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

Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks

We study the collective dynamics of a Leaky Integrate and Fire network in which precise relative phase relationship of spikes among neurons are stored, as attractors of the dynamics, and selectively replayed at differentctime scales. Using an STDP-based learning process, we store in the connectivity several phase-coded spike patterns, and we find that, depending on the excitability of the network, different working regimes are possible, with transient or persistent replay activity induced by a brief signal. We introduce an order parameter to evaluate the similarity between stored and recalled phase-coded pattern, and measure the storage capacity. Modulation of spiking thresholds during replay changes the frequency of the collective oscillation or the number of spikes per cycle, keeping preserved the phases relationship. This allows a coding scheme in which phase, rate and frequency are dissociable. Robustness with respect to noise and heterogeneity of neurons parameters is studied, showing that, since dynamics is a retrieval process, neurons preserve stablecprecise phase relationship among units, keeping a unique frequency of oscillation, even in noisy conditions and with heterogeneity of internal parameters of the units

IST Austria Thesis

The solving of complex tasks requires the functions of more than one brain area and their interaction. Whilst spatial navigation and memory is dependent on the hippocampus, flexible behavior relies on the medial prefrontal cortex (mPFC). To further examine the roles of the hippocampus and mPFC, we recorded their neural activity during a task that depends on both of these brain regions. With tetrodes, we recorded the extracellular activity of dorsal hippocampal CA1 (HPC) and mPFC neurons in Long-Evans rats performing a rule-switching task on the plus-maze. The plus-maze task had a spatial component since it required navigation along one of the two start arms and at the maze center a choice between one of the two goal arms. Which goal contained a reward depended on the rule currently in place. After an uncued rule change the animal had to abandon the old strategy and switch to the new rule, testing cognitive flexibility. Investigating the coordination of activity between the HPC and mPFC allows determination during which task stages their interaction is required. Additionally, comparing neural activity patterns in these two brain regions allows delineation of the specialized functions of the HPC and mPFC in this task. We analyzed neural activity in the HPC and mPFC in terms of oscillatory interactions, rule coding and replay. We found that theta coherence between the HPC and mPFC is increased at the center and goals of the maze, both when the rule was stable or has changed. Similar results were found for locking of HPC and mPFC neurons to HPC theta oscillations. However, no differences in HPC-mPFC theta coordination were observed between the spatially- and cue-guided rule. Phase locking of HPC and mPFC neurons to HPC gamma oscillations was not modulated by maze position or rule type. We found that the HPC coded for the two different rules with cofiring relationships between cell pairs. However, we could not find conclusive evidence for rule coding in the mPFC. Spatially-selective firing in the mPFC generalized between the two start and two goal arms. With Bayesian positional decoding, we found that the mPFC reactivated non-local positions during awake immobility periods. Replay of these non-local positions could represent entire behavioral trajectories resembling trajectory replay of the HPC. Furthermore, mPFC trajectory-replay at the goal positively correlated with rule-switching performance. Finally, HPC and mPFC trajectory replay occurred independently of each other. These results show that the mPFC can replay ordered patterns of activity during awake immobility, possibly underlying its role in flexible behavior

Prior learning of relevant non-aversive information is a boundary condition for avoidance memory reconsolidation in the rat hippocampus

Reactivated memories can be modified during reconsolidation, making this process a potential therapeutic target for post-traumatic stress disorder (PTSD), a mental illness characterized by the recurring avoidance of situations that evoke trauma-related fears. However, avoidance memory reconsolidation depends on a set of still loosely defined boundary conditions, limiting the translational value of basic research. In particular, the involvement of the hippocampus in fear-motivated avoidance memory reconsolidation remains controversial. Combining behavioral and electrophysiological analyses in male Wistar rats, we found that previous learning of relevant non-aversive information is essential to elicit the participation of the hippocampus in avoidance memory reconsolidation, which is associated with an increase in theta and gamma oscillations power and cross-frequency coupling in dorsal CA1 during reactivation of the avoidance response. Our results indicate that the hippocampus is involved in memory reconsolidation only when reactivation results in contradictory representations regarding the consequences of avoidance, and suggest that robust nesting of hippocampal theta-gamma rhythms at the time of retrieval is a specific reconsolidation marker.2018-03-1

Neural oscillations during conditional associative learning.

Associative learning requires mapping between complex stimuli and behavioural responses. When multiple stimuli are involved, conditional associative learning is a gradual process with learning based on trial and error. It is established that a distributed network of regions track associative learning, however the role of neural oscillations in human learning remains less clear. Here we used scalp EEG to test how neural oscillations change during learning of arbitrary visuo-motor associations. Participants learned to associative 48 different abstract shapes to one of four button responses through trial and error over repetitions of the shapes. To quantify how well the associations were learned for each trial, we used a state-space computational model of learning that provided a probability of each trial being correct given past performance for that stimulus, that we take as a measure of the strength of the association. We used linear modelling to relate single-trial neural oscillations to single-trial measures of association strength. We found frontal midline theta oscillations during the delay period tracked learning, where theta activity was strongest during the early stages of learning and declined as the associations were formed. Further, posterior alpha and low-beta oscillations in the cue period showed strong desynchronised activity early in learning, while stronger alpha activity during the delay period was seen as associations became well learned. Moreover, the magnitude of these effects during early learning, before the associations were learned, related to improvements in memory seen on the next presentation of the stimulus. The current study provides clear evidence that frontal theta and posterior alpha/beta oscillations play a key role during associative memory formation

Cholinergic modulation of cognitive processing: insights drawn from computational models

Acetylcholine plays an important role in cognitive function, as shown by pharmacological manipulations that impact working memory, attention, episodic memory, and spatial memory function. Acetylcholine also shows striking modulatory influences on the cellular physiology of hippocampal and cortical neurons. Modeling of neural circuits provides a framework for understanding how the cognitive functions may arise from the influence of acetylcholine on neural and network dynamics. We review the influences of cholinergic manipulations on behavioral performance in working memory, attention, episodic memory, and spatial memory tasks, the physiological effects of acetylcholine on neural and circuit dynamics, and the computational models that provide insight into the functional relationships between the physiology and behavior. Specifically, we discuss the important role of acetylcholine in governing mechanisms of active maintenance in working memory tasks and in regulating network dynamics important for effective processing of stimuli in attention and episodic memory tasks. We also propose that theta rhythm plays a crucial role as an intermediary between the physiological influences of acetylcholine and behavior in episodic and spatial memory tasks. We conclude with a synthesis of the existing modeling work and highlight future directions that are likely to be rewarding given the existing state of the literature for both empiricists and modelers

Prediction and memory: A predictive coding account

The hippocampus is crucial for episodic memory, but it is also involved in online prediction. Evidence suggests that a unitary hippocampal code underlies both episodic memory and predictive processing, yet within a predictive coding framework the hippocampal-neocortical interactions that accompany these two phenomena are distinct and opposing. Namely, during episodic recall, the hippocampus is thought to exert an excitatory influence on the neocortex, to reinstate activity patterns across cortical circuits. This contrasts with empirical and theoretical work on predictive processing, where descending predictions suppress prediction errors to ‘explain away’ ascending inputs via cortical inhibition. In this hypothesis piece, we attempt to dissolve this previously overlooked dialectic. We consider how the hippocampus may facilitate both prediction and memory, respectively, by inhibiting neocortical prediction errors or increasing their gain. We propose that these distinct processing modes depend upon the neuromodulatory gain (or precision) ascribed to prediction error units. Within this framework, memory recall is cast as arising from fictive prediction errors that furnish training signals to optimise generative models of the world, in the absence of sensory data