Periodic Reporting for period 1 - neuronsXnets (Network Analysis in Neocortex during Passive and Active Learning)
Periodo di rendicontazione: 2021-09-01 al 2023-08-31
How does the brain perform the complicated computations that allow us to learn about and interact with the environment? The rapid advances in new optical imaging, the powerful statistical analysis/machine-learning techniques, and the availability of computational resources, provide a unique opportunity to decipher this fundamental question. NeuronsXnets forms an international, multidisciplinary and intersectoral collaboration network involving leading groups in neuroscience, neuromorphic computing, data science, systems, optical tools/imaging, and hospitals. It takes advantage of this unique environment to improve the understanding of neural circuit function and integrate its findings in deep learning architectures and in neuromorphic circuits, aiming to develop a new generation of computing technologies based on the organizing principles of the biological nervous system, optimized for higher levels of cognition. NeuronsXnets will perform knowledge transfer through hands-on training and research activities during 196 secondments, courses, 4 international workshops, and seminars, to train a new generation of highly skilled systems neuroscientists and computer scientists/engineers. All partners, especially the SMEs, will benefit from the cross-fertilization, by integrating the findings and systems they have developed in their existing solutions or in new systems, capitalizing on the research results that will be achieved, and creating a long-term link between business, research, higher education & hospitals. Through open access to the developed tools and collected data, NeuronsXnets can make significant impact on the scientific community. We are committed to educate and mentor young students and raise the public knowledge about the fascinating field of neuroscience. We will strive for research excellence, to develop innovative systems, and pursue entrepreneurial objectives, leading to a new era of collaborative research in neuroscience & bio-inspired technologies.
The most mature research results produced in the first two years of the project focused on the following three topics:
* Neuronal computational and communication modules of the functional architecture in the primary visual cortex (AREADNE 2022, COSYNE 2024)
* Effects of working memory training on cognitive flexibility, dendritic spine density, and long-term potentiation in female mice
* Glutamate-specific gene linked to human brain evolution enhances synaptic plasticity and cognitive processes (iScience 2024)
1) Much has been learned about the computational properties of single neurons. However, we remain far from understanding how networks of cortical cells coordinate and interact with each other to process information. Several pioneering works (e.g. Lorente de Nó 1949, Hebb 1948, Abeles 1993, Carrillo-Reid 2016) have proposed theories regarding how the configuration of neuronal ensembles encodes information in the cortex. Within these ensembles sets of coactive neurons, demonstrate synchronous or correlated activity in both spontaneous and evoked neural activity. Ensembles of neurons that fire in synchrony are likely to be more efficient at relaying shared information to downstream targets as well as more likely to belong to networks of neurons subserving similar functions. How groups of neurons coordinate with each other to parse information is not well understood. Spontaneous activity occurs in the absence of external stimulation and corresponds to internally generated patterns of neural activity. Under these conditions, pairwise functional connectivity, a measure of synchrony across pairs of neurons, is shaped either directly or indirectly by anatomical connectivity, though the dependencies and exact mechanisms are still not well-understood. It is important to understand the structure of spontaneous activity and what it tells us about computational processing in the brain. With the availability of advanced imaging technologies, computational techniques, and datasets from thousands of neurons, the analysis of the functional connectivity patterns under spontaneous conditions becomes valuable, since it can reveal the processes that compose the internal state of the animal, providing a set of benchmarks for the quantification of the changes due to learning or various neurological diseases.
We used large-field 2-photon imaging to record simultaneously from layer 4 and layers 2,3 of mouse area V1 under spontaneous conditions, from thousands of neurons and identified functional connectivity patterns within and across layers: organizational modules, consisting of groups of neurons that fire in synchrony more than expected by chance. A substantial number of statistically significant pairwise neuronal correlations exist, some of which are reasonably long-range (up to 1 mm). Anticorrelated ones appear to be more spread out spatially, peaking on average at larger distances. Overall, there is little difference in the distribution of pairwise correlation strengths under different conditions, suggesting that the distribution of strengths of functional correlations may reflect a universal principle of “allowable” pyramidal neuron to pyramidal neuron functional correlations within cortical microcircuits. The architecture of the functional connectivity differs across layers.
2)Working memory (WM) is a cognitive function that refers to the ability of short-term storage and manipulation of information necessary for the accomplishment of a task. Two brain regions involved in WM are the prefrontal cortex (PFC) and the hippocampus (HPC). Several studies have suggested that training in WM (WMT) can improve performance in other cognitive tasks. However, our understanding of the neurobiological changes induced by WMT is very limited. Previous work from our lab showed that WMT enhances synaptic and structural plasticity in the PFC and HPC in male mice. We investigated the effect of WMT on cognitive flexibility and synaptic properties in PFC and HPC in adult female mice. Learning working memory enhances the function of the hippocampus, brain regions involved in spatial memory.
3) The human brain is characterized by upregulation of synaptic, mainly glutamatergic, transmission but its evolutionary origin(s) remain elusive. We approached this fundamental question by studying mice transgenic (Tg) for GLUD2, a human gene involved in glutamate metabolism that emerged in the hominoid and evolved concomitantly with brain expansion, and demonstrated that GLUD2 enhances cellular function in the hippocampus and learning.
* Neuronal computational and communication modules of the functional architecture in the primary visual cortex (AREADNE 2022, COSYNE 2024)
* Effects of working memory training on cognitive flexibility, dendritic spine density, and long-term potentiation in female mice
* Glutamate-specific gene linked to human brain evolution enhances synaptic plasticity and cognitive processes (iScience 2024)
1) Much has been learned about the computational properties of single neurons. However, we remain far from understanding how networks of cortical cells coordinate and interact with each other to process information. Several pioneering works (e.g. Lorente de Nó 1949, Hebb 1948, Abeles 1993, Carrillo-Reid 2016) have proposed theories regarding how the configuration of neuronal ensembles encodes information in the cortex. Within these ensembles sets of coactive neurons, demonstrate synchronous or correlated activity in both spontaneous and evoked neural activity. Ensembles of neurons that fire in synchrony are likely to be more efficient at relaying shared information to downstream targets as well as more likely to belong to networks of neurons subserving similar functions. How groups of neurons coordinate with each other to parse information is not well understood. Spontaneous activity occurs in the absence of external stimulation and corresponds to internally generated patterns of neural activity. Under these conditions, pairwise functional connectivity, a measure of synchrony across pairs of neurons, is shaped either directly or indirectly by anatomical connectivity, though the dependencies and exact mechanisms are still not well-understood. It is important to understand the structure of spontaneous activity and what it tells us about computational processing in the brain. With the availability of advanced imaging technologies, computational techniques, and datasets from thousands of neurons, the analysis of the functional connectivity patterns under spontaneous conditions becomes valuable, since it can reveal the processes that compose the internal state of the animal, providing a set of benchmarks for the quantification of the changes due to learning or various neurological diseases.
We used large-field 2-photon imaging to record simultaneously from layer 4 and layers 2,3 of mouse area V1 under spontaneous conditions, from thousands of neurons and identified functional connectivity patterns within and across layers: organizational modules, consisting of groups of neurons that fire in synchrony more than expected by chance. A substantial number of statistically significant pairwise neuronal correlations exist, some of which are reasonably long-range (up to 1 mm). Anticorrelated ones appear to be more spread out spatially, peaking on average at larger distances. Overall, there is little difference in the distribution of pairwise correlation strengths under different conditions, suggesting that the distribution of strengths of functional correlations may reflect a universal principle of “allowable” pyramidal neuron to pyramidal neuron functional correlations within cortical microcircuits. The architecture of the functional connectivity differs across layers.
2)Working memory (WM) is a cognitive function that refers to the ability of short-term storage and manipulation of information necessary for the accomplishment of a task. Two brain regions involved in WM are the prefrontal cortex (PFC) and the hippocampus (HPC). Several studies have suggested that training in WM (WMT) can improve performance in other cognitive tasks. However, our understanding of the neurobiological changes induced by WMT is very limited. Previous work from our lab showed that WMT enhances synaptic and structural plasticity in the PFC and HPC in male mice. We investigated the effect of WMT on cognitive flexibility and synaptic properties in PFC and HPC in adult female mice. Learning working memory enhances the function of the hippocampus, brain regions involved in spatial memory.
3) The human brain is characterized by upregulation of synaptic, mainly glutamatergic, transmission but its evolutionary origin(s) remain elusive. We approached this fundamental question by studying mice transgenic (Tg) for GLUD2, a human gene involved in glutamate metabolism that emerged in the hominoid and evolved concomitantly with brain expansion, and demonstrated that GLUD2 enhances cellular function in the hippocampus and learning.
Apart from the aforementioned scientific contributions, neuronsXnets performed extensive training activities, including the development and teaching yearly of a new graduate-level course on brain networks, supervision of several B.Sc. (13), M.Sc (7), Ph.D. (4) students, student interns (15), and postdoctoral researchers (9) in research projects closely related to the proposed research as well as outreach activities (e.g. invited talks at seminars/conferences, local workshops, EU MSCA events organized by NCPs, corporate dissemination activities of SMSs, and presentations in Open Science and Career Days events). It is committed to promoting the involvement of women: a significant number of key scientists graduate students, and undergraduates involved in the project are female (7/18 secondees are female).