Seminars

If we can make computers play chess and even Jeopardy and Go, then why can't we make them see like us? How does our brain solve the problem of seeing? I will describe some of our recent insights into understanding object recognition in the brain using behavioral, neuronal and computational methods.

Humans and other animals make decisions in order to satisfy their goals. However, it remains unknown how neural circuits compute which of multiple possible goals should be pursued (e.g., when balancing hunger and thirst) and how to combine these signals with estimates of available reward alternatives. Here, humans undergoing fMRI accumulated two distinct assets over a sequence of trials. Financial outcomes depended on the minimum cumulate of either asset, creating a need to maintain “value equilibrium” by redressing any imbalance among the assets. Blood-oxygen-level-dependent (BOLD) signals in the rostral anterior cingulate cortex (rACC) tracked the level of imbalance among goals, whereas the ventromedial prefrontal cortex (vmPFC) signaled the level of redress incurred by a choice rather than the overall amount received. These results suggest that a network of medial frontal brain regions compute a value signal that maintains value equilibrium among internal goals.

The idea that perception in humans relies on some form of probabilistic computations has become very popular over the last decades. It has been extremely difficult however to characterize the extent and the nature of the probabilistic representations and operations that are manipulated by neural populations in the human cortex. Several theoretical works suggest that probabilistic representations are present from low-level sensory areas to high-level areas. According to this view, the neural dynamics implements some forms of probabilistic message passing (i.e. neural sampling, probabilistic population coding, etc.) which solves the problem of perceptual inference. Here I will present recent experimental evidence that human and non-human primate perception implements some form of message passing. I will first review findings showing probabilistic integration of sensory evidence across space and time in primate visual cortex. Second, I will show that the confidence reports in a hierarchical task reveal that uncertainty is represented both at lower and higher levels, in a way that is consistent with probabilistic message passing both from lower to higher and from higher to lower representations. Finally, I will present behavioral and neural evidence that human perception takes into account pairwise correlations in sequences of sensory samples in agreement with the message passing hypothesis, and against standard accounts such as accumulation of sensory evidence or predictive coding.

If the mind controls the brain, then there is FREE WILL and its corollaries, dignity and responsibility. You are king in your skull-sized kingdom and the architect of your destiny. If, on the other hand, the brain controls the mind, an incendiary conclusion follows: There can be no FREE WILL, no praise, no punishment and no purgatory. There will be a presentation of the speaker’s novel which, inter alia, is concerned with this question: 21 year in the making this is the first presentation of A River Divided (environmental genre)

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

This talk will focus on our lab's extensive research on understanding and enhancing creative analogical reasoning. I will cover the development of the analogy finding matrix task, evidence for conscious augmentation of creative state during this task, and the real-world implications this ability has for college STEM education. I will also discuss recent research aimed at enhancing performance on this creative analogical reasoning task using both transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS).

The theory of predictive processing posits that the brain computes expectations to process information predictively. Empirical evidence in support of this theory, however, is scarce and largely limited to sensory areas. Here, we report a precise and adaptive mechanism in the frontal cortex of non-human primates consistent with predictive processing of temporal events. We found that the speed of neural dynamics is precisely adjusted according to the average time of an expected stimulus. This speed adjustment, in turn, enables neurons to encode stimuli in terms of deviations from expectation. This lawful relationship was evident across multiple experiments and held true during learning: when temporal statistics underwent covert changes, neural responses underwent predictable changes that reflected the new mean. Together, these results highlight a precise mathematical relationship between temporal statistics in the environment and neural activity in the frontal cortex that may serve as a mechanism for predictive temporal processing.

Cognitive models of timing often include a pacemaker analogue whose ticks are accumulated to form an internal representation of time, and a threshold that determines when a target duration has elapsed. However, clear EEG manifestations of these abstract components have not yet been identified. We measured the EEG of subjects while they performed a temporal bisection task in which they were requested to categorize visual stimuli as short or long in duration. We report an ERP component whose amplitude depends monotonically on the stimulus duration. The relation of the ERP amplitude and stimulus duration can be captured by a simple model, adapted from a known drift-diffusion model for time perception. It includes a noisy accumulator that starts with the stimulus onset and a threshold. If the threshold is reached during stimulus presentation, the stimulus is categorized as "long", otherwise the stimulus is categorized as "short". At the stimulus offset, a response proportional to the distance to the threshold is emitted. This simple model has two parameters that fit both the behavior and ERP amplitudes recorded in the task. Two subsequent experiments replicate and extend this finding to another modality (touch) as well as to different time ranges (subsecond and suprasecond), establishing the described ERP component as a useful handle on the cognitive processes involved in temporal decisions.

Over the last few decades, the use of deep brain stimulation (DBS) to improve the treatment of those with neurological movement disorders represents a critical success story in the development of invasive neurotechnology and the promise of brain-computer interfaces (BCI) to improve the lives of those suffering from incurable neurological disorders. In the last decade, investigational devices capable of recording and streaming neural activity from chronically implanted therapeutic electrodes has supercharged research into clinical applications of BCI, enabling in-human studies investigating the use of adaptive stimulation algorithms to further enhance therapeutic outcomes and improve future device performance. In this talk, Dr. Herron will review ongoing clinical research efforts in the field of adaptive DBS systems and algorithms. This will include an overview of DBS in current clinical practice, the development of bidirectional clinical-use research platforms, ongoing algorithm evaluation efforts, a discussion of current adoption barriers to be addressed in future work.

Temporal lobe epilepsy (TLE) is a progressive disorder mediated by pathological changes in molecular cascades and neural circuit remodeling in the hippocampus resulting in increased susceptibility to spontaneous seizures and cognitive dysfunction. Targeting these cascades could prevent or reverse symptom progression and has the potential to provide viable disease-modifying treatments that could reduce the portion of TLE patients (>30%) not responsive to current medical therapies. Changes in GABA(A) receptor subunit expression have been implicated in the pathogenesis of TLE, and the Janus Kinase/Signal Transducer and Activator of Transcription (JAK/STAT) pathway has been shown to be a key regulator of these changes. The JAK/STAT pathway is known to be involved in inflammation and immunity, and to be critical for neuronal functions such as synaptic plasticity and synaptogenesis. Our laboratories have shown that a STAT3 inhibitor, WP1066, could greatly reduce the number of spontaneous recurrent seizures (SRS) in an animal model of pilocarpine-induced status epilepticus (SE). This suggests promise for JAK/STAT inhibitors as disease-modifying therapies, however, the potential adverse effects of systemic or global CNS pathway inhibition limits their use. Development of more targeted therapeutics will require a detailed understanding of JAK/STAT-induced epileptogenic responses in different cell types. To this end, we have developed a new transgenic line where dimer-dependent STAT3 signaling is functionally knocked out (fKO) by tamoxifen-induced Cre expression specifically in forebrain excitatory neurons (eNs) via the Calcium/Calmodulin Dependent Protein Kinase II alpha (CamK2a) promoter. Most recently, we have demonstrated that STAT3 KO in excitatory neurons (eNSTAT3fKO) markedly reduces the progression of epilepsy (SRS frequency) in the intrahippocampal kainate (IHKA) TLE model and protects mice from kainic acid (KA)-induced memory deficits as assessed by Contextual Fear Conditioning. Using data from bulk hippocampal tissue RNA-sequencing, we further discovered a transcriptomic signature for the IHKA model that contains a substantial number of genes, particularly in synaptic plasticity and inflammatory gene networks, that are down-regulated after KA-induced SE in wild-type but not eNSTAT3fKO mice. Finally, we will review data from other models of brain injury that lead to epilepsy, such as TBI, that implicate activation of the JAK/STAT pathway that may contribute to epilepsy development.

A key purpose of causal reasoning by individuals and by collectives is to enhance action, to give humans yet more control over their environment. As a result, causal reasoning serves as the infrastructure of both thought and discourse. Humans represent causal systems accurately in some ways, but also show some systematic biases (we tend to neglect causal pathways other than the one we are thinking about). Even when accurate, people’s understanding of causal systems tends to be superficial; we depend on our communities for most of our causal knowledge and reasoning. Nevertheless, we are better causal reasoners than machines. Modern machine learners do not come close to matching human abilities.

While classical industrial robots are mostly designed for repetitive tasks, assistive robots will be challenged by a variety of different tasks in close contact with humans. Hereby, learning through the direct interaction with humans provides a potentially powerful tool for an assistive robot to acquire new skills and to incorporate prior human knowledge during the exploration of novel tasks. Moreover, an intuitive interactive teaching process may allow non-programming experts to contribute to robotic skill learning and may help to increase acceptance of robotic systems in shared workspaces and everyday life. In this talk, I will discuss recent research I did on interactive robot skill learning and the remaining challenges on the route to human-centered teaching of assistive robots. In particular, I will also discuss potential connections and overlap with cognitive science. The presented work covers learning a library of probabilistic movement primitives from human demonstrations, intention aware adaptation of learned skills in shared workspaces, and multi-channel interactive reinforcement learning for sequential tasks.

The visual system is characterized by multiple mirrored visuotopic maps, with each repetition corresponding to a different visual area. In this work we explore whether such visuotopic organization can emerge as a result of minimizing the total wire length between neurons connected in a deep hierarchical network. Our results show that networks with purely feedforward connectivity typically result in a single visuotopic map, and in certain cases no visuotopic map emerges. However, when we modify the network by introducing lateral connections, with sufficient lateral connectivity among neurons within layers, multiple visuotopic maps emerge, where some connectivity motifs yield mirrored alternations of visuotopic maps–a signature of biological visual system areas. These results demonstrate that different connectivity profiles have different emergent organizations under the minimum total wire length hypothesis, and highlight that characterizing the large-scale spatial organizing of tuning properties in a biological system might also provide insights into the underlying connectivity.