Periodic Reporting for period 2 - KiloNeurons (Population Codes for Space in the Mammalian Cortex)
Reporting period: 2022-08-01 to 2024-01-31
The key objective of this project is to identify the fundamental principles of neural computation in the space-coding circuits of the mammalian cortex. The coding is likely to take place in large, distributed and intermixed cell populations. To identify the coding algorithms in these circuits, we need population-wide activity measurements, at single-cell resolution, as well as theoretical models to interpret the data. In KILONEURONS, we combine experiments and theory to enable a paradigmatic shift from single-cell to population analysis for a prototypical high-level cortical system, the navigation system of the mammalian medial entorhinal-hippocampal region. In this system, spatial firing correlates of individual cells are so evident that they have been given simple, descriptive names – such as place cells, grid cells, and head direction cells. The wealth of information on the phenomenology of these cells, and the existence of theoretical frameworks that offer strong predictions on their population-wide activity patterns, renders the system perfect for population-level analyses of cortical computation. We are introducing experimental tools to obtain the amount and specificity of multi-neuron data required to decipher neural population codes in freely navigating rodents. Guided initially by theory on attractor network dynamics, we are identifying regularities in firing and connectivity patterns of thousands of simultaneously monitored neurons and use the data to test, refine and develop theoretical models. This exercise is extended to less-understood high-end systems such as lateral entorhinal cortex, where computational operations have remained elusive due to the lack of similar single-cell correlates. The project is transformative in that it is uncovering fundamental and general mechanisms of high-end cortical population coding in mammals.
The project is important because it pioneers theory-based neural population analysis of the kind needed to understand the mechanics of cognition in the brain, a goal that must be reached for the community to be able to target dysfunctions in neural function. While the immediate objective of the project is to get a better understanding of the fundamental principles of brain computation, such insights are necessary if we ever are to understand and treat complex brain disorders, which includes the entire spectrum of psychiatry. The studies have particular relevance for Alzheimer’s disease, which is associated with severe destruction of the entorhinal cortex during their earliest stages. A better understanding of entorhinal microcircuits and network computations might eventually help us understand the causes of the disease and find a cure. The project represents a paradigmatic shift in how we collect and analyze data and how data and analyses are used to understand high-end cortical computation by way of theoretical models. A more than half-century old tradition of studying cells one-by-one will be replaced by methods with a focus on distributed information processing in thousands of neurons with experimentally identified functional connectivity. With a renewed arsenal of methodologies we shall be able to determine the mechanistic of normal function as well as dysfunction in these systems underlying neurological and psychiatric disease.
The central objective of this proposal is to test, with novel methods for population recording and population analysis, the neural-circuit predictions of continuous attractor network models for spatial coding, which belong to the most influential theoretical frameworks in neuroscience. With access to activity and connectivity of thousands of neurons i medial entorhinal cortex, we expect to directly visualize the structure and dynamics of the joint activity of grid cells and other space-coding cells in the brain's positioning system.
The project is important because it pioneers theory-based neural population analysis of the kind needed to understand the mechanics of cognition in the brain, a goal that must be reached for the community to be able to target dysfunctions in neural function. While the immediate objective of the project is to get a better understanding of the fundamental principles of brain computation, such insights are necessary if we ever are to understand and treat complex brain disorders, which includes the entire spectrum of psychiatry. The studies have particular relevance for Alzheimer’s disease, which is associated with severe destruction of the entorhinal cortex during their earliest stages. A better understanding of entorhinal microcircuits and network computations might eventually help us understand the causes of the disease and find a cure. The project represents a paradigmatic shift in how we collect and analyze data and how data and analyses are used to understand high-end cortical computation by way of theoretical models. A more than half-century old tradition of studying cells one-by-one will be replaced by methods with a focus on distributed information processing in thousands of neurons with experimentally identified functional connectivity. With a renewed arsenal of methodologies we shall be able to determine the mechanistic of normal function as well as dysfunction in these systems underlying neurological and psychiatric disease.
The central objective of this proposal is to test, with novel methods for population recording and population analysis, the neural-circuit predictions of continuous attractor network models for spatial coding, which belong to the most influential theoretical frameworks in neuroscience. With access to activity and connectivity of thousands of neurons i medial entorhinal cortex, we expect to directly visualize the structure and dynamics of the joint activity of grid cells and other space-coding cells in the brain's positioning system.
Workpackage 1: The objective of the starting workpackage was to determine the structure and dynamics of neural population activity in grid cells and to establish whether the activity is consistent with the periodic low-dimensional manifolds predicted for grid cells in continuous attractor network models. We pioneered the use of Neuropixels probes to record activity from several thousands of neurons in freely moving rats, of which many hundred were grid cells. We found that the collective activity in a module of grid cells operated on a manifold with the topology of a torus, in complete agreement with continuous attractor network models. The findings, published in Nature (Gardner et al 2022), represent the first visualization of a continuous attractor network in the activity of mammalian neurons.
Workpackage 2: The objective here was to determine if the grid-cell network has the functional connectivity expected of a continuous attractor network. Over the course of the first 2 years of the project, we have first developed a miniature 2-photon microscope by which activity can be monitored in thousands of neurons in freely navigating mice. This was published in Cell in 2022 (Zong et al.). We are now using the miniscope to probe functional connectivity in the network and to directly test predictions about preferential connectivity made in continuous attractor network models.
Workpackage 3: This workpackage aims to determine whether the modular organization of grid cells emerges as a consequence of different birth dates of grid cells belonging to different modules. We are using a viral labelling approach to identify the embryonic birth date of neurons when recorded at adult age.
Workpackage 4: This workpackage set out to test whether grid cells of different modules are coordinated, as they should be if modules of grid cells are used collectively to encode position for the purpose of navigation. Using Neuropixels probes, we found that the states of different modules are tightly coordinated, even in darkness, pointing to mechanisms for tight coordination among modules of grid cells. The work was published in Neuron in 2022 (Waaga et al).
Workpackage 5: The objective of this workpackage was to determine the mechanistic relationship between grid-cell maps in the entorhinal cortex and place-cell maps in the hippocampus. Using dual Neuropixels recordings from the entorhinal cortex and hippocampus, we are collecting data to demonstrate how alignments of grid-cell modules might underlie map switches in hippocampal place cells.
Workpackage 6: Here we record activity in the lateral part of entorhinal cortex, where we find that population activity follows dynamics very different from that of grid cells in medial entorhinal cortex. The collective activity of thousands of neurons in the region changes with time in an episode-dependent manner.
Workpackage 2: The objective here was to determine if the grid-cell network has the functional connectivity expected of a continuous attractor network. Over the course of the first 2 years of the project, we have first developed a miniature 2-photon microscope by which activity can be monitored in thousands of neurons in freely navigating mice. This was published in Cell in 2022 (Zong et al.). We are now using the miniscope to probe functional connectivity in the network and to directly test predictions about preferential connectivity made in continuous attractor network models.
Workpackage 3: This workpackage aims to determine whether the modular organization of grid cells emerges as a consequence of different birth dates of grid cells belonging to different modules. We are using a viral labelling approach to identify the embryonic birth date of neurons when recorded at adult age.
Workpackage 4: This workpackage set out to test whether grid cells of different modules are coordinated, as they should be if modules of grid cells are used collectively to encode position for the purpose of navigation. Using Neuropixels probes, we found that the states of different modules are tightly coordinated, even in darkness, pointing to mechanisms for tight coordination among modules of grid cells. The work was published in Neuron in 2022 (Waaga et al).
Workpackage 5: The objective of this workpackage was to determine the mechanistic relationship between grid-cell maps in the entorhinal cortex and place-cell maps in the hippocampus. Using dual Neuropixels recordings from the entorhinal cortex and hippocampus, we are collecting data to demonstrate how alignments of grid-cell modules might underlie map switches in hippocampal place cells.
Workpackage 6: Here we record activity in the lateral part of entorhinal cortex, where we find that population activity follows dynamics very different from that of grid cells in medial entorhinal cortex. The collective activity of thousands of neurons in the region changes with time in an episode-dependent manner.
Advances in science tend to occur in a stepwise fashion rather than linearly. In neuroscience paradigmatic shifts are taking place at this time due to the invention of tools for recording and manipulating activity of thousands of neurons at single-cell resolution. This opens the doors to neural computation in circuits, enabling the discovery of the fundamental codes for cognitive activity. In this paradigmatic shift, the KILONEURONS project is playing a pioneering role. We have participated in the development of new Neuropixels silicon probes for large-scale simultaneous recording from thousands of neurons in behaving animals and we have developed the first miniature 2-photon microscope that is small and flexible enough to image neural activity at single-cell resolution with no disturbance of behavior in freely moving mice. We have used these advances to test key predictions of neural network theories for spatial coding, finding that neural activity in grid cells operates on a periodic toroidal manifold, in exact agreement with certain continuous attractor network models for spatial coding in grid cells. We are currently extending the testing to include connectivity in the network, establishing whether grid-cell networks exhibit preferential coupling between cells of similar function. These works have been published in widely read scientific journals like Nature and Cell.