Neuroscience seminars

The goal of neuroscience is to understand how the nervous system controls behaviour, not only in the simplified environments of the lab, but also in the natural environments for which nervous systems evolved. In pursuing this goal, neuroscience research is supported by an ever-larger toolbox, ranging from optogenetics to connectomics. However, often these tools are coupled with reductionist approaches for linking nervous systems and behaviour. This course will introduce advanced techniques for measuring and analysing behaviour, as well as three fundamental principles as necessary to understanding biological behaviour: (1) morphology and environment; (2) action-perception closed loops and purpose; and (3) individuality and historical contingencies [1]. [1] Gomez-Marin, A., & Ghazanfar, A. A. (2019). The life of behavior. Neuron, 104(1), 25-36

In the first half of my talk, I will discuss our recent work on the midbrain dopamine system. The hypothesis that midbrain dopamine neurons broadcast an error signal for the prediction of reward is among the great successes of computational neuroscience. However, our recent results contradict a core aspect of this theory: that the neurons uniformly convey a scalar, global signal. I will review this work, as well as our new efforts to update models of the neural basis of reinforcement learning with our data. In the second half of my talk, I will discuss our recent findings of state-dependent decision-making mechanisms in the striatum.

The development of circuit-based therapeutics to treat neurological and neuropsychiatric diseases require detailed localization and understanding of electrophysiological signals in the human brain. Electrodes can record and stimulate circuits in many ways, and we often rely on non-invasive imaging methods to predict the location to implant electrodes. However, electrophysiological and imaging signals measure the underlying tissue in a fundamentally different manner. To integrate multimodal data and benefit from these complementary measurements, I will describe an approach that considers how different measurements integrate signals across the underlying tissue. I will show how this approach helps relate fMRI and intracranial EEG measurements and provides new insights into how electrical stimulation influences human brain networks.

The human middle temporal complex (hMT+) has a crucial biological relevance for the processing and detection of direction and speed of motion in visual stimuli. In both humans and monkeys, it has been extensively investigated in terms of its retinotopic properties and selectivity for direction of moving stimuli; however, only in recent years there has been an increasing interest in how neurons in MT encode the speed of motion. In this talk, I will explore the proposed mechanism of speed encoding questioning whether hMT+ neuronal populations encode the stimulus speed directly, or whether they separate motion into its spatial and temporal components. I will characterize how neuronal populations in hMT+ encode the speed of moving visual stimuli using electrocorticography ECoG and 7T fMRI. I will illustrate that the neuronal populations measured in hMT+ are not directly tuned to stimulus speed, but instead encode speed through separate and independent spatial and temporal frequency tuning. Finally, I will suggest that this mechanism may play a role in evaluating multisensory responses for visual, tactile and auditory stimuli in hMT+.

Artificial neural networks simplify complex biological circuits into tractable models for computational exploration and experimentation. However, the simplification of artificial models also undermines their applicability to real brain dynamics. Typical efforts to address this mismatch add complexity to increasingly unwieldy models. Here, we take a different approach; by reducing the complexity of a biological cortical culture, we aim to distil the essential factors of neuronal dynamics and plasticity. We leverage recent advances in growing neurons from human induced pluripotent stem cells (hiPSCs) to analyse ex vivo cortical cultures with only two distinct excitatory and inhibitory neuron populations. Over 6 weeks of development, we record from thousands of neurons using high-density microelectrode arrays (HD-MEAs) that allow access to individual neurons and the broader population dynamics. We compare these dynamics to two-population artificial networks of single-compartment neurons with random sparse connections and show that they produce similar dynamics. Specifically, our model captures the firing and bursting statistics of the cultures. Moreover, tightly integrating models and cultures allows us to evaluate the impact of changing architectures over weeks of development, with and without external stimuli. Broadly, the use of simplified cortical cultures enables us to use the repertoire of theoretical neuroscience techniques established over the past decades on artificial network models. Our approach of deriving neural networks from human cells also allows us, for the first time, to directly compare neural dynamics of disease and control. We found that cultures e.g. from epilepsy patients tended to have increasingly more avalanches of synchronous activity over weeks of development, in contrast to the control cultures. Next, we will test possible interventions, in silico and in vitro, in a drive for personalised approaches to medical care. This work starts bridging an important theoretical-experimental neuroscience gap for advancing our understanding of mammalian neuron dynamics.

Memory consolidation is the process by which recent experiences are assimilated into long-term memory. In animals, this process requires the offline replay of sequences observed during online exploration in the hippocampus. Recent experimental work has found that salient but task-irrelevant stimuli are systematically excluded from these replay epochs, suggesting that replay samples from an abstracted model of the world, rather than verbatim previous experiences. We find that this phenomenon can be explained parsimoniously and biologically plausibly by a Hebbian spike time-dependent plasticity rule at inhibitory synapses. Using spiking networks at three levels of abstraction–leaky integrate-and-fire, biophysically detailed, and abstract binary–we show that this rule enables efficient inference of a model of the structure of the world. While plasticity has previously mainly been studied at excitatory synapses, we find that plasticity at excitatory synapses alone is insufficient to accomplish this type of structural learning. We present theoretical results in a simplified model showing that in the presence of Hebbian excitatory and inhibitory plasticity, the replayed sequences form a statistical estimator of a latent sequence, which converges asymptotically to the ground truth. Our work outlines a direct link between the synaptic and cognitive levels of memory consolidation, and highlights a potential conceptually distinct role for inhibition in computing with SNNs.

Talk & Tutorial

The spike-threshold nonlinearity is a fundamental, yet enigmatic, component of biological computation — despite its role in many theories, it has evaded definitive characterisation. Indeed, much classic work has attempted to limit the focus on spiking by smoothing over the spike threshold or by approximating spiking dynamics with firing-rate dynamics. Here, we take a novel perspective that captures the full potential of spike-based computation. Based on previous studies of the geometry of efficient spike-coding networks, we consider a population of neurons with low-rank connectivity, allowing us to cast each neuron’s threshold as a boundary in a space of population modes, or latent variables. Each neuron divides this latent space into subthreshold and suprathreshold areas. We then demonstrate how a network of inhibitory (I) neurons forms a convex, attracting boundary in the latent coding space, and a network of excitatory (E) neurons forms a concave, repellant boundary. Finally, we show how the combination of the two yields stable dynamics at the crossing of the E and I boundaries, and can be mapped onto a constrained optimization problem. The resultant EI networks are balanced, inhibition-stabilized, and exhibit asynchronous irregular activity, thereby closely resembling cortical networks of the brain. Moreover, we demonstrate how such networks can be tuned to either suppress or amplify noise, and how the composition of inhibitory convex and excitatory concave boundaries can result in universal function approximation. Our work puts forth a new theory of biologically-plausible computation in balanced spiking networks, and could serve as a novel framework for scalable and interpretable computation with spikes.

A central problem in biological learning is how information about the outcome of a decision or behavior can be used to reliably guide learning across distributed neural circuits while obeying biological constraints. This “credit assignment” problem is commonly solved in artificial neural networks through supervised gradient descent and the backpropagation algorithm. In contrast, biological learning is typically modelled using unsupervised Hebbian learning rules. While these rules only use local information to update synaptic weights, and are sometimes combined with weight constraints to reflect a diversity of excitatory (only positive weights) and inhibitory (only negative weights) cell types, they do not prescribe a clear mechanism for how to coordinate learning across multiple layers and propagate error information accurately across the network. In recent years, several groups have drawn inspiration from the known dendritic non-linearities of pyramidal neurons to propose new learning rules and network architectures that enable biologically plausible multi-layer learning by processing error information in segregated dendrites. Meanwhile, recent experimental results from the hippocampus have revealed a new form of plasticity—Behavioral Timescale Synaptic Plasticity (BTSP)—in which large dendritic depolarizations rapidly reshape synaptic weights and stimulus selectivity with as little as a single stimulus presentation (“one-shot learning”). Here we explore the implications of this new learning rule through a biologically plausible implementation in a rate neuron network. We demonstrate that regulation of dendritic spiking and BTSP by top-down feedback signals can effectively coordinate plasticity across multiple network layers in a simple pattern recognition task. By analyzing hidden feature representations and weight trajectories during learning, we show the differences between networks trained with standard backpropagation, Hebbian learning rules, and BTSP.

Understanding the nature of neural representation is a central challenge of neuroscience. One common approach to this challenge is to compute receptive fields by correlating neural activity with external variables drawn from sensory signals. But these receptive fields are only meaningful to the experimenter, not the organism, because only the experimenter has access to both the neural activity and knowledge of the external variables. To understand neural representation more directly, recent methodological advances have sought to capture the intrinsic geometry of sensory driven neural responses without external reference. To date, this approach has largely been restricted to low-dimensional stimuli as in spatial navigation. In this talk, I will discuss recent work from my lab examining the intrinsic geometry of sensory representations in a model vocal communication system, songbirds. From the assumption that sensory systems capture invariant relationships among stimulus features, we conceptualized the space of natural birdsongs to lie on the surface of an n-dimensional hypersphere. We computed composite receptive field models for large populations of simultaneously recorded single neurons in the auditory forebrain and show that solutions to these models define convex regions of response probability in the spherical stimulus space. We then define a combinatorial code over the set of receptive fields, realized in the moment-to-moment spiking and non-spiking patterns across the population, and show that this code can be used to reconstruct high-fidelity spectrographic representations of natural songs from evoked neural responses. Notably, we find that topological relationships among combinatorial codewords directly mirror acoustic relationships among songs in the spherical stimulus space. That is, the time-varying pattern of co-activity across the neural population expresses an intrinsic representational geometry that mirrors the natural, extrinsic stimulus space. Combinatorial patterns across this intrinsic space directly represent complex vocal communication signals, do not require computation of receptive fields, and are in a form, spike time coincidences, amenable to biophysical mechanisms of neural information propagation.

The brain efficiently performs nonlinear computations through its intricate networks of spiking neurons, but how this is done remains elusive. While recurrent spiking networks implementing linear computations can be directly derived and easily understood (e.g., in the spike coding network (SCN) framework), the connectivity required for nonlinear computations can be harder to interpret, as they require additional non-linearities (e.g., dendritic or synaptic) weighted through supervised training. Here we extend the SCN framework to directly implement any polynomial dynamical system. This results in networks requiring multiplicative synapses, which we term the multiplicative spike coding network (mSCN). We demonstrate how the required connectivity for several nonlinear dynamical systems can be directly derived and implemented in mSCNs, without training. We also show how to precisely carry out higher-order polynomials with coupled networks that use only pair-wise multiplicative synapses, and provide expected numbers of connections for each synapse type. Overall, our work provides an alternative method for implementing nonlinear computations in spiking neural networks, while keeping all the attractive features of standard SCNs such as robustness, irregular and sparse firing, and interpretable connectivity. Finally, we discuss the biological plausibility of mSCNs, and how the high accuracy and robustness of the approach may be of interest for neuromorphic computing.

Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological memory, it has so far been analyzed mostly in the context of pattern completion and unsupervised learning. Here, we propose that Hebbian plasticity is fundamental for computations in biological neural systems. We introduce a novel spiking neural network (SNN) architecture that is enriched by Hebbian synaptic plasticity. We experimentally show that our memory-equipped SNN model outperforms state-of-the-art deep learning mechanisms in a sequential pattern-memorization task, as well as demonstrate superior out-of-distribution generalization capabilities compared to these models. We further show that our model can be successfully applied to one-shot learning and classification of handwritten characters, improving over the state-of-the-art SNN model. We also demonstrate the capability of our model to learn associations for audio to image synthesis from spoken and handwritten digits. Our SNN model further presents a novel solution to a variety of cognitive question answering tasks from a standard benchmark, achieving comparable performance to both memory-augmented ANN and SNN-based state-of-the-art solutions to this problem. Finally we demonstrate that our model is able to learn from rewards on an episodic reinforcement learning task and attain near-optimal strategy on a memory-based card game. Hence, our results show that Hebbian enrichment renders spiking neural networks surprisingly versatile in terms of their computational as well as learning capabilities. Since local Hebbian plasticity can easily be implemented in neuromorphic hardware, this also suggests that powerful cognitive neuromorphic systems can be build based on this principle.

“Of pain you could wish only one thing: that it should stop. Nothing in the world was so bad as physical pain. In the face of pain there are no heroes.- George Orwell, ‘1984’ " "Neuroscience has revealed the secrets of the brain and nervous system to an extent that was beyond the realm of imagination just 10-20 years ago, let alone in 1949 when Orwell wrote his prophetic novel. Understanding pain, however, presents a unique challenge to academia, industry and medicine, being both a measurable physiological process as well as deeply personal and subjective. Given the millions of people who suffer from pain every day, wishing only, “that it should stop”, the need to find more effective treatments cannot be understated." "‘New treatments for pain’ will bring together approximately 120 people from the commercial, academic, and not-for-profit sectors to share current knowledge, identify future directions, and enable collaboration, providing delegates with meaningful and practical ways to accelerate their own work into developing treatments for pain.

Drosophila olfactory sensory hairs ("sensilla") typically house two olfactory receptor neurons (ORNs) which can laterally inhibit each other via electrical ("ephaptic") coupling. ORN pairing is highly stereotyped and genetically determined. Thus, olfactory signals arriving in the Antennal Lobe (AL) have been pre-processed by a fixed and shallow network at the periphery. To uncover the functional significance of this organization, we developed a nonlinear phenomenological model of asymmetrically coupled ORNs responding to odor mixture stimuli. We derived an analytical solution to the ORNs’ dynamics, which shows that the peripheral network can extract the valence of specific odor mixtures via transient amplification. Our model predicts that for efficient read-out of the amplified valence signal there must exist specific patterns of downstream connectivity that reflect the organization at the periphery. Analysis of AL→Lateral Horn (LH) fly connectomic data reveals evidence directly supporting this prediction. We further studied the effect of ephaptic coupling on olfactory processing in the AL→Mushroom Body (MB) pathway. We show that stereotyped ephaptic interactions between ORNs lead to a clustered odor representation of glomerular responses. Such clustering in the AL is an essential assumption of theoretical studies on odor recognition in the MB. Together our work shows that preprocessing of olfactory stimuli by a fixed and shallow network increases sensitivity to specific odor mixtures, and aids in the learning of novel olfactory stimuli. Work led by Palka Puri, in collaboration with Chih-Ying Su and Shiuan-Tze Wu.