggest that the vHPC houses discrete populations of cells that either promote or suppress anxiety through differences in their projection targets. Disruption of the intricate balance in the activity of these two neuron populations may drive inappropriate behavioral responses seen in anxiety disorders.

Synchronization of precise spike-times across multiple neurons carries information about sensory stimuli. Inhibitory interneurons are suggested to promote this synchronization, but it is unclear whether distinct interneuron subtypes provide different contributions. To test this, we examined single-unit recordings from barrel cortex in vivo and used optogenetics to determine the contribution of two classes of inhibitory interneurons: parvalbumin (PV)- and somatostatin (SST)-positive interneurons to spike-timing synchronization across cortical layers. We found that PV interneurons preferentially promote the synchronization of spike-times when instantaneous firing-rates are low (12 Hz). Furthermore, using a computational model, we demonstrate that these effects can be explained by PV and SST interneurons having preferential contribution to feedforward and feedback inhibition, respectively. Our findings demonstrate that distinct subtypes of inhibitory interneurons have frequency-selective roles in spatio-temporal synchronization of precise spike-times.

Forgetting involves the loss of information over time; however, we know little about what form this information loss takes. Do memories become less precise over time, or do they instead become less accessible? Here we assessed memory for word-location associations across four days, testing whether forgetting involves losses in precision versus accessibility and whether such losses are modulated by learning a generalizable pattern. We show that forgetting involves losses in memory accessibility with no changes in memory precision. When participants learned a set of related word-location associations that conformed to a general pattern, we saw a strong trade-off; accessibility was enhanced, whereas precision was reduced. However, this trade-off did not appear to be modulated by time or confer a long-term increase in the total amount of information maintained in memory. Our results place theoretical constraints on how models of forgetting and generalization account for time-dependent memory processes. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 4 June 2019. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.c.4368464.v1 .

Guaranteeing that synaptic plasticity leads to effective learning requires a means for assigning credit to each neuron for its contribution to behavior. The 'credit assignment problem' refers to the fact that credit assignment is non-trivial in hierarchical networks with multiple stages of processing. One difficulty is that if credit signals are integrated with other inputs, then it is hard for synaptic plasticity rules to distinguish credit-related activity from non-credit-related activity. A potential solution is to use the spatial layout and non-linear properties of dendrites to distinguish credit signals from other inputs. In cortical pyramidal neurons, evidence hints that top-down feedback signals are integrated in the distal apical dendrites and have a distinct impact on spike-firing and synaptic plasticity. This suggests that the distal apical dendrites of pyramidal neurons help the brain to solve the credit assignment problem.

Classic theories of reinforcement learning and neuromodulation rely on reward prediction errors. A new machine learning technique relies on neuromodulatory signals that are optimized for specific tasks, which may lead to better AI and better explanations of neuroscience data.

SummaryThe ability to generate action potentials (spikes) in response to synaptic input determines whether a neuron participates in information processing. How a developing neuron becomes an active participant in a circuit or whether this process is activity dependent is not known, especially as spike-dependent plasticity mechanisms would not be available to non-spiking neurons. Here we use the optic tectum of awake Xenopus laevis tadpoles to determine how a neuron becomes able to generate sensory-driven spikes in vivo. At the onset of vision, many tectal neurons do not exhibit visual spiking behavior, despite being intrinsically excitable and receiving visuotopically organized synaptic inputs. However, a brief period of visual stimulation can drive these neurons to start generating stimulus-driven spikes. This conversion relies upon a selective increase in glutamatergic input and requires depolarizing GABAergic transmission and NMDA receptor activation. This permissive form of experience-dependent plasticity enables a neuron to start contributing to circuit function.

Following learning, increased coupling between spindle oscillations in the medial prefrontal cortex (mPFC) and ripple oscillations in the hippocampus is thought to underlie memory consolidation. However, whether learning-induced increases in ripple-spindle coupling are necessary for successful memory consolidation has not been tested directly. In order to decouple ripple-spindle oscillations, here we chemogenetically inhibited parvalbumin-positive (PV+) interneurons, since their activity is important for regulating the timing of spiking activity during oscillations. We found that contextual fear conditioning increased ripple-spindle coupling in mice. However, inhibition of PV+ cells in either CA1 or mPFC eliminated this learning-induced increase in ripple-spindle coupling without affecting ripple or spindle incidence. Consistent with the hypothesized importance of ripple-spindle coupling in memory consolidation, post-training inhibition of PV+ cells disrupted contextual fear memory consolidation. These results indicate that successful memory consolidation requires coherent hippocampal-neocortical communication mediated by PV+ cells., eLife digest Sleep contributes to the strengthening of memories. During non-dreaming sleep, neurons in regions of the brain that form and store memories – such as the hippocampus and prefrontal cortex – fire in rhythmic waves. The neurons in the hippocampus tend to fire during a wave that repeats up to 250 times per second, called sharp-wave ripples. Meanwhile, in the prefrontal cortex, the neurons tend to fire during a lower frequency wave that repeats 12 to 15 times per second, called spindles. During sleep and quiet wakefulness, hippocampal ripples often synchronize with prefrontal spindles; that is, both waves tend to occur at approximately the same time. Many neuroscientists think this allows the brain regions to better communicate with one another, which in turn should help the brain to strengthen memories. Consistent with this possibility, rodents that learn a new task show more synchrony between ripples and spindles afterwards. But no one had actually tested whether this increase in ripple-spindle synchrony does strengthen the rodent’s memory of the task. It was also unclear how the brain achieves such an increase. Xia et al. suspected that this process involved a group of inhibitory brain cells called parvalbumin-positive interneurons. These cells act like timekeepers, and help to synchronize the firing of groups of neurons. Xia et al. now show that training mice to associate an environment with a mild electric shock made it more likely that the animals would show ripple-spindle synchrony. Yet, inhibiting the activity of parvalbumin-positive interneurons in either the hippocampus or prefrontal cortex blocked this effect. It also prevented sleep from strengthening the animals’ memory of the link between the environment and the shock. Patients with Alzheimer’s disease have fewer parvalbumin-positive interneurons. By showing that these neurons help strengthen new memories, these findings may explain why losing them can impair memory. Restoring or replacing interneuron activity could be a promising therapeutic avenue to explore.

Symptoms of schizophrenia may arise from a failure of cortical circuits to filter-out irrelevant inputs. Schizophrenia has also been linked to disruptions in cortical inhibitory interneurons, consistent with the possibility that in the normally functioning brain, these cells are in some part responsible for determining which sensory inputs are relevant versus irrelevant. Here, we develop a neural network model that demonstrates how the cortex may learn to ignore irrelevant inputs through plasticity processes affecting inhibition. The model is based on the proposal that the amount of excitatory output from a cortical circuit encodes the expected magnitude of reward or punishment (“relevance”), which can be trained using a temporal difference learning mechanism acting on feedforward inputs to inhibitory interneurons. In the model, irrelevant and blocked stimuli drive lower levels of excitatory activity compared with novel and relevant stimuli, and this difference in activity levels is lost following disruptions to inhibitory units. When excitatory units are connected to a competitive-learning output layer with a threshold, the relevance code can be shown to “gate” both learning and behavioral responses to irrelevant stimuli. Accordingly, the combined network is capable of recapitulating published experimental data linking inhibition in frontal cortex with fear learning and expression. Finally, the model demonstrates how relevance learning can take place in parallel with other types of learning, through plasticity rules involving inhibitory and excitatory components, respectively. Altogether, this work offers a theory of how the cortex learns to selectively inhibit inputs, providing insight into how relevance-assignment problems may emerge in schizophrenia., Author summary Individuals with schizophrenia have difficulty ignoring ideas and experiences that most people would treat as unimportant. There is evidence that this may be due to changes in neuronal inhibition, suggesting that inhibitory neurons may be involved in learning to ignore irrelevant inputs. By developing a computational model that learns relevance and irrelevance through changes in the strength of feedforward inhibition, we are able to simulate many specific effects of inhibitory neuron dysfunction on behavior. We also show two computational advantages to this mechanism: (1) if relevance is signaled by the level of excitatory activity, then downstream circuits can easily avoid learning from irrelevant stimuli, (2) relevance learning can occur simultaneously with other types of learning. The model therefore offers insight into the relationships between neural inhibition and behavior, including symptoms of schizophrenia.

Symptoms of schizophrenia may arise from a failure of cortical circuits to filter-out irrelevant inputs. Schizophrenia has also been linked to disruptions to cortical inhibitory interneurons, consistent with the possibility that in the normally functioning brain, these cells are in some part responsible for determining which inputs are relevant and which irrelevant. Here, we develop an abstract but biologically plausible neural network model that demonstrates how the cortex may learn to ignore irrelevant inputs through plasticity processes affecting inhibition. The model is based on the proposal that the amount of excitatory output from a cortical circuit encodes expected magnitude of reward or punishment (”relevance”), which can be trained using a temporal difference learning mechanism acting on feed-forward inputs to inhibitory interneurons. The model exhibits learned irrelevance and blocking, which become impaired following disruptions to inhibitory units. When excitatory units are connected to a competitive-learning output layer, the relevance code is capable of modulating learning and activity. Accordingly, the combined network is capable of recapitulating published experimental data linking inhibition in frontal cortex with fear learning and expression. Finally, the model demonstrates how relevance learning can take place in parallel with other types of learning, through plasticity rules involving inhibitory and excitatory components respectively. Altogether, this work offers a theory of how the cortex learns to selectively inhibit inputs, providing insight into how relevance-assignment problems may emerge in schizophrenia.

Memories are not static but continue to be processed after encoding. This is thought to allow the integration of related episodes via the identification of patterns. Although this idea lies at the heart of contemporary theories of systems consolidation, it has yet to be demonstrated experimentally. Using a modified water-maze paradigm in which platforms are drawn stochastically from a spatial distribution, we found that mice were better at matching platform distributions 30 d compared to 1 d after training. Post-training time-dependent improvements in pattern matching were associated with increased sensitivity to new platforms that conflicted with the pattern. Increased sensitivity to pattern conflict was reduced by pharmacogenetic inhibition of the medial prefrontal cortex (mPFC). These results indicate that pattern identification occurs over time, which can lead to conflicts between new information and existing knowledge that must be resolved, in part, by computations carried out in the mPFC.

Memories serve to establish some permanence to our inner lives despite the fleeting nature of subjective experience. Most neurobiological theories of memory assume that this mental permanence reflects an underlying cellular permanence. Namely, it is assumed that the cellular changes which first occur to store a memory are perpetuated for as long as the memory is stored. But is that really the case? In an opinion piece in this issue of Hippocampus, Aryeh Routtenberg raises the provocative idea that the subjective sense of memory persistence is not in fact a result of persistence at the cellular level, rather, that “supple synapses” and multiple “evanescent networks” that are forever changing are responsible for our memories. On one level, his proposal could be construed as a radical challenge to some of our most fundamental theories of the neurobiology of memory, including Donald Hebb's proposal that memories are stored by networks that strengthen their connections to increase the likelihood of the same activity patterns being recreated at a later date. However, it could also be seen as a moderating call, a call for a greater acknowledgement of the dynamic, stochastic, and distributed nature of neural networks. In this response to Routtenberg's article, we attempt to provide a clarification of the dividing line between these two interpretations of his argument, and in doing so, we provide some overview of the empirical evidence that bears on this subject. We argue that the data that exists to date favors the more moderate interpretation: that memory storage involves a process in which activity patterns are made more likely to reoccur, but that an under-appreciated reality is that mnemonic traces may continue to change and evolve over time. © 2013 Wiley Periodicals, Inc.

Synaptic inhibition is a critical regulator of neuronal excitability, and in the mature brain the majority of synaptic inhibition is mediated by Cl−-permeable GABAA receptors. Unlike other physiologically relevant ions, Cl− is dynamically regulated, and alterations in the Cl− gradient can have significant impact on neuronal excitability. Due to changes in the neuronal Cl− concentration, GABAergic transmission can bidirectionally regulate the induction of excitatory synaptic plasticity and gate the closing of the critical period for monocular deprivation in visual cortex. GABAergic circuitry can also provide a powerful restraining mechanism for the spread of excitation, however Cl− extrusion mechanisms can become overwhelmed and GABA can paradoxically contribute to pathological excitation such as the propagation of seizure activity.

Over the course of systems consolidation, there is a switch from a reliance on detailed episodic memories to generalized schematic memories. This switch is sometimes referred to as “memory transformation.” Here we demonstrate a previously unappreciated benefit of memory transformation, namely, its ability to enhance reinforcement learning in a dynamic environment. We developed a neural network that is trained to find rewards in a foraging task where reward locations are continuously changing. The network can use memories for specific locations (episodic memories) and statistical patterns of locations (schematic memories) to guide its search. We find that switching from an episodic to a schematic strategy over time leads to enhanced performance due to the tendency for the reward location to be highly correlated with itself in the short-term, but regress to a stable distribution in the long-term. We also show that the statistics of the environment determine the optimal utilization of both types of memory. Our work recasts the theoretical question of why memory transformation occurs, shifting the focus from the avoidance of memory interference toward the enhancement of reinforcement learning across multiple timescales.SIGNIFICANCE STATEMENTAs time passes, memories transform from a highly detailed state to a more gist-like state, in a process called “memory transformation.” Theories of memory transformation speak to its advantages in terms of reducing memory interference, increasing memory robustness, and building models of the environment. However, the role of memory transformation from the perspective of an agent that continuously acts and receives reward in its environment is not well explored. In this work, we demonstrate a view of memory transformation that defines it as a way of optimizing behavior across multiple timescales.

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the brain optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, the neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network can learn to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful representations---the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the dendritic morphology of neocortical pyramidal neurons., Comment: 41 pages, 11 figures

Memory stabilization following encoding (synaptic consolidation) or memory reactivation (reconsolidation) requires gene expression and protein synthesis (Dudai and Eisenberg, 2004; Tronson and Taylor, 2007; Nader and Einarsson, 2010; Alberini, 2011). Although consolidation and reconsolidation may be mediated by distinct molecular mechanisms (Lee et al., 2004), disrupting the function of the transcription factor CREB impairs both processes (Kida et al., 2002; Mamiya et al., 2009). Phosphorylation of CREB at Ser133 recruits CREB binding protein (CBP)/p300 coactivators to activate transcription (Chrivia et al., 1993; Parker et al., 1996). In addition to this well known mechanism, CREB regulated transcription coactivators (CRTCs), previously called transducers of regulated CREB (TORC) activity, stimulate CREB-mediated transcription, even in the absence of CREB phosphorylation. Recently, CRTC1 has been shown to undergo activity-dependent trafficking from synapses and dendrites to the nucleus in excitatory hippocampal neurons (Ch'ng et al., 2012). Despite being a powerful and specific coactivator of CREB, the role of CRTC in memory is virtually unexplored. To examine the effects of increasing CRTC levels, we used viral vectors to locally and acutely increase CRTC1 in the dorsal hippocampus dentate gyrus region of mice before training or memory reactivation in context fear conditioning. Overexpressing CRTC1 enhanced both memory consolidation and reconsolidation; CRTC1-mediated memory facilitation was context specific (did not generalize to nontrained context) and long lasting (observed after virally expressed CRTC1 dissipated). CREB overexpression produced strikingly similar effects. Therefore, increasing CRTC1 or CREB function is sufficient to enhance the strength of new, as well as established reactivated, memories without compromising memory quality.

The retinotectal pathway of Xenopus laevis is a well-established experimental model for studying activity-dependent processes during visual system development. Such processes can be guided by stimulus-evoked activity patterns, which depend on the refractive characteristics of the eye. Previous work has shown that many animals are hyperopic at early developmental stages due to immature refractive properties. Whether this is also the case for Xenopus laevis is unknown. Here, we measure the focal length of the lens and the size of the eye of embryos at different stages and find that Xenopus laevis exhibits a similar shift from hyperopia to emmetropia. At early stages, immediately after innervation of the tectum by the optic nerve, Xenopus embryos are hyperopic. Soon afterwards the focal length of the lens decreases and the eye converges to a state of emmetropia. Despite being hyperopic we find that some visuospatial information is available to the young circuit. Calculations based on the optical properties of the eye show that even when the animals are hyperopic the blurred retinal image provides a crude spatial resolution. Furthermore, using whole-cell recordings in the optic tectum combined with visual stimulation through the intact eye, we show that tectal neurons in hyperopic embryos have spatially restricted glutamatergic receptive fields. Our data demonstrate that Xenopus laevis eyes undergo a process of developmental emmetropization, and suggest that despite an initial stage of suboptimal image formation there is potentially enough information to guide activity-dependent refinements of the retinotectal pathway from the onset of vision. © 2011 Wiley Periodicals, Inc. Develop Neurobiol 72: 507–519, 2012

We argue that an explanation of relevance realization is a pervasive problem within cognitive science, and that it is becoming the criterion of the cognitive in terms of which a new framework for doing cognitive science is emerging. We articulate that framework and then make use of it to provide the beginnings of a theory of relevance realization that incorporates many existing insights implicit within the contributing disciplines of cognitive science. We also introduce some theoretical and potentially technical innovations motivated by the articulation of those insights. Finally, we show how the explication of the framework and development of the theory help to clear up some important incompleteness and confusions within both Montague's work and Sperber and Wilson's theory of relevance.

In vivo measurement of cortical thickness is a sensitive representation of pathology in neurodegenerative disorders which primarily target the gray mantle. In this study we used magnetic resonance images to describe the patterns of cortical thinning in 11 frontotemporal dementia (FTD), 38 Alzheimer's disease (AD) and 34 healthy elderly (H E ) subjects. AD and FTD displayed significant thinning of the cortical mantle compared to the H E group, but with distinctive distributions. AD subjects had significantly thinner cortex in all lobes whereas FTD compared to H E showed significant differences only in specific regions of frontal and temporal lobes. When compared to AD, the FTD subjects had a trend of thinner cortex in the anterior cingulate region and in selective regions of anterior frontal and temporal regions. In conclusion, the cortical thinning in dementia when compared to H E , is disease specific whereby FTD subjects display a pattern distinct than that seen in Alzheimer's disease.

The present investigation sought to identify which brain regions distinguish pedophilic from nonpedophilic men, using unbiased, automated analyses of the whole brain. T1-weighted magnetic resonance images (MRIs) were acquired from men who demonstrated illegal or clinically significant sexual behaviors or interests (n = 65) and from men who had histories of nonsexual offenses but no sexual offenses (n = 62). Sexual interest in children was assessed by participants' admissions of pedophilic interest, histories of committing sexual offenses against children, and psychophysiological responses in the laboratory to erotic stimuli depicting children or adults. Automated parcellation of the MRIs revealed significant negative associations between pedophilia and white matter volumes of the temporal and parietal lobes bilaterally. Voxel-based morphometry corroborated the associations and indicated that the regions of lower white matter volumes followed, and were limited to, two major fiber bundles: the superior fronto-occipital fasciculus and the right arcuate fasciculus. No significant differences were found in grey matter or in cerebrospinal fluid (CSF). Because the superior fronto-occipital and arcuate fasciculi connect the cortical regions that respond to sexual cues, these results suggest (1) that those cortical regions operate as a network for recognizing sexually relevant stimuli and (2) that pedophilia results from a partial disconnection within that network. Language: en

The objectives of this research were to further delineate the neural circuits subserving proposed memory-based behavioural subsystems in the hippocampal formation. These studies were guided by anatomical evidence showing a topographical organization of the hippocampal formation. Briefly, perpendicular to the medial/lateral entorhinal cortex division there is a second system of parallel circuits that separates the dorsal and ventral hippocampus. Recent work from this laboratory has provided evidence that the hippocampus incidentally encodes a context-specific inhibitory association during acquisition of a visual discrimination task. One question that emerges from this dataset is whether the dorsal or ventral hippocampus makes a unique contribution to this newly described function. Rats with neurotoxic lesions of the dorsal or ventral hippocampus were assessed on the acquisition of the visual discrimination task. Following asymptotic performance they were given reversal training in either the same or a different context from the original training. The results showed that the context-specific inhibition effect is mediated by a circuit that includes the ventral but not the dorsal hippocampus. Results from a control procedure showed that rats with either dorso-lateral striatum damage or dorsal hippocampal lesions were impaired on a tactile/spatial discrimination. Taken together, the results represent a double dissociation of learning and memory function between the ventral and dorsal hippocampus. The formation of an incidental inhibitory association was dependent on ventral but not dorsal hippocampal circuitry, and the opposite dependence was found for the spatial component of a tactile/spatial discrimination.

SummaryMemories are thought to be sparsely encoded in neuronal networks, but little is known about why a given neuron is recruited or allocated to a particular memory trace. Previous research shows that in the lateral amygdala (LA), neurons with increased CREB are selectively recruited to a fear memory trace. CREB is a ubiquitous transcription factor implicated in many cellular processes. Which process mediates neuronal memory allocation? One hypothesis is that CREB increases neuronal excitability to bias neuronal recruitment, although this has not been shown experimentally. Here we use several methods to increase neuronal excitability and show this both biases recruitment into the memory trace and enhances memory formation. Moreover, artificial activation of these neurons alone is a sufficient retrieval cue for fear memory expression, showing that these neurons are critical components of the memory trace. These results indicate that neuronal memory allocation is based on relative neuronal excitability immediately before training.Video Abstract

Forget It! When examining the relationship between the production of new neurons in the hippocampus and memory, studies have generally first manipulated hippocampal neurogenesis and afterward investigated memory formation and found that new neurons help to encode new memories. However, when investigating how similar manipulations of neurogenesis impact established hippocampus-dependent memories, Akers et al. (p. 598 ; see the Perspective by Mongiat and Schinder ) uncovered a role for neurogenesis in memory clearance. Thus, the continuous addition of new neurons both degrades existing information stored in hippocampal circuits and simultaneously provides substrates for new learning.

Spike-timing-dependent plasticity (STDP) is found in vivo in a variety of systems and species, but the first demonstrations of in vivo STDP were carried out in the optic tectum of Xenopus laevis embryos. Since then, the optic tectum has served as an excellent experimental model for studying STDP in sensory systems, allowing researchers to probe the developmental consequences of this form of synaptic plasticity during early development. In this review, we will describe what is known about the role of STDP in shaping feed-forward and recurrent circuits in the optic tectum with a focus on the functional implications for vision. We will discuss both the similarities and differences between the optic tectum and mammalian sensory systems that are relevant to STDP. Finally, we will highlight the unique properties of the embryonic tectum that make it an important system for researchers who are interested in how STDP contributes to activity-dependent development of sensory computations. © 2010 Richards, Aizenman and Akerman.

OBJECTIVE. Abnormalities in cognitive function and brain structure have been reported in acutely ill adolescents with anorexia nervosa, but whether these abnormalities persist or are reversible in the context of weight restoration remains unclear. Brain structure and cognitive function in female subjects with adolescent-onset anorexia nervosa assessed at long-term follow-up were studied in comparison with healthy female subjects, and associations with clinical outcome were investigated.PATIENTS AND METHODS. Sixty-six female subjects (aged 21.3 ± 2.3 years) who had a diagnosis of adolescent-onset anorexia nervosa and treated 6.5 ± 1.7 years earlier in a tertiary care hospital and 42 healthy female control subjects (aged 20.7 ± 2.5 years) were assessed. All participants underwent a clinical examination, magnetic resonance brain scan, and cognitive evaluation. Clinical data were analyzed first as a function of weight recovery (n = 14, RESULTS. Compared with control subjects, participants with anorexia nervosa who remained at low weight had larger lateral ventricles. Twenty-four–hour urinary free-cortisol levels were positively correlated with volumes of the temporal horns of the lateral ventricles and negatively correlated with volumes of the hippocampi in clinical participants. Participants who were amenorrheic or had irregular menses showed significant cognitive deficits across a broad range of many domains.CONCLUSIONS. Female subjects with adolescent-onset anorexia nervosa showed abnormal cognitive function and brain structure compared with healthy individuals despite an extended period since diagnosis. To our knowledge, this is the first study to report a specific relationship between menstrual function and cognitive function in this patient population. Possible mechanisms underlying neural and cognitive deficits with anorexia nervosa are discussed. Additional examination of the effects of estrogen on cognitive function in female subjects with anorexia nervosa is necessary.