Neuroscience seminars

The mammalian retina is considered an autonomous neuronal tissue, yet there is evidence that it receives inputs from the brain in the form of retinopetal axons. A sub-population of these axons was suggested to belong to histaminergic neurons located in the tuberomammillarynucleus (TMN) of the hypothalamus. Using viral injections to the TMN, we identified these retinopetal axons and found that although few in number, they extensively branch to cover a large portion of the retina. Using Ca2+ imaging and electrophysiology, we show that histamine application increases spontaneous firing rates and alters the light responses of a significant portion of retinal ganglion cells (RGCs). Direct activation of the histaminergic axons also induced significant changes in RGCs activity. Since activity in the TMN was shown to correlate with arousal state, our data suggest the retinal code may change with the animal's behavioral state through the release of histamine from TMN histaminergic neurons.

The basal ganglia are a collection of brain areas that are connected by a variety of synaptic pathways and are a site of significant reward-related dopamine release. These properties suggest a possible role for the basal ganglia in action selection, guided by reinforcement learning. In this talk, I will discuss a framework for how this function might be performed. I will also present some recent experimental results and theory that call for a re-evaluation of certain aspects of this framework. Next, I will turn to the changes in basal ganglia activity observed to occur with the dopamine depletion associated with Parkinson’s disease. I will discuss some of the potential functional implications of some of these changes and, if time permits, will conclude with some new results that focus on delta oscillations under dopamine depletion.

Cortical plasticity is the neural mechanism by which the cerebrum adapts itself to its environment, while at the same time making it vulnerable to impoverished sensory or developmental experiences. Like the visual system, auditory development passes through a series of sensitive periods in which circuits and connections are established and then refined by experience. Current research is expanding our understanding of cerebral processing and organization in the deaf. In the congenitally deaf, higher-order areas of "deaf" auditory cortex demonstrate significant crossmodal plasticity with neurons responding to visual and somatosensory stimuli. This crucial cerebral function results in compensatory plasticity. Not only can the remaining inputs reorganize to substitute for those lost, but this additional circuitry also confers enhanced abilities to the remaining systems. In this presentation we will review our present understanding of the structure and function of “deaf” auditory cortex using psychophysical, electrophysiological, and connectional anatomy approaches and consider how this knowledge informs our expectations of the capabilities of cochlear implants in the developing brain.

The CNS is responsive to an ever-changing environment. Until recently, studies of neural plasticity focused almost exclusively on functional and structural changes of neuronal synapses. In recent years, myelin plasticity has emerged as a potential modulator of neural networks. Myelination of previously unmyelinated axons, and changes in the structure on already-myelinated axons, can have large effects on network function. The heterogeneity of the extent of how axons in the CNS are myelinated offers diverse scope for dynamic myelin changes to fine-tune neural circuits. The traditionally held view of myelin as a passive insulator of axons is now changing to one of lifelong changes in myelin, modulated by neuronal activity and experience. Myelin, produced by oligodendrocytes (OLs), is essential for normal brain function, as it provides fast signal transmission, promotes synchronization of neuronal signals and helps to maintain neuronal function. OLs differentiate from oligodendrocyte precursor cells (OPCs), which are distributed throughout the adult brain, and myelination continues into late adulthood. OPCs can sense neuronal activity as they receive synaptic inputs from neurons and express voltage-gated ion channels and neurotransmitter receptors, and differentiate into myelinating OLs in response to changes in neuronal activity. This lecture will explore to what extent myelin plasticity occurs in adult animals, whether myelin changes occur in non-motor learning tasks, especially in learning and memory, and questions whether myelin plasticity and myelin regeneration are two sides of the same coin.

Mammalian vision and visually-guided behavior relies on neurons distributed across diverse brain regions. In this talk I will describe our efforts to create tools that allow us to measure activity from these distributed circuits - Neuropixels probes for large-scale electrophysiology - and our findings from studies deploying these tools to study visual detection and discrimination in mice.

Ascending neuromodulatory projections from the locus coeruleus (LC) affect cortical neural networks via the release of norepinephrine (NE). However, the exact nature of these neuromodulatory effects on neural activity patterns in vivo is not well understood. Here we show that in awake monkeys, LC activation is associated with changes in coordinated activity patterns in the anterior cingulate cortex (ACC). These relationships, which are largely independent of changes in firing rates of individual ACC neurons, depend on the type of LC activation: ACC pairwise correlations tend to be reduced when tonic (baseline) LC activity increases but are enhanced when external events drive phasic LC responses. Both relationships covary with pupil changes that reflect LC activation and arousal. These results suggest that modulations of information processing that reflect changes in coordinated activity patterns in cortical networks can result partly from ongoing, context-dependent, arousal-related changes in activation of the LC-NE system.

Hydra is an extraordinary creature. Continuously replacing itself, it can live indefinitely, performing a stable repertoire of reasonably sophisticated behaviors. This remarkable stability under plasticity may be due to the uniform nature of its nervous system, which consists of two apparently noncommunicating nerve net layers. We use modeling to understand the role of active muscles and biomechanics interact with neural activity to shape Hydra behaviour. We will discuss our findings and thoughts on how this simple nervous system may self-organize to produce purposeful behavior.

Homeostatic plasticity plays a key role in stabilizing neural network activity. But what is its role in neural information processing? We showed analytically how homeostasis changes collective dynamics and consequently information flow - depending on the input to the network. We then studied how input and homeostasis on a recurrent network of LIF neurons impacts information flow and task performance. We showed how we can tune the working point of the network, and found that, contrary to previous assumptions, there is not one optimal working point for a family of tasks, but each task may require its own working point.

Choosing among spatially distributed options is a central challenge for animals, from deciding among alternative potential food sources or refuges, to choosing with whom to associate. Here, using an integrated theoretical and experimental approach (employing immersive Virtual Reality), with both invertebrate and vertebrate models—the fruit fly, desert locust and zebrafish—we consider the recursive interplay between movement and collective vectorial integration in the brain during decision-making regarding options (potential ‘targets’) in space. We reveal that the brain repeatedly breaks multi-choice decisions into a series of abrupt (critical) binary decisions in space-time where organisms switch, spontaneously, from averaging vectorial information among, to suddenly excluding one of, the remaining options. This bifurcation process repeats until only one option—the one ultimately selected—remains. Close to each bifurcation the ‘susceptibility’ of the system exhibits a sharp increase, inevitably causing small differences among the remaining options to become amplified; a property that both comes ‘for free’ and is highly desirable for decision-making. This mechanism facilitates highly effective decision-making, and is shown to be robust both to the number of options available, and to context, such as whether options are static (e.g. refuges) or mobile (e.g. other animals). In addition, we find evidence that the same geometric principles of decision-making occur across scales of biological organisation, from neural dynamics to animal collectives, suggesting they are fundamental features of spatiotemporal computation.

Perception of object motion is fundamentally multisensory, yet little is known about similarities and differences in the computations that give rise to our experience across senses. Insight can be provided by examining auditory motion processing in early blind individuals. In those who become blind early in life, the ‘visual’ motion area hMT+ responds to auditory motion. Meanwhile, the planum temporale, associated with auditory motion in sighted individuals, shows reduced selectivity for auditory motion, suggesting competition between cortical areas for functional role. According to the metamodal hypothesis of cross-modal plasticity developed by Pascual-Leone, the recruitment of hMT+ is driven by it being a metamodal structure containing “operators that execute a given function or computation regardless of sensory input modality”. Thus, the metamodal hypothesis predicts that the computations underlying auditory motion processing in early blind individuals should be analogous to visual motion processing in sighted individuals - relying on non-separable spatiotemporal filters. Inconsistent with the metamodal hypothesis, evidence suggests that the computational algorithms underlying auditory motion processing in early blind individuals fail to undergo a qualitative shift as a result of cross-modal plasticity. Auditory motion filters, in both blind and sighted subjects, are separable in space and time, suggesting that the recruitment of hMT+ to extract motion information from auditory input includes a significant modification of its normal computational operations.

During our everyday lives, much of what we experience is familiar and predictable. We typically follow the same morning routine, take the same route to work, and encounter the same colleagues. However, every once in a while, we encounter a surprising event that violates our expectations. When we encounter such violations of our expectations, it is adaptive to update our internal model of the world in order to make better predictions in the future. The hippocampus is thought to support both the learning of the predictable structure of our environment, as well as the detection and encoding of violations. However, the hippocampus is a complex and heterogeneous structure, composed of different subfields that are thought to subserve different functions. As such, it is not yet known how the hippocampus accomplishes the learning and updating of structured knowledge. Using behavioral methods and high-resolution fMRI, I'll show that during learning of repeated and predicted events, hippocampal subfields differentially integrate and separate event representations, thus learning the structure of ongoing experience. I then move on to discuss how when events violate our predictions, there is a shift in communication between hippocampal subfields, potentially allowing for efficient encoding of the novel and surprising information. If time permits, I'll present an additional behavioral study showing that violations of predictions promote detailed memories. Together, these studies advance our understanding of how we adaptively learn and update our knowledge.

In nature, sensory inputs are often highly structured, and statistical regularities of these signals can be extracted to form expectation about future sensorimotor associations, thereby optimizing behavior. One of the fundamental questions in neuroscience concerns the neural computations that underlie these probabilistic sensorimotor processing. Through a recurrent neural network (RNN) model and human psychophysics and electroencephalography (EEG), the present study investigates circuit mechanisms for processing probabilistic structures of sensory signals to guide behavior. We first constructed and trained a biophysically constrained RNN model to perform a series of probabilistic decision-making tasks similar to paradigms designed for humans. Specifically, the training environment was probabilistic such that one stimulus was more probable than the others. We show that both humans and the RNN model successfully extract information about stimulus probability and integrate this knowledge into their decisions and task strategy in a new environment. Specifically, performance of both humans and the RNN model varied with the degree to which the stimulus probability of the new environment matched the formed expectation. In both cases, this expectation effect was more prominent when the strength of sensory evidence was low, suggesting that like humans, our RNNs placed more emphasis on prior expectation (top-down signals) when the available sensory information (bottom-up signals) was limited, thereby optimizing task performance. Finally, by dissecting the trained RNN model, we demonstrate how competitive inhibition and recurrent excitation form the basis for neural circuitry optimized to perform probabilistic information processing.

Neuronal alternative splicing is a key gene regulatory mechanism in the brain. However, the spliceosome machinery is insufficient to fully specify splicing complexity. In considering the role of the epigenome in activity-dependent alternative splicing, we and others find the histone modification H3K36me3 to be a putative splicing regulator. In this study, we found that mouse cocaine self-administration caused widespread differential alternative splicing, concomitant with the enrichment of H3K36me3 at differentially spliced junctions. Importantly, only targeted epigenetic editing can distinguish between a direct role of H3K36me3 in splicing and an indirect role via regulation of splice factor expression elsewhere on the genome. We targeted Srsf11, which was both alternatively spliced and H3K36me3 enriched in the brain following cocaine self-administration. Epigenetic editing of H3K36me3 at Srsf11 was sufficient to drive its alternative splicing and enhanced cocaine self-administration, establishing the direct causal relevance of H3K36me3 to alternative splicing of Srsf11 and to reward behavior.

Traumatic brain injury (TBI), a leading cause of acquired epilepsy, results in primary cellular injury as well as secondary neurophysiological and inflammatory responses which contribute to epileptogenesis. I will present our recent studies identifying a role for neuro-immune interactions, specifically, the innate immune receptor Toll-like receptor 4 (TLR4), in enhancing network excitability and cell loss in hippocampal dentate gyrus early after concussive brain injury. I will describe results indicating that the transient post-traumatic increases in dentate neurogenesis which occurs during the same early post-injury period augments dentate network excitability and epileptogenesis. I will provide evidence for the beneficial effects of targeting TLR4 and neurogenesis early after brain injury in limiting epileptogenesis. We will discuss potential mechanisms for convergence of the post-traumatic neuro-immune and neurogenic changes and the implications for therapies to reduce neurological deficits and epilepsy after brain injury.

Spike-driven adaptation involves intracellular mechanisms that are initiated by spiking and lead to the subsequent reduction of spiking rate. One of its consequences is the temporal patterning of spike trains, as it imparts serial correlations between interspike intervals in baseline activity. Surprisingly the hidden adaptation states that lead to these correlations themselves exhibit quasi-independence. This talk will first discuss recent findings about the role of such adaptation in suppressing noise and extending sensory detection to weak stimuli that leave the firing rate unchanged. Further, a matching of the post-synaptic responses to the pre-synaptic adaptation time scale enables a recovery of the quasi-independence property, and can explain observations of correlations between post-synaptic EPSPs and behavioural detection thresholds. We then consider the involvement of spike-driven adaptation in the representation of intervals between sensory events. We discuss the possible link of this time-stamping mechanism to the conversion of egocentric to allocentric coordinates. The heterogeneity of the population parameters enables the representation and Bayesian decoding of time sequences of events which may be put to good use in path integration and hilus neuron function in hippocampus.