Rotation of visual landmark cues influences the spatial response profile of hippocampal neurons in freely-moving homing pigeons

AbstractPlace cells in the mammalian hippocampus signal self-location with sparse spatially stable firing fields. Based on observation of place cell activity it is possible to accurately decode an animal’s location. The precision of this decoding sets a lower bound for the amount of information that the hippocampal population conveys about the location of the animal. In this work we use a novel recurrent neural network (RNN) decoder to infer the location of freely moving rats from single unit hippocampal recordings. RNNs are biologically plausible models of neural circuits that learn to incorporate relevant temporal context without the need to make complicated assumptions about the use of prior information to predict the current state. When decoding animal position from spike counts in 1D and 2D-environments, we show that the RNN consistently outperforms a standard Bayesian model with flat priors. In addition, we also conducted a set of sensitivity analysis on the RNN decoder to determine which neurons and sections of firing fields were the most influential. We found that the application of RNNs to neural data allowed flexible integration of temporal context, yielding improved accuracy relative to a commonly used Bayesian approach and opens new avenues for exploration of the neural code.Author summaryBeing able to accurately self-localize is critical for most motile organisms. In mammals, place cells in the hippocampus appear to be a central component of the brain network responsible for this ability. In this work we recorded the activity of a population of hippocampal neurons from freely moving rodents and carried out neural decoding to determine the animals’ locations. We found that a machine learning approach using recurrent neural networks (RNNs) allowed us to predict the rodents’ true positions more accurately than a standard Bayesian method with flat priors. The RNNs are able to take into account past neural activity without making assumptions about the statistics of neuronal firing. Further, by analyzing the representations learned by the network we were able to determine which neurons, and which aspects of their activity, contributed most strongly to the accurate decoding.

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Persistent trajectory-modulated hippocampal neurons support memory-guided navigation

10.1101/676593 ◽

2019 ◽

Author(s):

Nathaniel R. Kinsky ◽

William Mau ◽

David W. Sullivan ◽

Samuel J. Levy ◽

Evan A. Ruesch ◽

...

Keyword(s):

Calcium Imaging ◽

Hippocampal Neurons ◽

Spatial Information ◽

Single Photon ◽

Place Cells ◽

Dependent Activity ◽

Freely Moving ◽

Stable Trajectory ◽

Alternation Task

ABSTRACTTrajectory-dependent splitter neurons in the hippocampus encode information about a rodent’s prior trajectory during performance of a continuous alternation task. As such, they provide valuable information for supporting memory-guided behavior. Here, we employed single-photon calcium imaging in freely moving mice to investigate the emergence and fate of trajectory-dependent activity through learning and mastery of a continuous spatial alternation task. We found that the quality of trajectory-dependent information in hippocampal neurons correlated with task performance. We thus hypothesized that, due to their utility, splitter neurons would exhibit heightened stability. We found that splitter neurons were more likely to remain active and retained more consistent spatial information across multiple days than did place cells. Furthermore, we found that both splitter neurons and place cells emerged rapidly and maintained stable trajectory-dependent/spatial activity thereafter. Our results suggest that neurons with useful functional coding properties exhibit heightened stability to support memory guided behavior.

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Activity of hippocampal formation neurons in the monkey related to a conditional spatial response task

Journal of Neurophysiology ◽

10.1152/jn.1989.61.3.669 ◽

1989 ◽

Vol 61 (3) ◽

pp. 669-678 ◽

Cited By ~ 62

Author(s):

Y. Miyashita ◽

E. T. Rolls ◽

P. M. Cahusac ◽

H. Niki ◽

J. D. Feigenbaum

Keyword(s):

Visual Stimulus ◽

Rhesus Monkeys ◽

Hippocampal Neurons ◽

Hippocampal Formation ◽

Visual Stimuli ◽

Video Monitor ◽

Stimulus Response ◽

Spatial Response ◽

The Mean ◽

Response Task

To analyze neurophysiologically the functions of the primate hippocampus, the activity of 905 single hippocampal formation neurons was analyzed in two rhesus monkeys performing a conditional spatial response task known to be impaired in monkeys and in man by damage to the hippocampus or fornix. In the task, the monkey learned to make one spatial response, touching a screen three times when he saw one visual stimulus on the video monitor, and a different spatial response, of withdrawing his hand from the screen, when a different visual stimulus was shown. Fourteen percent of the neurons fired differentially to one or the other of the stimulus-spatial response associations. The mean latency of these differential responses was 154 +/- 44 (SD) ms. The firing of these neurons was shown to reflect a combination of the particular stimulus and the particular response associated by learning in the stimulus-response association task and could not be accounted for by the motor requirements of the task, nor wholly the stimulus aspects of the task, as demonstrated by testing their firing in related visual discrimination tasks. Responsive neurons were found throughout the hippocampal formation, but were particularly concentrated in the subicular complex and the CA3 subfield. These results show that single hippocampal neurons respond to combinations of the visual stimuli and the spatial responses with which they must become associated in conditional spatial response tasks and are consistent with the suggestion that part of the mechanism of this learning involves associations between visual stimuli and spatial responses learned by single hippocampal neurons.

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Spatial-specificity of single-units in the hippocampal formation of freely moving homing pigeons

Hippocampus ◽

10.1002/hipo.20025 ◽

2005 ◽

Vol 15 (1) ◽

pp. 26-40 ◽

Cited By ~ 34

Author(s):

Jennifer J. Siegel ◽

Douglas Nitz ◽

Verner P. Bingman

Keyword(s):

Hippocampal Formation ◽

Single Units ◽

Homing Pigeons ◽

Freely Moving ◽

Spatial Specificity

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An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex

Journal of Neurophysiology ◽

10.1152/jn.1987.58.6.1233 ◽

1987 ◽

Vol 58 (6) ◽

pp. 1233-1258 ◽

Cited By ~ 977

Author(s):

J. P. Jones ◽

L. A. Palmer

Keyword(s):

Gabor Filter ◽

Spectral Response ◽

Receptive Fields ◽

Gabor Filters ◽

Simple Cell ◽

Two Dimensional ◽

Gabor Function ◽

Spatial Response ◽

Response Profile ◽

Response Profiles

1. Using the two-dimensional (2D) spatial and spectral response profiles described in the previous two reports, we test Daugman's generalization of Marcelja's hypothesis that simple receptive fields belong to a class of linear spatial filters analogous to those described by Gabor and referred to here as 2D Gabor filters. 2. In the space domain, we found 2D Gabor filters that fit the 2D spatial response profile of each simple cell in the least-squared error sense (with a simplex algorithm), and we show that the residual error is devoid of spatial structure and statistically indistinguishable from random error. 3. Although a rigorous statistical approach was not possible with our spectral data, we also found a Gabor function that fit the 2D spectral response profile of each simple cell and observed that the residual errors are everywhere small and unstructured. 4. As an assay of spatial linearity in two dimensions, on which the applicability of Gabor theory is dependent, we compare the filter parameters estimated from the independent 2D spatial and spectral measurements described above. Estimates of most parameters from the two domains are highly correlated, indicating that assumptions about spatial linearity are valid. 5. Finally, we show that the functional form of the 2D Gabor filter provides a concise mathematical expression, which incorporates the important spatial characteristics of simple receptive fields demonstrated in the previous two reports. Prominent here are 1) Cartesian separable spatial response profiles, 2) spatial receptive fields with staggered subregion placement, 3) Cartesian separable spectral response profiles, 4) spectral response profiles with axes of symmetry not including the origin, and 5) the uniform distribution of spatial phase angles. 6. We conclude that the Gabor function provides a useful and reasonably accurate description of most spatial aspects of simple receptive fields. Thus it seems that an optimal strategy has evolved for sampling images simultaneously in the 2D spatial and spatial frequency domains.

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