Sequential activation of neurons is a common feature of network activity during a variety of behaviors, including working memory and decision making. Previous network models for sequences and memory ...emphasized specialized architectures in which a principled mechanism is pre-wired into their connectivity. Here we demonstrate that, starting from random connectivity and modifying a small fraction of connections, a largely disordered recurrent network can produce sequences and implement working memory efficiently. We use this process, called Partial In-Network Training (PINning), to model and match cellular resolution imaging data from the posterior parietal cortex during a virtual memory-guided two-alternative forced-choice task. Analysis of the connectivity reveals that sequences propagate by the cooperation between recurrent synaptic interactions and external inputs, rather than through feedforward or asymmetric connections. Together our results suggest that neural sequences may emerge through learning from largely unstructured network architectures.
•Sequences emerge in random networks by modifying a small fraction of their connections•Analysis reveals new circuit mechanism for input-dependent sequence propagation•Sequential activation may provide a dynamic mechanism for short-term memory
Rajan et al. show that neural sequences similar to those observed during memory-based decision-making tasks can be generated by minimally structured networks. Sequences may effectively mediate the short-term memory engaged in these tasks.
Virtual reality (VR) enables precise control of an animal’s environment and otherwise impossible experimental manipulations. Neural activity in rodents has been studied on virtual 1D tracks. However, ...2D navigation imposes additional requirements, such as the processing of head direction and environment boundaries, and it is unknown whether the neural circuits underlying 2D representations can be sufficiently engaged in VR. We implemented a VR setup for rats, including software and large-scale electrophysiology, that supports 2D navigation by allowing rotation and walking in any direction. The entorhinal-hippocampal circuit, including place, head direction, and grid cells, showed 2D activity patterns similar to those in the real world. Furthermore, border cells were observed, and hippocampal remapping was driven by environment shape, suggesting functional processing of virtual boundaries. These results illustrate that 2D spatial representations can be engaged by visual and rotational vestibular stimuli alone and suggest a novel VR tool for studying rat navigation.
•Virtual reality (VR) system for unconstrained 2D navigation in rats•Flexible control software and integration with large-scale electrophysiology•2D activity of place cells, grid cells, head direction cells, and border cells in VR•Different forms of hippocampal remapping in VR
Aronov and Tank describe a virtual-reality system for 2D navigation in rats, which allows full-body rotations. Spatial patterns in the hippocampal-entorhinal network are successfully recorded, suggesting that visual stimulation combined with physical rotation is sufficient to engage 2D firing patterns.
During spatial navigation, neural activity in the hippocampus and the medial entorhinal cortex (MEC) is correlated to navigational variables such as location, head direction, speed, and proximity to ...boundaries. These activity patterns are thought to provide a map-like representation of physical space. However, the hippocampal-entorhinal circuit is involved not only in spatial navigation, but also in a variety of memory-guided behaviours. The relationship between this general function and the specialized spatial activity patterns is unclear. A conceptual framework reconciling these views is that spatial representation is just one example of a more general mechanism for encoding continuous, task-relevant variables. Here we tested this idea by recording from hippocampal and entorhinal neurons during a task that required rats to use a joystick to manipulate sound along a continuous frequency axis. We found neural representation of the entire behavioural task, including activity that formed discrete firing fields at particular sound frequencies. Neurons involved in this representation overlapped with the known spatial cell types in the circuit, such as place cells and grid cells. These results suggest that common circuit mechanisms in the hippocampal-entorhinal system are used to represent diverse behavioural tasks, possibly supporting cognitive processes beyond spatial navigation.
The hippocampus plays a critical role in goal-directed navigation. Across different environments, however, hippocampal maps are randomized, making it unclear how goal locations could be encoded ...consistently. To address this question, we developed a virtual reality task with shifting reward contingencies to distinguish place versus reward encoding. In mice performing the task, large-scale recordings in CA1 and subiculum revealed a small, specialized cell population that was only active near reward yet whose activity could not be explained by sensory cues or stereotyped reward anticipation behavior. Across different virtual environments, most cells remapped randomly, but reward encoding consistently arose from a single pool of cells, suggesting that they formed a dedicated channel for reward. These observations represent a significant departure from the current understanding of CA1 as a relatively homogeneous ensemble without fixed coding properties and provide a new candidate for the cellular basis of goal memory in the hippocampus.
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•The hippocampus contains a dedicated population of “reward cells”•Reward cells were active at multiple reward sites in one environment•Across environments, reward cell identity was preserved despite global remapping•Reward encoding could not be explained by reward anticipation behaviors
Gauthier and Tank use in vivo imaging to identify a small population of CA1 and subiculum neurons specialized for encoding reward location. The same cells are active near multiple reward sites in one environment and even across environments during global remapping.
Hippocampal neurons encode physical variables
such as space
or auditory frequency
in cognitive maps
. In addition, functional magnetic resonance imaging studies in humans have shown that the ...hippocampus can also encode more abstract, learned variables
. However, their integration into existing neural representations of physical variables
is unknown. Here, using two-photon calcium imaging, we show that individual neurons in the dorsal hippocampus jointly encode accumulated evidence with spatial position in mice performing a decision-making task in virtual reality
. Nonlinear dimensionality reduction
showed that population activity was well-described by approximately four to six latent variables, which suggests that neural activity is constrained to a low-dimensional manifold. Within this low-dimensional space, both physical and abstract variables were jointly mapped in an orderly manner, creating a geometric representation that we show is similar across mice. The existence of conjoined cognitive maps suggests that the hippocampus performs a general computation-the creation of task-specific low-dimensional manifolds that contain a geometric representation of learned knowledge.
The posterior parietal cortex (PPC) has an important role in many cognitive behaviours; however, the neural circuit dynamics underlying PPC function are not well understood. Here we optically imaged ...the spatial and temporal activity patterns of neuronal populations in mice performing a PPC-dependent task that combined a perceptual decision and memory-guided navigation in a virtual environment. Individual neurons had transient activation staggered relative to one another in time, forming a sequence of neuronal activation spanning the entire length of a task trial. Distinct sequences of neurons were triggered on trials with opposite behavioural choices and defined divergent, choice-specific trajectories through a state space of neuronal population activity. Cells participating in the different sequences and at distinct time points in the task were anatomically intermixed over microcircuit length scales (<100 micrometres). During working memory decision tasks, the PPC may therefore perform computations through sequence-based circuit dynamics, rather than long-lived stable states, implemented using anatomically intermingled microcircuits.
Linking neural microcircuit function to emergent properties of the mammalian brain requires fine-scale manipulation and measurement of neural activity during behavior, where each neuron's coding and ...dynamics can be characterized. We developed an optical method for simultaneous cellular-resolution stimulation and large-scale recording of neuronal activity in behaving mice. Dual-wavelength two-photon excitation allowed largely independent functional imaging with a green fluorescent calcium sensor (GCaMP3, λ = 920 ± 6 nm) and single-neuron photostimulation with a red-shifted optogenetic probe (C1V1, λ = 1,064 ± 6 nm) in neurons coexpressing the two proteins. We manipulated task-modulated activity in individual hippocampal CA1 place cells during spatial navigation in a virtual reality environment, mimicking natural place-field activity, or 'biasing', to reveal subthreshold dynamics. Notably, manipulating single place-cell activity also affected activity in small groups of other place cells that were active around the same time in the task, suggesting a functional role for local place cell interactions in shaping firing fields.
We demonstrate that channelrhodopsin-2 (CR), a light-gated ion Channel that is conventionally activated by using visible-light excitation, can also be activated by using IR two-photon excitation ...(TPE). An empirical estimate of CR's two-photon absorption crosssection at λ = 920 nm is presented, with a value (260 ± 20 GM) indicating that TPE stimulation of CR photocurrents is not typically limited by intrinsic molecular excitability 1 GM = 10⁻₄₀ (cm₄ s)/photon. By using direct physiological measurements of CR photocurrents and a model of ground-state depletion, we evaluate how saturation of CR's current-conducting state influences the spatial resolution of focused TPE photostimulation, and how photocurrents stimulated by using low-power scanning TPE temporally summate. We show that TPE, like visible-light excitation, can be used to stimulate action potentials in cultured CR-expressing neurons.
There is increased appreciation that dopamine neurons in the midbrain respond not only to reward
and reward-predicting cues
, but also to other variables such as the distance to reward
, movements
...and behavioural choices
. An important question is how the responses to these diverse variables are organized across the population of dopamine neurons. Whether individual dopamine neurons multiplex several variables, or whether there are subsets of neurons that are specialized in encoding specific behavioural variables remains unclear. This fundamental question has been difficult to resolve because recordings from large populations of individual dopamine neurons have not been performed in a behavioural task with sufficient complexity to examine these diverse variables simultaneously. Here, to address this gap, we used two-photon calcium imaging through an implanted lens to record the activity of more than 300 dopamine neurons from the ventral tegmental area of the mouse midbrain during a complex decision-making task. As mice navigated in a virtual-reality environment, dopamine neurons encoded an array of sensory, motor and cognitive variables. These responses were functionally clustered, such that subpopulations of neurons transmitted information about a subset of behavioural variables, in addition to encoding reward. These functional clusters were spatially organized, with neighbouring neurons more likely to be part of the same cluster. Together with the topography between dopamine neurons and their projections, this specialization and anatomical organization may aid downstream circuits in correctly interpreting the wide range of signals transmitted by dopamine neurons.
Discussions of the hippocampus often focus on place cells, but many neurons are not place cells in any given environment. Here we describe the collective activity in such mixed populations, treating ...place and non-place cells on the same footing. We start with optical imaging experiments on CA1 in mice as they run along a virtual linear track and use maximum entropy methods to approximate the distribution of patterns of activity in the population, matching the correlations between pairs of cells but otherwise assuming as little structure as possible. We find that these simple models accurately predict the activity of each neuron from the state of all the other neurons in the network, regardless of how well that neuron codes for position. Our results suggest that understanding the neural activity may require not only knowledge of the external variables modulating it but also of the internal network state.
•A successful unified theoretical framework for population states•Maximum entropy model predictions have high precision agreement with data•Network interactions explain a substantial amount of population activity in CA1•Place cells and non-place cells encode information collectively
Correlation patterns in CA1 hippocampus only partially arise from place encoding. Meshulam et al. utilize a population-level modeling approach to uncover collective patterns of activity in CA1 neurons that substantially reflect not only position but also their internal network state.
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