Independent Theta Phase Coding Accounts for CA1 Population Sequences and Enables Flexible Remapping

Chadwick, Angus; Rossum, Mark C. W. van; Nolan, Matthew F.

doi:10.1101/005066

Cited by 20 publications

(30 citation statements)

References 48 publications

(104 reference statements)

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“…To study the impact of synaptic clustering under in vivo-like conditions in the biophysical models we carefully matched the inputs of the models to the synaptic inputs experienced by these neurons in vivo. Inputs, corresponding to synaptic release events, were generated by a Generalised Linear Model (GLM) tuned to a number of different external and internal features (Pillow, 2007;Chadwick et al, 2015). Specifically, the presynaptic input count at time t for synapse i were generated from a Poisson process with rate λ i t :…”

Section: Inputsmentioning

confidence: 99%

Impact of functional synapse clusters on neuronal response selectivity

Ujfalussy

Makara

2019

Preprint

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Clustering of functionally similar synapses in dendrites is thought to affect input-output transformation by inducing dendritic nonlinearities. However, neither the in vivo impact of synaptic clusters on somatic membrane potential (sVm), nor the rules of cluster formation are elucidated. We developed a computational approach to measure the effect of functional synaptic clusters on sVm response of biophysical model CA1 and L2/3 pyramidal neurons to behaviorally relevant in vivo-like inputs. Large-scale dendritic spatial inhomogeneities in synaptic tuning properties did influence sVm, but small synaptic clusters appearing randomly with unstructured connectivity did not. With structured connectivity, ∼10-20 synapses per cluster was optimal for clustering-based tuning, but larger responses were achieved by 2-fold potentiation of the same synapses. We further show that without nonlinear amplification of the effect of random clusters, action potential-based, global plasticity rules can not generate functional clustering. Our results suggest that clusters likely form via local synaptic interactions, and have to be moderately large to impact sVm responses.

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Section: Inputsmentioning

confidence: 99%

Impact of functional synapse clusters on neuronal response selectivity

Ujfalussy

Makara

2019

Preprint

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“…Statistical models have shown that the precise trial-to-trial timing can be predicted by the spiking activity of other neurons, as would be expected if sequencing was brought about through a chaining of co-active ensembles oscillating faster than the baseline LFP (Harris et al 2002;Dragoi and Buzsáki 2006;Geisler et al 2007). However, principal cells respond to many environment stimuli and therefore a misspecification of the model may mistake common external modulation for a causal network interaction (Chadwick et al 2015). Therefore further experimentation is needed to resolve whether theta sequences truly reflect network level synchronization.…”

Section: The Hippocampus Organizes the Spatial Code Into Temporal Seqmentioning

confidence: 99%

Hippocampal Mechanisms for the Segmentation of Space by Goals and Boundaries

McKenzie

Buzsáki

2016

Research and Perspectives in Neurosciences

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In memory, the continuous flow of experience is punctuated at meaningful boundaries between one episode and the next. When salient events are separated by increasing amounts of space or time, memory systems can accommodate in two ways. One option is to increase the amount of neural resources devoted to longer event segments. The other is to maintain the same neural resources with sacrificed spatiotemporal resolution. Here we review how the spatial coding system is affected by the segmentation of space by goals and boundaries. We argue that the resolution of the place code is dictated by the amount of space encoded within periods of theta. Thus, the theta cycle is viewed as a 'neural word' that segregates segments of space and its cognitive equivalents (memory, planning). In support of this conclusion, we report that, as rats traverse a linear track, the beginning of a journey is represented at the falling phase of theta whereas the journey's end is represented on the ascending phase. The current location is represented in the temporal context of the past and future event boundaries. These results are discussed in relation to the changes in physiology observed across the longitudinal axis of the hippocampus, with a special consideration for how sequence information could be integrated by downstream 'reader' neurons.

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“…The analysis of tree branching provides an estimate of the statistical dependencies between spike trains, independent of the underlying type of interaction and without assuming a particular transfer function for the target neuron [Harris et al, 2003]. The nature of neuronal coordination as observed from spike trains is still debated, for example in the hippocampus [Chadwick et al, 2015], and unbiased, model-free methods may be highly informative on the nature of the actual statistical dependencies between neurons.…”

Section: Discussionmentioning

confidence: 99%

Brain-state invariant thalamo-cortical coordination revealed by non-linear encoders

Viejo

Cortier

Peyrache

2017

Preprint

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Understanding how neurons cooperate to integrate sensory inputs and guide behavior is a fundamental problem in neuroscience. A large body of methods have been developed to study neuronal firing at the single cell and population levels, generally seeking interpretability as well as predictivity. However, these methods are usually confronted with the lack of groundtruth necessary to validate the approach. Here, using neuronal data from the head-direction (HD) system, we present evidence demonstrating how gradient boosted trees, a non-linear and supervised Machine Learning tool, can learn the relationship between behavioral parameters and neuronal responses with high accuracy by optimizing the information rate. Interestingly, and unlike other classes of Machine Learning methods, the intrinsic structure of the trees can be interpreted in relation to behavior (e.g. to recover the tuning curves) or to study how neurons cooperate with their peers in the network. We show how the method, unlike linear analysis, reveals that the coordination in thalamo-cortical circuits is qualitatively the same during wakefulness and sleep, indicating a brain-state independent feed-forward circuit. Machine Learning tools thus open new avenues for benchmarking model-based characterization of spike trains. Author summaryThe thalamus is a brain structure that relays sensory information to the cortex and mediates cortico-cortical interaction. Unraveling the dialogue between the thalamus and the cortex is thus a central question in neuroscience, with direct implications on our understanding of how the brain operates at the macro scale and of the neuronal basis of brain disorders that possibly result from impaired thalamo-cortical networks, such as absent epilepsy and schizophrenia. Methods that are classically used to study the coordination between neuronal populations are usually sensitive to the ongoing global dynamics of the networks, in particular desynchronized (wakefulness and REM sleep) and synchronized (non-REM sleep) states. They thus fail to capture the underlying temporal coordination. By analyzing recordings of thalamic and cortical neuronal populations of the HD system in freely moving mice during exploration and sleep, we show how a general non-linear encoder captures a brain-state independent temporal coordination where the thalamic neurons leading their cortical targets by 20-50ms in all brain states. This study thus demonstrates how methods that do not assume any models of neuronal activity may be used to reveal important aspects of neuronal dynamics and coordination between brain regions. *

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Independent Theta Phase Coding Accounts for CA1 Population Sequences and Enables Flexible Remapping

Cited by 20 publications

References 48 publications

Impact of functional synapse clusters on neuronal response selectivity

Impact of functional synapse clusters on neuronal response selectivity

Hippocampal Mechanisms for the Segmentation of Space by Goals and Boundaries

Brain-state invariant thalamo-cortical coordination revealed by non-linear encoders

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