Correction: Distinct neuronal populations contribute to trace conditioning and extinction learning in the hippocampal CA1

Distinct neuronal populations contribute to trace conditioning and extinction learning in the hippocampal CA1

eLife ◽

10.7554/elife.56491 ◽

2021 ◽

Vol 10 ◽

Author(s):

Rebecca A Mount ◽

Sudiksha Sridhar ◽

Kyle R Hansen ◽

Ali I Mohammed ◽

Moona E Abdulkerim ◽

...

Keyword(s):

Neural Activity ◽

Calcium Imaging ◽

Network Connectivity ◽

Trace Conditioning ◽

Extinction Learning ◽

Ca1 Neurons ◽

Neuronal Populations ◽

Hippocampal Ca1 ◽

Conditioned Learning ◽

Connectivity Patterns

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.

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Distinct Neuronal Populations Contribute to Trace Conditioning and Extinction Learning in the Hippocampal CA1

10.1101/2020.03.06.980854 ◽

2020 ◽

Author(s):

Rebecca A. Mount ◽

Kyle R. Hansen ◽

Sudiksha Sridhar ◽

Ali I. Mohammed ◽

Moona Abdulkerim ◽

...

Keyword(s):

Network Connectivity ◽

Fluorescence Analysis ◽

Trace Conditioning ◽

Extinction Learning ◽

Ca1 Neurons ◽

Neuronal Populations ◽

Neural Activities ◽

Hippocampal Ca1 ◽

Conditioned Learning ◽

Connectivity Patterns

AbstractTrace conditioning and extinction learning depend on the hippocampus, but it remains unclear how ongoing neural activities in the hippocampus are modulated during different learning processes. To explore this question, we performed calcium imaging in a large number of individual CA1 neurons during both trace eye-blink conditioning and subsequent extinction learning in mice. Using trial-averaged calcium fluorescence analysis, we found direct evidence that in real time, as learning emerges, distinct populations of CA1 cells contribute to trace conditioned learning versus extinction learning. Furthermore, we examined network connectivity by calculating co-activity between CA1 neuron pairs, and found that CA1 network connectivity is different between conditioning and extinction and between correct versus incorrect behavioral responses during trace conditioned learning. However, the overall connectivity density remains constant across these behavioral conditions. Together, our results demonstrate that distinct populations of CA1 neurons, forming different sub-networks with unique connectivity patterns, encode different aspects of learning.

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Active Inference and Learning in the Cerebellum

Neural Computation ◽

10.1162/neco_a_00863 ◽

2016 ◽

Vol 28 (9) ◽

pp. 1812-1839 ◽

Cited By ~ 7

Author(s):

Karl Friston ◽

Ivan Herreros

Keyword(s):

Eyeblink Conditioning ◽

Predictive Coding ◽

Trace Conditioning ◽

Face Validity ◽

Active Inference ◽

Focal Lesions ◽

Delay Conditioning ◽

Neuronal Populations ◽

Intrinsic Connectivity ◽

The Face

This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and (delay or trace) conditioning. We then establish the face validity of the model using simulated responses to unconditioned and conditioned stimuli to reproduce the sorts of behavior that are observed empirically. The scheme’s anatomical validity is then addressed by associating variables in the predictive coding scheme with nuclei and neuronal populations to match the (extrinsic and intrinsic) connectivity of the cerebellar (eyeblink conditioning) system. Finally, we try to establish predictive validity by reproducing selective failures of delay conditioning, trace conditioning, and extinction using (simulated and reversible) focal lesions. Although rather metaphorical, the ensuing scheme can account for a remarkable range of anatomical and neurophysiological aspects of cerebellar circuitry—and the specificity of lesion-deficit mappings that have been established experimentally. From a computational perspective, this work shows how conditioning or learning can be formulated in terms of minimizing variational free energy (or maximizing Bayesian model evidence) using exactly the same principles that underlie predictive coding in perception.

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Conjunctive spatial and idiothetic codes are topographically organized in the lateral septum

10.1101/2020.11.03.366849 ◽

2020 ◽

Author(s):

Suzanne van der Veldt ◽

Guillaume Etter ◽

Fernanda Sosa ◽

Coralie-Anne Mosser ◽

Sylvain Williams

Keyword(s):

Medial Septum ◽

Place Cells ◽

Lateral Septum ◽

Head Direction ◽

Neuronal Populations ◽

Spatial Tuning ◽

Hippocampal Ca1 ◽

Memory Network ◽

Anterior Posterior

AbstractThe relevance of the hippocampal spatial code for downstream neuronal populations – in particular its main subcortical output, the lateral septum (LS) - is still poorly understood. Here, we addressed this knowledge gap by first clarifying the organization of LS afferents and efferents via retrograde and anterograde trans-synaptic tracing. We found that mouse LS receives inputs from hippocampal subregions CA1, CA3, and subiculum, and in turn projects directly to the lateral hypothalamus (LH), ventral tegmental area (VTA), and medial septum (MS). Next, we functionally characterized the spatial tuning properties of LS GABAergic cells, the principal cells composing the LS, via calcium imaging combined with unbiased analytical methods. We identified a significant number of cells that are modulated by place (38.01%), speed (23.71%), acceleration (27.84%), and head-direction (23.09%), and conjunctions of these properties, with spatial tuning comparable to hippocampal CA1 and CA3 place cells. Bayesian decoding of position on the basis of LS place cells accurately reflected the location of the animal. The distributions of cells exhibiting these properties formed gradients along the anterior-posterior axis of the LS, directly reflecting the organization of hippocampal inputs to the LS. A portion of LS place cells showed stable fields over the course of multiple days, potentially reflecting long-term episodic memory. Together, our findings demonstrate that the LS accurately and robustly represents spatial and idiothetic information and is uniquely positioned to relay this information from the hippocampus to the VTA, LH, and MS, thus occupying a key position within this distributed spatial memory network.

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Conjunctive spatial and self-motion codes are topographically organized in the GABAergic cells of the lateral septum

PLoS Biology ◽

10.1371/journal.pbio.3001383 ◽

2021 ◽

Vol 19 (8) ◽

pp. e3001383

Author(s):

Suzanne van der Veldt ◽

Guillaume Etter ◽

Coralie-Anne Mosser ◽

Frédéric Manseau ◽

Sylvain Williams

Keyword(s):

Dorsal Hippocampus ◽

Pyramidal Cells ◽

Medial Septum ◽

Excitatory Input ◽

Lateral Septum ◽

Neuronal Populations ◽

Hippocampal Ca1 ◽

Memory Network ◽

Self Motion

The hippocampal spatial code’s relevance for downstream neuronal populations—particularly its major subcortical output the lateral septum (LS)—is still poorly understood. Here, using calcium imaging combined with unbiased analytical methods, we functionally characterized and compared the spatial tuning of LS GABAergic cells to those of dorsal CA3 and CA1 cells. We identified a significant number of LS cells that are modulated by place, speed, acceleration, and direction, as well as conjunctions of these properties, directly comparable to hippocampal CA1 and CA3 spatially modulated cells. Interestingly, Bayesian decoding of position based on LS spatial cells reflected the animal’s location as accurately as decoding using the activity of hippocampal pyramidal cells. A portion of LS cells showed stable spatial codes over the course of multiple days, potentially reflecting long-term episodic memory. The distributions of cells exhibiting these properties formed gradients along the anterior–posterior and dorsal–ventral axes of the LS, directly reflecting the topographical organization of hippocampal inputs to the LS. Finally, we show using transsynaptic tracing that LS neurons receiving CA3 and CA1 excitatory input send projections to the hypothalamus and medial septum, regions that are not targeted directly by principal cells of the dorsal hippocampus. Together, our findings demonstrate that the LS accurately and robustly represents spatial, directional as well as self-motion information and is uniquely positioned to relay this information from the hippocampus to its downstream regions, thus occupying a key position within a distributed spatial memory network.

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