Model of the Hippocampal Learning of Spatio-temporal Sequences

International audienceWe propose a model of the hippocampus aimed at learning the timed association between subsequent sensory events. The properties of the neural network allow it to learn and predict the evolution of con- tinuous rate-coded signals as well as the occurrence of transitory events, using both spatial and non-spatial information. The system is able to provide predictions based on the time trace of past sensory events. Per- formance of the neural network in the precise temporal learning of spatial and non-spatial signals is tested in a simulated experiment. The ability of the hippocampus proper to predict the occurrence of upcoming spatio- temporal events could play a crucial role in the carrying out of tasks requiring accurate time estimation and spatial localization

Neural Avalanches at the Critical Point between Replay and Non-Replay of Spatiotemporal Patterns

We model spontaneous cortical activity with a network of coupled spiking units, in which multiple spatio-temporal patterns are stored as dynamical attractors. We introduce an order parameter, which measures the overlap (similarity) between the activity of the network and the stored patterns. We find that, depending on the excitability of the network, different working regimes are possible. For high excitability, the dynamical attractors are stable, and a collective activity that replays one of the stored patterns emerges spontaneously, while for low excitability, no replay is induced. Between these two regimes, there is a critical region in which the dynamical attractors are unstable, and intermittent short replays are induced by noise. At the critical spiking threshold, the order parameter goes from zero to one, and its fluctuations are maximized, as expected for a phase transition (and as observed in recent experimental results in the brain). Notably, in this critical region, the avalanche size and duration distributions follow power laws. Critical exponents are consistent with a scaling relationship observed recently in neural avalanches measurements. In conclusion, our simple model suggests that avalanche power laws in cortical spontaneous activity may be the effect of a network at the critical point between the replay and non-replay of spatio-temporal patterns

Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity

We study the storage and retrieval of phase-coded patterns as stable dynamical attractors in recurrent neural networks, for both an analog and a integrate-and-fire spiking model. The synaptic strength is determined by a learning rule based on spike-time-dependent plasticity, with an asymmetric time window depending on the relative timing between pre- and post-synaptic activity. We store multiple patterns and study the network capacity. For the analog model, we find that the network capacity scales linearly with the network size, and that both capacity and the oscillation frequency of the retrieval state depend on the asymmetry of the learning time window. In addition to fully-connected networks, we study sparse networks, where each neuron is connected only to a small number z << N of other neurons. Connections can be short range, between neighboring neurons placed on a regular lattice, or long range, between randomly chosen pairs of neurons. We find that a small fraction of long range connections is able to amplify the capacity of the network. This imply that a small-world-network topology is optimal, as a compromise between the cost of long range connections and the capacity increase. Also in the spiking integrate and fire model the crucial result of storing and retrieval of multiple phase-coded patterns is observed. The capacity of the fully-connected spiking network is investigated, together with the relation between oscillation frequency of retrieval state and window asymmetry

Effects on orientation perception of manipulating the spatio–temporal prior probability of stimuli

Spatial and temporal regularities commonly exist in natural visual scenes. The knowledge of the probability structure of these regularities is likely to be informative for an efficient visual system. Here we explored how manipulating the spatio–temporal prior probability of stimuli affects human orientation perception. Stimulus sequences comprised four collinear bars (predictors) which appeared successively towards the foveal region, followed by a target bar with the same or different orientation. Subjects' orientation perception of the foveal target was biased towards the orientation of the predictors when presented in a highly ordered and predictable sequence. The discrimination thresholds were significantly elevated in proportion to increasing prior probabilities of the predictors. Breaking this sequence, by randomising presentation order or presentation duration, decreased the thresholds. These psychophysical observations are consistent with a Bayesian model, suggesting that a predictable spatio–temporal stimulus structure and an increased probability of collinear trials are associated with the increasing prior expectation of collinear events. Our results suggest that statistical spatio–temporal stimulus regularities are effectively integrated by human visual cortex over a range of spatial and temporal positions, thereby systematically affecting perception

A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP

Humans perform remarkably well in many cognitive tasks including pattern recognition. However, the neuronal mechanisms underlying this process are not well understood. Nevertheless, artificial neural networks, inspired in brain circuits, have been designed and used to tackle spatio-temporal pattern recognition tasks. In this paper we present a multineuronal spike pattern detection structure able to autonomously implement online learning and recognition of parallel spike sequences (i.e., sequences of pulses belonging to different neurons/neural ensembles). The operating principle of this structure is based on two spiking/synaptic neurocomputational characteristics: spike latency, that enables neurons to fire spikes with a certain delay and heterosynaptic plasticity, that allows the own regulation of synaptic weights. From the perspective of the information representation, the structure allows mapping a spatio-temporal stimulus into a multidimensional, temporal, feature space. In this space, the parameter coordinate and the time at which a neuron fires represent one specific feature. In this sense, each feature can be considered to span a single temporal axis. We applied our proposed scheme to experimental data obtained from a motor inhibitory cognitive task. The test exhibits good classification performance, indicating the adequateness of our approach. In addition to its effectiveness, its simplicity and low computational cost suggest a large scale implementation for real time recognition applications in several areas, such as brain computer interface, personal biometrics authentication or early detection of diseases.Comment: Submitted to Frontiers in Neuroscienc

Songbird organotypic culture as an in vitro model for interrogating sparse sequencing networks

Sparse sequences of neuronal activity are fundamental features of neural circuit computation; however, the underlying homeostatic mechanisms remain poorly understood. To approach these questions, we have developed a method for cellular-resolution imaging in organotypic cultures of the adult zebra finch brain, including portions of the intact song circuit. These in vitro networks can survive for weeks, and display mature neuron morphologies. Neurons within the organotypic slices exhibit a diversity of spontaneous and pharmacologically induced activity that can be easily monitored using the genetically encoded calcium indicator GCaMP6. In this study, we primarily focus on the classic song sequence generator HVC and the surrounding areas. We describe proof of concept experiments including physiological, optical, and pharmacological manipulation of these exposed networks. This method may allow the cellular rules underlying sparse, stereotyped neural sequencing to be examined with new degrees of experimental control

Spatio-Temporal and Multisensory Integration: the relationship between sleep and the cerebellum

Does the cerebellum sleep? If so, does sleep contribute to cerebellar cognition? In this thesis, the sleep contribution to the consolidation process of spatial-temporal and multisensory integration was investigated in relation to the human cerebellum. Multiple experimental approaches were used to answer research questions addressed in the various chapters. Summarizing the evidence of the electrophysiology and neuroimaging studies, in Chapter1 we present intriguing evidence that the cerebellum is involved in sleep physiology, and that cerebellar-dependent memory formation can be consolidated during sleep. In Chapter 2, using functional neuroimaging in healthy participants during various forms of the Serial interception sequential learning (SISL) task, i.e., predictive timing, motor coordination, and motor imagination, we assessed the cerebellar involvement in spatio-temporal predictive timing; and possible cerebellar interactions with other regions, most notably the hippocampus. In Chapter 3, we add to the findings of Chapter 2 that indicate the cerebellum and hippocampus are involved in the task, by showing that more than simply activated, the cerebellum is a necessary and responsible region for the establishment of the spatio-temporal prediction. This follows from the deficits in behavioral properties of the predictive and reactive timing in the cerebellar ataxia type 6 patients, using the modified version of the SISL task. In Chapter 4, we assessed the subsequent post-interval behavioral performances on the learning of the fixed and random timing sequences in the SISL task, comparing a sleep group and wake group in healthy participants. Our findings show that sleep consolidates the process of cerebellar-dependent spatio-temporal integration. In Chapter 5, we investigated the establishment of visual-tactile integration during sleep through the examination of tactile motion stimulation during sleep and showed that, subsequent to sleep, directional visual motion discrimination i