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Poster 

Poster 

Poster 

Action selection is important for species survival. The basal ganglia, a subcortical structure, has long been thought to play a crucial role in action selection and movement initiation. Classical theories suggest that an important role of the striatum, the input region of the basal ganglia, is to select actions to be performed based on cortical projections carrying action information. However, thanks to recent progress in neural recording techniques, new experimental evidence suggests that the striatum does not perform action selection. Rather, the striatum plays an advisory role. Thus the classical theories of the basal ganglia need to be revisited and revised. As a rst step, in this work we hypothesize a new computational role for the striatum. We present a network-level theory in which the striatum transforms cortical action bids into action evaluations. Based on the region’s neural circuitry, we theorize that the role of the striatum is to transform bids to action values that are normalized, contrast-enhanced, orthogonalized, and encoded as continuous values through the use of two separate neuron populations with bipolar tuning and both feedforward and collateral inhibitory mechanisms. We simulate our network and investigate the role of the network components in its dynamics. Finally, we compare the behavior of our network to previous literature on decision-making behavior in rodents and primates. 

Genetics are recognized as a significant risk factor in schizophrenia [1], and computational modeling studies have highlighted deficits in belief updating as a key aspect of the disorder and an underlying cause of delusion [2]. In particular, the patients often show strong priors on envi- ronmental volatility. However, the intricate mechanisms bridging these genetic risk factors and belief updating deficits remain poorly understood. Our challenge here was to build a biologically plausible neural network that provides a link between genetic risk factors and impaired belief updating. In constructing our schizophrenia model, we first focused on the prefrontal cortex (PFC)- mediodorsal thalamus (MD) circuit, given mounting evidence implicating alterations in these regions in schizophrenia pathology [3]. Drawing from experimental findings demonstrating the involvement of MD neurons expressing D2 receptors in cognitive flexibility [4], the known asso- ciation of D2 receptor genes with heightened schizophrenia risk [5], and the predominant mode of action of antipsychotic treatments as dopamine antagonists at D2 receptors [6], we simulated schizophrenia by reducing the excitability of MD neurons to mimic the hyperactive D2 receptors in Schizophrenia. To investigate the belief updating process, we consider a probability reversal task, in which the reward structure switches in blocks for every 200 trials. Our normal thalamocortical model is capable of flexibly switching across blocks and its PFC-MD connections learn the contextual model of the world, a neural signature for continual learning. We further mathematically analyze the model and deduce that under mild assumptions, the model approximates CUSUM algorithm, an algorithm known for its optimality in detecting environmental changes [7]. On the other hand, our schizophrenia model exhibited a stronger bias towards environmental volatility, prompting exploratory behaviors following contextual switches. By mathematical analysis, we deduce that the decreased excitability makes the evidence accumulation dynamics leaky and therefore the model can sporadically switch, consistent with the qualitative results in Schizophrenia patients [2]. Additionally, decreased excitability in MD compromised the ability of PFC-MD connections to accurately learn the environmental model. To address this impairment, we applied current injections to MD to restore activity levels to a range conducive to Hebbian plasticity. Remarkably, the rescue model demonstrated reduced exploratory behavior following switches and exhibited a higher threshold for MD activity switching, indicative of a diminished bias towards environmental volatility. Moreover, the rescue model exhibited improved learning of the environmental model within its PFC-MD connections. These findings suggest a potential mechanism for utilizing deep brain stimulation at a novel site to mitigate schizophrenia symptoms. 

Deploying nanoscopic particles and robots in the human body promises increasingly selective drug delivery with fewer side effects. We consider the problem of a homogeneous swarm of nanobots locating a singular cancerous region and treating it by releasing some onboard payload of drugs once at the site. At nanoscale, the computation, communication, sensing, and locomotion capabilities of individual agents are extremely limited, noisy, and/or nonexistent. We present a general model to formally describe the individual and collective behaviour of agents in a colloidal environment, such as the bloodstream, for the problem of cancer detection and treatment by nanobots. This includes a feasible and precise model of agent locomotion, which is inspired by actual nanoscopic vesicles which, when in the presence of an external chemical gradient, tend towards areas of higher concentration by means of self-propulsion. The delivered payloads have a dual purpose of treating the cancer, as well as diffusing throughout the space to form a chemical gradient which other agents can sense and noisily ascend. We present simulation results to analyze the behavior of individual agents under our locomotion model and to investigate the efficacy of this collectively amplified chemical signal in helping the larger swarm efficiently locate the cancer site. 

Animals flexibly select actions that maximize future rewards despite facing uncertainty in sen- sory inputs, action-outcome associations or contexts. The computational and circuit mechanisms underlying this ability are poorly understood. A clue to such computations can be found in the neural systems involved in representing sensory features, sensorimotor-outcome associations and contexts. Specifically, the basal ganglia (BG) have been implicated in forming sensorimotor-outcome association [1] while the thalamocortical loop between the prefrontal cortex (PFC) and mediodorsal thalamus (MD) has been shown to engage in contextual representations [2, 3]. Interestingly, both human and non-human animal experiments indicate that the MD represents different forms of uncertainty [3, 4]. However, finding evidence for uncertainty representation gives little insight into how it is utilized to drive behavior. Normative theories have excelled at providing such computational insights. For example, de- ploying traditional machine learning algorithms to fit human decision-making behavior has clarified how associative uncertainty alters exploratory behavior [5, 6]. However, despite their computa- tional insight and ability to fit behaviors, normative models cannot be directly related to neural mechanisms. Therefore, a critical gap exists between what we know about the neural representa- tion of uncertainty on one end and the computational functions uncertainty serves in cognition. This gap can be filled with mechanistic neural models that can approximate normative models as well as generate experimentally observed neural representations. In this work, we build a mechanistic cortico-thalamo-BG loop network model that directly fills this gap. The model includes computationally-relevant mechanistic details of both BG and thalamocortical circuits such as distributional activities of dopamine [7] and thalamocortical pro- jection modulating cortical effective connectivity [3] and plasticity [8] via interneurons. We show that our network can more efficiently and flexibly explore various environments compared to com- monly used machine learning algorithms and we show that the mechanistic features we include are crucial for handling different types of uncertainty in decision-making. Furthermore, through derivation and mathematical proofs, we approximate our models to two novel normative theories. We show mathematically the first has near-optimal performance on bandit tasks. The second is a generalization on the well-known CUMSUM algorithm, which is known to be optimal on single change point detection tasks [9]. Our normative model expands on this by detecting multiple sequential contextual changes. To our knowledge, our work is the first to link computational in- sights, normative models and neural realization together in decision-making under various forms of uncertainty. 

We continue our study from [5], of how concepts that have hierarchical structure might be represented in brain-like neural networks, how these representations might be used to recognize the concepts, and how these representations might be learned. In [5], we considered simple tree-structured concepts and feed-forward layered networks. Here we extend the model in two ways: we allow limited overlap between children of different concepts, and we allow networks to include feedback edges. For these more general cases, we describe and analyze algorithms for recognition and algorithms for learning. 

Decision making in natural settings requires efficient exploration to handle uncertainty. Since associations between actions and outcomes are uncertain, animals need to balance the explorations and exploitation to select the actions that lead to maximal rewards. The computa- tional principles by which animal brains explore during decision-making are poorly understood. Our challenge here was to build a biologically plausible neural network that efficiently explores an environment and understands its effectiveness mathematically. One of the most evolutionarily conserved and important systems in decision making is basal ganglia (BG)1. In particular, the dopamine activities (DA) in BG is thought to represent reward prediction error (RPE) to facilitate reinforcement learning2. Therefore, our starting point is a cortico-BG loop motif3. This network adjusts exploration based on neuronal noises and updates its value estimate through RPE. To account for the fact that animals adjust exploration based on experience, we modified the network in two ways. First, it is recently discovered that DA does not simply represent the scalar RPE value; rather it represents RPE in a distribution4. We incorporated the distributional RPE framework and further the hypothesis, allowing an RPE distribution to update the posterior of action values encoded by cortico-BG connections. Second, it is known that the firing in the layer 2/3 of cortex fires is variable and sparse5. Our network thus included a random sparsification of cortical activity as a mechanism of sampling from this posterior for experience-based exploration. Combining these two features, our network is able to take the uncertainty of our value estimates into account to accomplish efficient exploration in a variety of environments. 

Task allocation is an important problem for robot swarms to solve, allowing agents to reduce task completion time by performing tasks in a distributed fashion. Existing task allocation algorithms often assume prior knowledge of task location and demand or fail to consider the effects of the geometric distribution of tasks on the completion time and communication cost of the algorithms. In this paper, we examine an environment where agents must explore and discover tasks with positive demand and successfully assign themselves to complete all such tasks. We first provide a new discrete general model for modeling swarms. Operating within this theoretical framework, we propose two new task allocation algorithms for initially unknown environments – one based on N-site selection and the other on virtual pheromones. We analyze each algorithm separately and also evaluate the effectiveness of the two algorithms in dense vs. sparse task distributions. Compared to the Levy walk, which has been theorized to be optimal for foraging, our virtual pheromone inspired algorithm is much faster in sparse to medium task densities but is communication and agent intensive. Our site selection inspired algorithm also outperforms Levy walk in sparse task densities and is a less resource-intensive option than our virtual pheromone algorithm for this case. Because the performance of both algorithms relative to random walk is dependent on task density, our results shed light on how task density is important in choosing a task allocation algorithm in initially unknown environments.