Temporal Dynamics of Decision-Making during Motion Perception in the Visual Cortex

How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical "decision neurons." A biophysically realistic model of interactions within and between Retina/LGN and cortical areas V1, MT, MST, and LIP, gated by basal ganglia, simulates dynamic properties of decision-making in response to ambiguous visual motion stimuli used by Newsome, Shadlen, and colleagues in their neurophysiological experiments. The model clarifies how brain circuits that solve the aperture problem interact with a recurrent competitive network with self-normalizing choice properties to carry out probablistic decisions in real time. Some scientists claim that perception and decision-making can be described using Bayesian inference or related general statistical ideas, that estimate the optimal interpretation of the stimulus given priors and likelihoods. However, such concepts do not propose the neocortical mechanisms that enable perception, and make decisions. The present model explains behavioral and neurophysiological decision-making data without an appeal to Bayesian concepts and, unlike other existing models of these data, generates perceptual representations and choice dynamics in response to the experimental visual stimuli. Quantitative model simulations include the time course of LIP neuronal dynamics, as well as behavioral accuracy and reaction time properties, during both correct and error trials at different levels of input ambiguity in both fixed duration and reaction time tasks. Model MT/MST interactions compute the global direction of random dot motion stimuli, while model LIP computes the stochastic perceptual decision that leads to a saccadic eye movement.National Science Foundation (SBE-0354378, IIS-02-05271); Office of Naval Research (N00014-01-1-0624); National Institutes of Health (R01-DC-02852

Task-Irrelevant Perceptual Learning Specific to the Contrast Polarity of Motion Stimuli

Studies of perceptual learning have focused on aspects of learning that are related to early stages of sensory processing. However, conclusions that perceptual learning results in low-level sensory plasticity are of great controversy, largely because such learning can often be attributed to plasticity in later stages of sensory processing or in the decision processes. To address this controversy, we developed a novel random dot motion (RDM) stimulus to target motion cells selective to contrast polarity, by ensuring the motion direction information arises only from signal dot onsets and not their offsets, and used these stimuli in conjunction with the paradigm of task-irrelevant perceptual learning (TIPL). In TIPL, learning is achieved in response to a stimulus by subliminally pairing that stimulus with the targets of an unrelated training task. In this manner, we are able to probe learning for an aspect of motion processing thought to be a function of directional V1 simple cells with a learning procedure that dissociates the learned stimulus from the decision processes relevant to the training task. Our results show learning for the exposed contrast polarity and that this learning does not transfer to the unexposed contrast polarity. These results suggest that TIPL for motion stimuli may occur at the stage of directional V1 simple cells.CELEST, an NSF Science of Learning Center (SBE-0354378); Defense Advanced Research Projects Agency SyNAPSE program (HR0011-09-3-0001, HR001-09-C-0011); National Science Foundation (BCS-0549036); National Institutes of Health (R21 EY017737

Concept-modulated model-based offline reinforcement learning for rapid generalization

The robustness of any machine learning solution is fundamentally bound by the data it was trained on. One way to generalize beyond the original training is through human-informed augmentation of the original dataset; however, it is impossible to specify all possible failure cases that can occur during deployment. To address this limitation we combine model-based reinforcement learning and model-interpretability methods to propose a solution that self-generates simulated scenarios constrained by environmental concepts and dynamics learned in an unsupervised manner. In particular, an internal model of the agent's environment is conditioned on low-dimensional concept representations of the input space that are sensitive to the agent's actions. We demonstrate this method within a standard realistic driving simulator in a simple point-to-point navigation task, where we show dramatic improvements in one-shot generalization to different instances of specified failure cases as well as zero-shot generalization to similar variations compared to model-based and model-free approaches

Neuromodulated attention and goal-driven perception in uncertain domains.

In uncertain domains, the goals are often unknown and need to be predicted by the organism or system. In this paper, contrastive Excitation Backprop (c-EB) was used in two goal-driven perception tasks - one with pairs of noisy MNIST digits and the other with a robot in an action-based attention scenario. The first task included attending to even, odd, low, and high digits, whereas the second task included action goals, such as "eat", "work-on-computer", "read", and "say-hi" that led to attention to objects associated with those actions. The system needed to increase attention to target items and decrease attention to distractor items and background noise. Because the valid goal was unknown, an online learning model based on the cholinergic and noradrenergic neuromodulatory systems was used to predict a noisy goal (expected uncertainty) and re-adapt when the goal changed (unexpected uncertainty). This neurobiologically plausible model demonstrates how neuromodulatory systems can predict goals in uncertain domains and how attentional mechanisms can enhance the perception for that goal

Modeling Contextual Modulation of Memory Associations in the Hippocampus

We present a computational model of how memories can be contextually acquired and recalled in the hippocampus. Our adaptive contextual memory model comprises the lateral entorhinal cortex (LEC), the dentate gyrus (DG) and areas CA3 and CA1 in the hippocampus, and assumes external inputs about context that originate in the prefrontal cortex (PFC). Specifically, we propose that there is a top-down bias on the excitability of cells in the DG of the hippocampus that recruits a sub-population of cells to differentiate contexts, independent of experienced stimuli, expanding the “pattern separation” role typically attributed to the DG. It has been demonstrated in rats that if PFC is inactivated, both acquisition and recall of memory associations are impaired. However, PFC inactivation during acquisition of one set of memory associations surprisingly leads to subsequent facilitation of the acquisition of a conflicting set of memory associations in the same context under normal PFC operation. We provide here the first computational and algorithmic account of how the absence or presence of the top-down contextual biases on the excitability of DG cells during different learning phases of these experiments explains these data. Our model simulates PFC inactivation as the loss of inhibitory control on DG, which leads to full or partial activation of DG cells related to conflicting memory associations previously acquired in different contexts. This causes context-inappropriate memory traces to become active in the CA3 recurrent network and thereby the output CA1 area within the hippocampus. We show that these incongruous memory patterns proactively interfere with and slow the acquisition of new memory associations. Further, we demonstrate that pattern completion within CA3 in response to a partial cue for the recall of previously acquired memories is also impaired by PFC inactivation for the same reason. Pre-training the model with interfering memories in contexts different from those used in the experiments, simulating a lifetime of experiences, was crucial to reproduce the rat behavioral data. Finally, we made several testable predictions based on the model that suggest future experiments to deepen our understanding of brain-wide memory processes

Context Meta-Reinforcement Learning via Neuromodulation

Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt quickly to tasks from few samples in dynamic environments. Such a feat is achieved through dynamic representations in an agent's policy network (obtained via reasoning about task context, model parameter updates, or both). However, obtaining rich dynamic representations for fast adaptation beyond simple benchmark problems is challenging due to the burden placed on the policy network to accommodate different policies. This paper addresses the challenge by introducing neuromodulation as a modular component to augment a standard policy network that regulates neuronal activities in order to produce efficient dynamic representations for task adaptation. The proposed extension to the policy network is evaluated across multiple discrete and continuous control environments of increasing complexity. To prove the generality and benefits of the extension in meta-RL, the neuromodulated network was applied to two state-of-the-art meta-RL algorithms (CAVIA and PEARL). The result demonstrates that meta-RL augmented with neuromodulation produces significantly better result and richer dynamic representations in comparison to the baselines

Sliced Cramer synaptic consolidation for preserving deeply learned representations

Deep neural networks suffer from the inability to preserve the learned data representation (i.e., catastrophic forgetting) in domains where the input data distribution is non-stationary, and it changes during training. Various selective synaptic plasticity approaches have been recently proposed to preserve network parameters, which are crucial for previously learned tasks while learning new tasks. We explore such selective synaptic plasticity approaches through a unifying lens of memory replay and show the close relationship between methods like Elastic Weight Consolidation (EWC) and Memory-Aware-Synapses (MAS). We then propose a fundamentally different class of preservation methods that aim at preserving the distribution of the network’s output at an arbitrary layer for previous tasks while learning a new one. We propose the sliced Cramer distance as a suitable ´ choice for such preservation and evaluate our Sliced Cramer Preservation (SCP) ´ algorithm through extensive empirical investigations on various network architectures in both supervised and unsupervised learning settings. We show that SCP consistently utilizes the learning capacity of the network better than online-EWC and MAS methods on various incremental learning tasks

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.Comment: To appear in Neural Network

Dose-Dependent Effects of Closed-Loop tACS Delivered During Slow-Wave Oscillations on Memory Consolidation

Sleep is critically important to consolidate information learned throughout the day. Slow-wave sleep (SWS) serves to consolidate declarative memories, a process previously modulated with open-loop non-invasive electrical stimulation, though not always effectively. These failures to replicate could be explained by the fact that stimulation has only been performed in open-loop, as opposed to closed-loop where phase and frequency of the endogenous slow-wave oscillations (SWOs) are matched for optimal timing. The current study investigated the effects of closed-loop transcranial Alternating Current Stimulation (tACS) targeting SWOs during sleep on memory consolidation. 21 participants took part in a three-night, counterbalanced, randomized, single-blind, within-subjects study, investigating performance changes (correct rate and F1 score) on images in a target detection task over 24 h. During sleep, 1.5 mA closed-loop tACS was delivered in phase over electrodes at F3 and F4 and 180° out of phase over electrodes at bilateral mastoids at the frequency (range 0.5–1.2 Hz) and phase of ongoing SWOs for a duration of 5 cycles in each discrete event throughout the night. Data were analyzed in a repeated measures ANOVA framework, and results show that verum stimulation improved post-sleep performance specifically on generalized versions of images used in training at both morning and afternoon tests compared to sham, suggesting the facilitation of schematization of information, but not of rote, veridical recall. We also found a surprising inverted U-shaped dose effect of sleep tACS, which is interpreted in terms of tACS-induced faciliatory and subsequent refractory dynamics of SWO power in scalp EEG. This is the first study showing a selective modulation of long-term memory generalization using a novel closed-loop tACS approach, which holds great potential for both healthy and neuropsychiatric populations