The hippocampus has been used extensively as a model to study plastic changes in the brain's neural circuitry. Immediately after high-frequency stimulation to hippocampal Schaffer collateral axons, a dramatic change occurs in the relationship between the presynaptic CA3 and the postsynaptic CA1 pyramidal neurons. For a fixed excitatory postsynaptic potential (EPSP), there arises an increased likelihood of action potential generation in the CA1 pyramidal neuron. This phenomenon is called EPSP-spike (E-S) potentiation. We explored E-S potentiation, using patch-clamp techniques in the hippocampal slice preparation. A specific protocol was developed to measure the action potential probability for a given synaptic strength, which allowed us to quantify the amount of E-S potentiation for a single neuron. E-S potentiation was greatest when gamma-aminobutyric acid (GABA)ergic inhibition was intact, suggesting that modulation of inhibition is a major aspect of E-S potentiation. Expression of E-S potentiation also correlated with a reduced action-potential threshold, which was greatest when GABAergic inhibition was intact. Conditioning stimuli produced a smaller threshold reduction when inhibition was blocked, but some reduction also occurred in the absence of a conditioning stimulus. Together, these results suggest that E-S potentiation is caused primarily through a reduction of GABAergic inhibition, leading to larger EPSPs and reduced action potential threshold. Our findings do not rule out, however, the possibility that modulation of voltage-gated conductances also contributes to E-S potentiation.

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, 'state cells'). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.

To study how the brain drives cognition and behavior we need to understand its cellular composition. Advances in single-cell transcriptomics have revolutionized our ability to characterize neuronal diversity. To arrive at meaningful descriptions of cell types, however, gene expression must be linked to structural and functional properties. Axonal projection patterns are an appropriate measure, as they are diverse, change only gradually over time, and they influence and constrain circuit function. Here, we consider how efforts to map transcriptional and morphological diversity in the mouse brain could be linked to generate a modern taxonomy of the mouse brain.

To study how the brain drives cognition and behavior we need to understand its cellular composition. Advances in single-cell transcriptomics have revolutionized our ability to characterize neuronal diversity. To arrive at meaningful descriptions of cell types, however, gene expression must be linked to structural and functional properties. Axonal projection patterns are an appropriate measure, as they are diverse, change only gradually over time, and they influence and constrain circuit function. Here, we consider how efforts to map transcriptional and morphological diversity in the mouse brain could be linked to generate a modern taxonomy of the mouse brain.

Understanding the principles governing neuronal diversity is a fundamental goal for neuroscience. Here we provide an anatomical and transcriptomic database of nearly 200 genetically identified cell populations. By separately analyzing the robustness and pattern of expression differences across these cell populations, we identify two gene classes contributing distinctly to neuronal diversity. Short homeobox transcription factors distinguish neuronal populations combinatorially, and exhibit extremely low transcriptional noise, enabling highly robust expression differences. Long neuronal effector genes, such as channels and cell adhesion molecules, contribute disproportionately to neuronal diversity, based on their patterns rather than robustness of expression differences. By linking transcriptional identity to genetic strains and anatomical atlases we provide an extensive resource for further investigation of mouse neuronal cell types.

Abstract We recently described a new form of neural integration and firing in a subset of interneurons, in which evoking hundreds of action potentials over tens of seconds to minutes produces a sudden barrage of action potentials lasting about a minute beyond the inciting stimulation. During this persistent firing, action potentials are generated in the distal axon and propagate retrogradely to the soma. To distinguish this from other forms of persistent firing, we refer to it here as ’retroaxonal barrage firing’, or ’barrage firing’ for short. Its induction is blocked by chemical inhibitors of gap junctions and curiously, stimulation of one interneuron in some cases triggers barrage firing in a nearby, unstimulated interneuron. Beyond these clues, the mechanisms of barrage firing are unknown. Here we report new results related to these mechanisms. Induction of barrage firing was blocked by lowering extracellular calcium, as long as normal action potential threshold was maintained, and it was inhibited by blocking L-type voltage-gated calcium channels. Despite its calcium dependence, barrage firing was not prevented by inhibiting chemical synaptic transmission. Furthermore, loading the stimulated/recorded interneuron with BAPTA did not block barrage firing, suggesting that the required calcium entry occurs in other cells. Finally, barrage firing was normal in mice with deletion of the primary gene for neuronal gap junctions (connexin36), suggesting that non-neuronal gap junctions may be involved. Together, these findings suggest that barrage firing is probably triggered by a multicellular mechanism involving calcium signalling and gap junctions, but operating independently of chemical synaptic transmission.

Excitatory postsynaptic currents in neurones of the central nervous system have a dual-component time course that results from the co-activation of AMPA/kainate-type and NMDA-type glutamate receptors. New approaches in electrophysiology and molecular biology have provided a better understanding of the factors that determine the kinetics of excitatory postsynaptic currents. Recent studies suggest that the time course of neurotransmitter concentration in the synaptic cleft, the gating properties of the native channels, and the glutamate receptor subunit composition all appear to be important factors.

As animals navigate, they must identify features within context. In the mammalian brain, the hippocampus has the ability to separately encode different environmental contexts, even when they share some prominent features. To do so, neurons respond to sensory features in a context-dependent manner; however, it is not known how this encoding emerges. To examine this, we performed electrical recordings in the hippocampus as mice navigated in two distinct virtual environments. In CA1, both synaptic input to single neurons and population activity strongly tracked visual cues in one environment, whereas responses were almost completely absent when the same cue was presented in a second environment. A very similar, highly context-dependent pattern of cue-driven spiking was also observed in CA3. These results indicate that CA1 inherits a complex spatial code from upstream regions, including CA3, that have already computed a context-dependent representation of environmental features.

Animals can learn general task structures and use them to solve new problems with novel sensory specifics. This capacity of ‘learning to learn’, or meta-learning, is difficult to achieve in artificial systems, and the mechanisms by which it is achieved in animals are unknown. As a step toward enabling mechanistic studies, we developed a behavioral paradigm that demonstrates meta-learning in head-fixed mice. We trained mice to perform a two-alternative forced-choice task in virtual reality (VR), and successively changed the visual cues that signaled reward location. Mice showed increased learning speed in both cue generalization and serial reversal tasks. During reversal learning, behavior exhibited sharp transitions, with the transition occurring earlier in each successive reversal. Analysis of motor patterns revealed that animals utilized similar motor programs to execute the same actions in response to different cues but modified the motor programs during reversal learning. Our study demonstrates that mice can perform meta-learning tasks in VR, thus opening up opportunities for future mechanistic studies.

Electrophysiology is the most used approach for the collection of functional data in basic and translational neuroscience, but it is typically limited to either intracellular or extracellular recordings. The integration of multiple physiological modalities for the routine acquisition of multimodal data with microelectrodes could be useful for biomedical applications, yet this has been challenging owing to incompatibilities of fabrication methods. Here, we present a suite of glass pipettes with integrated microelectrodes for the simultaneous acquisition of multimodal intracellular and extracellular information in vivo, electrochemistry assessments, and optogenetic perturbations of neural activity. We used the integrated devices to acquire multimodal signals from the CA1 region of the hippocampus in mice and rats, and show that these data can serve as ground-truth validation for the performance of spike-sorting algorithms. The microdevices are applicable for basic and translational neurobiology, and for the development of next-generation brain-machine interfaces.