A diversity of interneurons and Hebbian plasticity facilitate rapid compressible learning in the hippocampus

Nicola, Wilten; Clopath, Claudia

doi:10.1038/s41593-019-0415-2

Cited by 60 publications

(66 citation statements)

References 77 publications

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“…Other studies have suggested that phase precession during spatial navigation could originate from a dual oscillator process (39, 50). Along this line, a recent model of the hippocampus uses the interference between two oscillators to model the neural dynamics related to spatial navigation (51). Although this model shares similarities with ours, a fundamental difference is that our model uses the phase of combined oscillators to create a unique input at every time-step of a task, whereas their model relies on the beat of the combined frequencies.…”

Section: Discussionmentioning

confidence: 99%

Learning long temporal sequences in spiking networks by multiplexing neural oscillations

Vincent-Lamarre

Calderini

Thivierge

2019

Preprint

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Many cognitive and behavioral tasks -such as interval timing, spatial navigation, motor control and speech -require the execution of preciselytimed sequences of neural activation that cannot be fully explained by a succession of external stimuli. We use a reservoir computing framework to explain how such neural sequences can be generated and employed in temporal tasks. We propose a general solution for recurrent neural networks to autonomously produce rich patterns of activity by providing a multi-periodic oscillatory signal as input. We show that the model accurately learns a variety of tasks, including speech generation, motor control and spatial navigation. Further, the model performs temporal rescaling of natural spoken words and exhibits sequential neural activity commonly found in experimental data involving temporal processing. In the context of spatial navigation, the model learns and replays compressed sequences of place cells and captures features of neural activity such as the emergence of ripples and theta phase precession. Together, our findings suggest that combining oscillatory neuronal inputs with different frequencies provides a key mechanism to generate precisely timed sequences of activity in recurrent circuits of the brain. 1 2 3 4 5 6 7 8 9 10 Reservoir computing | Neural oscillations | Temporal processing | Balanced networks 1. Introduction 1 Virtually every aspect of sensory, cognitive and motor processing in biological organisms involves operations unfolding in time 2(1). In the brain, neuronal circuits must represent time on a variety of scales, from milliseconds to minutes and longer circadian 3 rhythms (2). Despite increasingly sophisticated models of brain activity, time representation remains a challenging problem in 4 computational modelling (3, 4). 5 Recurrent neural networks offer a promising avenue to detect and produce precisely timed sequences of activity (5). However, 6 it is challenging to train these networks due to their complexity (6), particularly when operating in a chaotic regime associated 7 with biological neural networks (7, 8). 8 One avenue to address this issue is with the use of reservoir computing (RC) (9, 10). Under this framework, a recurrent 9 network (the reservoir) projects onto a read-out layer whose synaptic weights are adjusted to produce a desired response. 10 However, while RC can capture some behavioral and cognitive processes (11)(12)(13), it often relies on biologically implausible 11 mechanisms (14). Further, current RC implementations offer little insight to understand how the brain generates activity that 12 does not follow a strict rhythmic pattern (1, 5). That is because RC models are either restricted to learning periodic functions, 13 or require an aperiodic input to generate an aperiodic output, thus leaving the neural origins of aperiodic activity unresolved 14 (5). A solution to this problem is to train the recurrent connections of the reservoir to stabilize innate patterns of activity (12), 15 but this approach is more com...

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Section: Discussionmentioning

confidence: 99%

Learning long temporal sequences in spiking networks by multiplexing neural oscillations

Vincent-Lamarre

Calderini

Thivierge

2019

Preprint

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“…during the 100 ms step and by ν Xµ (t) = ν Xbg (11) during only background activity. Parameter α Xν is a scalar that sets the ratio of excitatory and inhibitory firing rate.…”

Section: Methods Of Complementary Inhibitory Weight Profiles Emerge Fmentioning

confidence: 99%

“…I nhibitory neurons exhibit large variability in morphology, connectivity motifs, and electrophysiological properties [1][2][3][4] . Inhibition often balances excitatory inputs, thus stabilising neuronal network activity 5,6 and allowing for a range of different functions [7][8][9][10][11] . When both inhibitory and excitatory inputs share the same statistics and their weight profiles are similar 12 , the resulting state of the postsynaptic neuron is one of precise balance of input currents 9 .…”

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confidence: 99%

“…When both inhibitory and excitatory inputs share the same statistics and their weight profiles are similar 12 , the resulting state of the postsynaptic neuron is one of precise balance of input currents 9 . Modulation of inhibition, e.g., a decrease or increase in local inhibitory activity and, consequently, a change in the balance between excitation and inhibition, can control the activity of neuronal groups 13,14 , and it is believed that disinhibition is an important mechanism for the implementation of high-level brain functions, such as attention 14,15 , memory retrieval 6,16,17 , signal gating 18,19 , and rapid learning 11 .…”

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confidence: 99%

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Complementary inhibitory weight profiles emerge from plasticity and allow attentional switching of receptive fields

Agnes

Luppi

Vogels

2019

Preprint

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Cortical areas comprise multiple types of inhibitory interneurons with stereotypical connectivity motifs, but their combined effect on postsynaptic dynamics has been largely unexplored. Here, we analyse the response of a single postsynaptic model neuron receiving tuned excitatory connections alongside inhibition from two plastic populations. Depending on the inhibitory plasticity rule, synapses remain unspecific (flat), become anti-correlated to, or mirror excitatory synapses. Crucially, the neuron's receptive field, i.e., its response to presynaptic stimuli, depends on the modulatory state of inhibition. When both inhibitory populations are active, inhibition balances excitation, resulting in uncorrelated postsynaptic responses regardless of the inhibitory tuning profiles. Modulating the activity of a given inhibitory population produces strong correlations to either preferred or non-preferred inputs, in line with recent experimental findings showing dramatic context-dependent changes of neurons' receptive fields. We thus confirm that a neuron's receptive field doesn't follow directly from the weight profiles of its presynaptic afferents.Agnes, Luppi, Vogels (2019)

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“…Touretzky and Redish 1996;Samsonovich and McNaughton 1997;Cutsuridis and Hasselmo 2012;Saravanan et al 2015;Erdem et al 2015;Stachenfeld et al 2017), however, there is still ongoing debate particularly on the mechanisms (e.g. Foster 2017; Liu et al 2018b;Matheus Gauy et al 2018;Nicola and Clopath 2019) and the functional role of temporal activity patterns (Liu et al 2018a). In contrast to technical systems, where autonomous localization methods are based on a costly collection of extensive series of sensory snapshots (Davison et al 2007;Milford and Wyeth 2012;Siam and Zhang 2017), the prevalent idea in neuroscience is that, in mammals, the hippocampal space code arises from efficient internal dynamics that is locked to places of salient sensory features (Keinath et al 2018) thereby saving the synaptic resources for explicitly memorizing all details of place-specific sensory inputs.…”

Section: Introductionmentioning

confidence: 99%

A Model for Navigation in Unknown Environments Based on a Reservoir of Hippocampal Sequences

Leibold

2019

Preprint

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Hippocampal place cell populations are activated in sequences on multiple time scales during active behavior, resting and sleep states, suggesting that these sequences are the genuine dynamical motifs of the hippocampal circuit. Recently, prewired hippocampal place cell sequences have even been reported to correlate to future behaviors, but so far there is no explanation of what could be the computational benefits of such a mapping between intrinsic dynamical structure and external sensory inputs. Here, I propose a computational model in which a set of predefined internal sequences is used as a dynamical reservoir to construct a spatial map of a large unknown maze based on only a small number of salient landmarks. The model is based on a new variant of temporal difference learning and implements a simultaneous localization and mapping algorithm. As a result sequences during intermittent replay periods can be decoded as spatial trajectories and improve navigation performance, which supports the functional interpretation of replay to consolidate memories of motor actions.

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A diversity of interneurons and Hebbian plasticity facilitate rapid compressible learning in the hippocampus

Cited by 60 publications

References 77 publications

Learning long temporal sequences in spiking networks by multiplexing neural oscillations

Learning long temporal sequences in spiking networks by multiplexing neural oscillations

Complementary inhibitory weight profiles emerge from plasticity and allow attentional switching of receptive fields

A Model for Navigation in Unknown Environments Based on a Reservoir of Hippocampal Sequences

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