Stimulus information guides the emergence of behavior-related signals in primary somatosensory cortex during learning.

Neurons in the primary cortex carry sensory- and behavior-related information, but it remains an open question how this information emerges and intersects together during learning. Current evidence points to two possible learning-related changes: sensory information increases in the primary cortex or sensory information remains stable, but its readout efficiency in association cortices increases. We investigated this question by imaging neuronal activity in mouse primary somatosensory cortex before, during, and after learning of an object localization task. We quantified sensory- and behavior-related information and estimated how much sensory information was used to instruct perceptual choices as learning progressed. We find that sensory information increases from the start of training, while choice information is mostly present in the later stages of learning. Additionally, the readout of sensory information becomes more efficient with learning as early as in the primary sensory cortex. Together, our results highlight the importance of primary cortical neurons in perceptual learning.

Excitability mediates allocation of pre-configured ensembles to a hippocampal engram supporting contextual conditioned threat in mice.

Little is understood about how engrams, sparse groups of neurons that store memories, are formed endogenously. Here, we combined calcium imaging, activity tagging, and optogenetics to examine the role of neuronal excitability and pre-existing functional connectivity on the allocation of mouse cornu ammonis area 1 (CA1) hippocampal neurons to an engram ensemble supporting a contextual threat memory. Engram neurons (high activity during recall or TRAP2-tagged during training) were more active than non-engram neurons 3 h (but not 24 h to 5 days) before training. Consistent with this, optogenetically inhibiting scFLARE2-tagged neurons active in homecage 3 h, but not 24 h, before conditioning disrupted memory retrieval, indicating that neurons with higher pre-training excitability were allocated to the engram. We also observed stable pre-configured functionally connected sub-ensembles of neurons whose activity cycled over days. Sub-ensembles that were more active before training were allocated to the engram, and their functional connectivity increased at training. Therefore, both neuronal excitability and pre-configured functional connectivity mediate allocation to an engram ensemble.

Engineering protein nanoparticles for drug delivery.

Protein nanoparticles offer a highly tunable platform for engineering multifunctional drug delivery vehicles that can improve drug efficacy and reduce off-target effects. While many protein nanoparticles have demonstrated the ability to tolerate genetic and posttranslational modifications for drug delivery applications, this review will focus on three protein nanoparticles of increasing size. Each protein nanoparticle possesses distinct properties such as highly tunable stability, capacity for splitting or fusing subunits for modular surface decoration, and well-characterized conformational changes with impressive capacity for large protein cargos. While many of the genetic and posttranslational modifications leverage these protein nanoparticle's properties, the shared techniques highlight engineering approaches that have been generalized across many protein nanoparticle platforms.

Responses to Pattern-Violating Visual Stimuli Evolve Differently Over Days in Somata and Distal Apical Dendrites.

Scientists have long conjectured that the neocortex learns patterns in sensory data to generate top-down predictions of upcoming stimuli. In line with this conjecture, different responses to pattern-matching vs pattern-violating visual stimuli have been observed in both spiking and somatic calcium imaging data. However, it remains unknown whether these pattern-violation signals are different between the distal apical dendrites, which are heavily targeted by top-down signals, and the somata, where bottom-up information is primarily integrated. Furthermore, it is unknown how responses to pattern-violating stimuli evolve over time as an animal gains more experience with them. Here, we address these unanswered questions by analyzing responses of individual somata and dendritic branches of layer 2/3 and layer 5 pyramidal neurons tracked over multiple days in primary visual cortex of awake, behaving female and male mice. We use sequences of Gabor patches with patterns in their orientations to create pattern-matching and pattern-violating stimuli, and two-photon calcium imaging to record neuronal responses. Many neurons in both layers show large differences between their responses to pattern-matching and pattern-violating stimuli. Interestingly, these responses evolve in opposite directions in the somata and distal apical dendrites, with somata becoming less sensitive to pattern-violating stimuli and distal apical dendrites more sensitive. These differences between the somata and distal apical dendrites may be important for hierarchical computation of sensory predictions and learning, since these two compartments tend to receive bottom-up and top-down information, respectively.

Responses of pyramidal cell somata and apical dendrites in mouse visual cortex over multiple days.

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.

Adult neurogenesis acts as a neural regularizer.

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.

Forgetting Enhances Episodic Control With Structured Memories.

Forgetting is a normal process in healthy brains, and evidence suggests that the mammalian brain forgets more than is required based on limitations of mnemonic capacity. Episodic memories, in particular, are liable to be forgotten over time. Researchers have hypothesized that it may be beneficial for decision making to forget episodic memories over time. Reinforcement learning offers a normative framework in which to test such hypotheses. Here, we show that a reinforcement learning agent that uses an episodic memory cache to find rewards in maze environments can forget a large percentage of older memories without any performance impairments, if they utilize mnemonic representations that contain structural information about space. Moreover, we show that some forgetting can actually provide a benefit in performance compared to agents with unbounded memories. Our analyses of the agents show that forgetting reduces the influence of outdated information and states which are not frequently visited on the policies produced by the episodic control system. These results support the hypothesis that some degree of forgetting can be beneficial for decision making, which can help to explain why the brain forgets more than is required by capacity limitations.

Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation.

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 using parallel, recurrent cascade 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.

Promoting and Optimizing the Use of 3D-Printed Objects in Spontaneous Recognition Memory Tasks in Rodents: A Method for Improving Rigor and Reproducibility.

Spontaneous recognition memory tasks are widely used to assess cognitive function in rodents and have become commonplace in the characterization of rodent models of neurodegenerative, neuropsychiatric and neurodevelopmental disorders. Leveraging an animal's innate preference for novelty, these tasks use object exploration to capture the what, where and when components of recognition memory. Choosing and optimizing objects is a key feature when designing recognition memory tasks. Although the range of objects used in these tasks varies extensively across studies, object features can bias exploration, influence task difficulty and alter brain circuit recruitment. Here, we discuss the advantages of using 3D-printed objects in rodent spontaneous recognition memory tasks. We provide strategies for optimizing their design and usage, and offer a repository of tested, open-source designs for use with commonly used rodent species. The easy accessibility, low-cost, renewability and flexibility of 3D-printed open-source designs make this approach an important step toward improving rigor and reproducibility in rodent spontaneous recognition memory tasks.

Neocortical inhibitory interneuron subtypes are differentially attuned to synchrony- and rate-coded information.

Neurons can carry information with both the synchrony and rate of their spikes. However, it is unknown whether distinct subtypes of neurons are more sensitive to information carried by synchrony versus rate, or vice versa. Here, we address this question using patterned optical stimulation in slices of somatosensory cortex from mouse lines labelling fast-spiking (FS) and regular-spiking (RS) interneurons. We used optical stimulation in layer 2/3 to encode a 1-bit signal using either the synchrony or rate of activity. We then examined the mutual information between this signal and the interneuron responses. We found that for a synchrony encoding, FS interneurons carried more information in the first five milliseconds, while both interneuron subtypes carried more information than excitatory neurons in later responses. For a rate encoding, we found that RS interneurons carried more information after several milliseconds. These data demonstrate that distinct interneuron subtypes in the neocortex have distinct sensitivities to synchrony versus rate codes.

The overfitted brain hypothesis.

What is the purpose of dreaming? Many scientists have postulated a role for dreaming in learning, often with the aim of improving generative models. In this issue of Patterns, Erik Hoel proposes a novel hypothesis, namely, that dreaming provides a means to reduce overfitting. This hypothesis is interesting both for neuroscience and for the development of new machine-learning systems.

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits.

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well established that it depends on pre- and postsynaptic activity. However, models that rely solely on pre- and postsynaptic activity for synaptic changes have, so far, not been able to account for learning complex tasks that demand credit assignment in hierarchical networks. Here we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then pyramidal neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits.

Dissociating memory accessibility and precision in forgetting.

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 .

Distinct roles of parvalbumin and somatostatin interneurons in gating the synchronization of spike times in the neocortex.

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 parvalbumin (PV)- and somatostatin (SST)-positive interneurons to the synchronization of spike times across cortical layers. We found that PV interneurons preferentially promote the synchronization of spike times when instantaneous firing rates are low (<12 Hz), whereas SST interneurons preferentially promote the synchronization of spike times when instantaneous firing rates are high (>12 Hz). Furthermore, using a computational model, we demonstrate that these effects can be explained by PV and SST interneurons having preferential contributions to feedforward and feedback inhibition, respectively. Our findings demonstrate that distinct subtypes of inhibitory interneurons have frequency-selective roles in the spatiotemporal synchronization of precise spike times.

Systems consolidation impairs behavioral flexibility.

Behavioral flexibility is important in a changing environment. Previous research suggests that systems consolidation, a long-term poststorage process that alters memory traces, may reduce behavioral flexibility. However, exactly how systems consolidation affects flexibility is unknown. Here, we tested how systems consolidation affects: (1) flexibility in response to value changes and (2) flexibility in response to changes in the optimal sequence of actions. Mice were trained to obtain food rewards in a Y-maze by switching nose pokes between three arms. During initial training, all arms were rewarded and mice simply had to switch arms in order to maximize rewards. Then, after either a 1 or 28 d delay, we either devalued one arm, or we reinforced a specific sequence of pokes. We found that after a 1 d delay mice adapted relatively easily to the changes. In contrast, mice given a 28 d delay struggled to adapt, especially for changes to the optimal sequence of actions. Immediate early gene imaging suggested that the 28 d mice were less reliant on their hippocampus and more reliant on their medial prefrontal cortex. These data suggest that systems consolidation reduces behavioral flexibility, particularly for changes to the optimal sequence of actions.

Optogenetic activation of parvalbumin and somatostatin interneurons selectively restores theta-nested gamma oscillations and oscillation-induced spike timing-dependent long-term potentiation impaired by amyloid ß oligomers.

BACKGROUND: Abnormal accumulation of amyloid ß1-42 oligomers (AßO1-42), a hallmark of Alzheimer's disease, impairs hippocampal theta-nested gamma oscillations and long-term potentiation (LTP) that are believed to underlie learning and memory. Parvalbumin-positive (PV) and somatostatin-positive (SST) interneurons are critically involved in theta-nested gamma oscillogenesis and LTP induction. However, how AßO1-42 affects PV and SST interneuron circuits is unclear. Through optogenetic manipulation of PV and SST interneurons and computational modeling of the hippocampal neural circuits, we dissected the contributions of PV and SST interneuron circuit dysfunctions on AßO1-42-induced impairments of hippocampal theta-nested gamma oscillations and oscillation-induced LTP. RESULTS: Targeted whole-cell patch-clamp recordings and optogenetic manipulations of PV and SST interneurons during in vivo-like, optogenetically induced theta-nested gamma oscillations in vitro revealed that AßO1-42 causes synapse-specific dysfunction in PV and SST interneurons. AßO1-42 selectively disrupted CA1 pyramidal cells (PC)-to-PV interneuron and PV-to-PC synapses to impair theta-nested gamma oscillogenesis. In contrast, while having no effect on PC-to-SST or SST-to-PC synapses, AßO1-42 selectively disrupted SST interneuron-mediated disinhibition to CA1 PC to impair theta-nested gamma oscillation-induced spike timing-dependent LTP (tLTP). Such AßO1-42-induced impairments of gamma oscillogenesis and oscillation-induced tLTP were fully restored by optogenetic activation of PV and SST interneurons, respectively, further supporting synapse-specific dysfunctions in PV and SST interneurons. Finally, computational modeling of hippocampal neural circuits including CA1 PC, PV, and SST interneurons confirmed the experimental observations and further revealed distinct functional roles of PV and SST interneurons in theta-nested gamma oscillations and tLTP induction. CONCLUSIONS: Our results reveal that AßO1-42 causes synapse-specific dysfunctions in PV and SST interneurons and that optogenetic modulations of these interneurons present potential therapeutic targets for restoring hippocampal network oscillations and synaptic plasticity impairments in Alzheimer's disease.

A deep learning framework for neuroscience.

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.

Forgetting at biologically realistic levels of neurogenesis in a large-scale hippocampal model.

Neurogenesis persists throughout life in the dentate gyrus region of the mammalian hippocampus. Computational models have established that the addition of neurons degrades existing memories (i.e., produces forgetting). These predictions are supported by empirical observations in rodents, where post-training increases in neurogenesis also promote forgetting of hippocampus-dependent memories. However, in these computational models which use 10-1,000 neurons to represent the dentate gyrus, forgetting is only observed at rates of new neuron addition that greatly exceed adult neurogenesis rates observed in vivo. In order to address this, here we generated an artificial neural network which incorporated more realistic features of the hippocampus - including increased network size (with up to 20,000 dentate gyrus neurons), sparse activity, and sparse connectivity - features that were not present in earlier models. In addition, we explored how properties of new neurons - their connectivity, excitability, and plasticity - impact forgetting using a pattern categorization task. Our results revealed that neurogenic networks forget previously learned input-output pattern associations. This forgetting predicted a performance enhancement in subsequent conflictual learning, compared to static networks (with no added neurons). These effects were especially sensitive to changes in increased output connectivity and excitability of new neurons. Crucially, forgetting was observed at much lower rates of neurogenesis in larger networks, with the addition of as little as 0.2% of the total DG population sufficient to induce forgetting.

Dendritic solutions to the credit assignment problem.

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.