Learning Compact DNN Models for Behavior Prediction from Neural Activity of Calcium Imaging

Author(s):  
Xiaomin Wu ◽  
Da-Ting Lin ◽  
Rong Chen ◽  
Shuvra S. Bhattacharyya
2021 ◽  
Author(s):  
Christopher D Harvey ◽  
Charlotte Arlt ◽  
Roberto Barroso-Luque ◽  
Shinichiro Kira ◽  
Carissa A Bruno ◽  
...  

The neural correlates of decision-making have been investigated extensively, and recent work aims to identify under what conditions cortex is actually necessary for making accurate decisions. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same task, revealing past learning as a critical determinant of whether cortex is necessary for decision-making. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate decision-making in mice performing a simple navigation-based decision task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multi-area calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron-neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience sets the landscape for how future tasks are solved by the brain and is a key factor in determining whether cortical areas have a causal role in decision-making.


eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Markus Frey ◽  
Sander Tanni ◽  
Catherine Perrodin ◽  
Alice O'Leary ◽  
Matthias Nau ◽  
...  

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors – including a novel representation of head direction - from raw neural activity.


2021 ◽  
Author(s):  
Jinyong Zhang ◽  
Ryan N Hughes ◽  
Namsoo Kim ◽  
Isabella P Fallon ◽  
Konstantin I bakhurin ◽  
...  

While in vivo calcium imaging makes it possible to record activity in defined neuronal populations with cellular resolution, optogenetics allows selective manipulation of neural activity. Recently, these two tools have been combined to stimulate and record neural activity at the same time, but current approaches often rely on two-photon microscopes that are difficult to use in freely moving animals. To address these limitations, we have developed a new integrated system combining a one-photon endoscope and a digital micromirror device for simultaneous calcium imaging and precise optogenetic photo-stimulation with near cellular resolution (Miniscope with All-optical Patterned Stimulation and Imaging, MAPSI). Using this highly portable system in freely moving mice, we were able to image striatal neurons from either the direct pathway or the indirect pathway while simultaneously activating any neuron of choice in the field of view, or to synthesize arbitrary spatiotemporal patterns of photo-stimulation. We could also select neurons based on their relationship with behavior and recreate the behavior by mimicking the natural neural activity with photo-stimulation. MAPSI thus provides a powerful tool for interrogation of neural circuit function in freely moving animals.


Author(s):  
Antonia Strutz ◽  
Thomas Völler ◽  
Thomas Riemensperger ◽  
André Fiala ◽  
Silke Sachse

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Rebecca A Mount ◽  
Sudiksha Sridhar ◽  
Kyle R Hansen ◽  
Ali I Mohammed ◽  
Moona E Abdulkerim ◽  
...  

Trace conditioning and extinction learning depend on the hippocampus, but it remains unclear how neural activity in the hippocampus is modulated during these two different behavioral processes. To explore this question, we performed calcium imaging from a large number of individual CA1 neurons during both trace eye-blink conditioning and subsequent extinction learning in mice. Our findings reveal that distinct populations of CA1 cells contribute to trace conditioned learning versus extinction learning, as learning emerges. Furthermore, we examined network connectivity by calculating co-activity between CA1 neuron pairs and found that CA1 network connectivity patterns also differ between conditioning and extinction, even though the overall connectivity density remains constant. Together, our results demonstrate that distinct populations of hippocampal CA1 neurons, forming different sub-networks with unique connectivity patterns, encode different aspects of learning.


2020 ◽  
Vol 16 (11) ◽  
pp. e1008330
Author(s):  
Marcus A. Triplett ◽  
Zac Pujic ◽  
Biao Sun ◽  
Lilach Avitan ◽  
Geoffrey J. Goodhill

The pattern of neural activity evoked by a stimulus can be substantially affected by ongoing spontaneous activity. Separating these two types of activity is particularly important for calcium imaging data given the slow temporal dynamics of calcium indicators. Here we present a statistical model that decouples stimulus-driven activity from low dimensional spontaneous activity in this case. The model identifies hidden factors giving rise to spontaneous activity while jointly estimating stimulus tuning properties that account for the confounding effects that these factors introduce. By applying our model to data from zebrafish optic tectum and mouse visual cortex, we obtain quantitative measurements of the extent that neurons in each case are driven by evoked activity, spontaneous activity, and their interaction. By not averaging away potentially important information encoded in spontaneous activity, this broadly applicable model brings new insight into population-level neural activity within single trials.


2020 ◽  
Author(s):  
Ashwin Vishwanathan ◽  
Alexandro D. Ramirez ◽  
Jingpeng Wu ◽  
Alex Sood ◽  
Runzhe Yang ◽  
...  

AbstractNeuronal wiring diagrams reconstructed from electron microscopic images are enabling new ways of attacking neuroscience questions. We address two central issues, modularity and neural coding, by reconstructing and analyzing a wiring diagram from a larval zebrafish brainstem. We identified a recurrently connected “center” within the 3000-node graph, and applied graph clustering algorithms to divide the center into two modules with stronger connectivity within than between modules. Outgoing connection patterns and registration to maps of neural activity suggested the modules were specialized for body and eye movements. The eye movement module further subdivided into two submodules corresponding to the control of the two eyes. We constructed a recurrent network model of the eye movement module with connection strengths estimated from synapse numbers. Neural activity in the model replicated the statistics of eye position encoding across multiple populations of neurons as observed by calcium imaging. Our findings show that synapse-level wiring diagrams can be used to extract structural modules with interpretable functions in the vertebrate brain, and can be related to the encoding of computational variables important for behavior. We also show through a potential synapse formalism that these modeling successes require true synaptic connectivity; connectivity inferred from arbor overlap is insufficient.


Sign in / Sign up

Export Citation Format

Share Document