BI 243 Alison Barth: Learning as a Window to Cortex
Alison Barth explains how distinct neuron types contribute to cortical function and how learning can be used as an experimental window into the organization and plasticity of cortical circuits.
Predictive Coding Light
Current machine learning systems consume vastly more energy than biological brains. Neuromorphic systems aim to overcome this difference by mimicking the brain’s information coding via discrete voltage spikes. However, it remains unclear how both artificial and natural networks of spiking neurons can learn energy-efficient information processing strategies. Here we propose Predictive Coding Light (PCL), a recurrent hierarchical spiking neural network for unsupervised representation learning. In contrast to previous predictive coding approaches, PCL does not transmit prediction errors to higher processing stages. Instead, it suppresses the most predictable spikes and transmits a compressed representation of the input. Using only biologically plausible spike-timing based learning rules, PCL reproduces a wealth of findings on information processing in visual cortex and permits strong performance in downstream classification tasks. Overall, PCL offers a new approach to predictive coding and its implementation in natural and artificial spiking neural networks
Developmental and evolutionary perspectives on thalamic function
Brain organization and function is a complex topic. We are good at establishing correlates of perception and behavior across forebrain circuits, as well as manipulating activity in these circuits to affect behavior. However, we still lack good models for the large-scale organization and function of the forebrain. What are the contributions of the cortex, basal ganglia, and thalamus to behavior? In addressing these questions, we often ascribe function to each area as if it were an independent processing unit. However, we know from the anatomy that the cortex, basal ganglia, and thalamus, are massively interconnected in a large network. One way to generate insight into these questions is to consider the evolution and development of forebrain systems. In this talk, I will discuss the developmental and evolutionary (comparative anatomy) data on the thalamus, and how it fits within forebrain networks. I will address questions including, when did the thalamus appear in evolution, how is the thalamus organized across the vertebrate lineage, and how can the change in the organization of forebrain networks affect behavioral repertoires.
Reactivation in the human brain connects the past with the present
Contrasting developmental principles of human brain development and their relevance to neurodevelopmental disorders
Investigating activity-dependent processes during cortical neuronal assembly in development and disease
Cortical interneurons from brain development to disease
Modeling shared and variable information encoded in fine-scale cortical topographies
Information is encoded in fine-scale functional topographies that vary from brain to brain. Hyperalignment models information that is shared across brain in a high-dimensional common information space. Hyperalignment transformations project idiosyncratic individual topographies into the common model information space. These transformations contain topographic basis functions, affording estimates of how shared information in the common model space is instantiated in the idiosyncratic functional topographies of individual brains. This new model of the functional organization of cortex – as multiplexed, overlapping basis functions – captures the idiosyncratic conformations of both coarse-scale topographies, such as retinotopy and category-selectivity, and fine-scale topographies. Hyperalignment also makes it possible to investigate how information that is encoded in fine-scale topographies differs across brains. These individual differences in fine-grained cortical function were not accessible with previous methods.
Food, taste, and the role of the cortical laminar in action and imagination
Cortex-dependent corrections as the mouse tongue reaches for and misses targets
Brendan Ito (Cornell University, USA) and Teja Bollu (Salk Institute, USA) share unique insights into rapid online motor corrections during mouse licking, analogous to primate goal-oriented reaching. Techniques covered include large-scale single unit recording during behaviour with optogenetics, and a deep-learning-based neural network to resolve 3D tongue kinematics during licking.
Molecular recognition and the assembly of feature-selective retinal circuits
NMC4 Short Talk: Multiscale and extended retrieval of associative memory structures in a cortical model of local-global inhibition balance
Inhibitory neurons take on many forms and functions. How this diversity contributes to memory function is not completely known. Previous formal studies indicate inhibition differentiated by local and global connectivity in associative memory networks functions to rescale the level of retrieval of excitatory assemblies. However, such studies lack biological details such as a distinction between types of neurons (excitatory and inhibitory), unrealistic connection schemas, and non-sparse assemblies. In this study, we present a rate-based cortical model where neurons are distinguished (as excitatory, local inhibitory, or global inhibitory), connected more realistically, and where memory items correspond to sparse excitatory assemblies. We use this model to study how local-global inhibition balance can alter memory retrieval in associative memory structures, including naturalistic and artificial structures. Experimental studies have reported inhibitory neurons and their sub-types uniquely respond to specific stimuli and can form sophisticated, joint excitatory-inhibitory assemblies. Our model suggests such joint assemblies, as well as a distribution and rebalancing of overall inhibition between two inhibitory sub-populations – one connected to excitatory assemblies locally and the other connected globally – can quadruple the range of retrieval across related memories. We identify a possible functional role for local-global inhibitory balance to, in the context of choice or preference of relationships, permit and maintain a broader range of memory items when local inhibition is dominant and conversely consolidate and strengthen a smaller range of memory items when global inhibition is dominant. This model therefore highlights a biologically-plausible and behaviourally-useful function of inhibitory diversity in memory.
Spontaneous activity competes with externally evoked responses in sensory cortex
The interaction between spontaneously and externally evoked neuronal activity is fundamental for a functional brain. Increasing evidence suggests that bursts of high-power oscillations in the 15-30 Hz beta-band represent activation of resting state networks and can mask perception of external cues. Yet demonstration of the effect of beta power modulation on perception in real-time is missing, and little is known about the underlying mechanism. In this talk I will present the methods we developed to fill this gap together with our recent results. We used a closed-loop stimulus-intensity adjustment system based on online burst-occupancy analyses in rats involved in a forepaw vibrotactile detection task. We found that the masking influence of burst-occupancy on perception can be counterbalanced in real-time by adjusting the vibration amplitude. Offline analysis of firing-rates and local field potentials across cortical layers and frequency bands confirmed that beta-power in the somatosensory cortex anticorrelated with sensory evoked responses. Mechanistically, bursts in all bands were accompanied by transient synchronization of cell assemblies, but only beta-bursts were followed by a reduction of firing-rate. Our closed loop approach reveals that spontaneous beta-bursts reflect a dynamic state that competes with external stimuli.
Role of primary visual cortex (V1) in visual awareness: insights from blindsight
Visual Decisions in Natural Action
Natural behavior reveals the way that gaze serves the needs of the current task, and the complex cognitive control mechanisms that are involved. It has become increasingly clear that even the simplest actions involve complex decision processes that depend on an interaction of visual information, knowledge of the current environment, and the intrinsic costs and benefits of actions choices. I will explore these ideas in the context of walking in natural terrain, where we are able to recover the 3D structure of the visual environment. We show that subjects choose flexible paths that depend on the flatness of the terrain over the next few steps. Subjects trade off flatness with straightness of their paths towards the goal, indicating a nuanced trade-off between stability and energetic costs on both the time scale of the next step and longer-range constraints.
The generation of cortical novelty responses through inhibitory plasticity
Animals depend on fast and reliable detection of novel stimuli in their environment. Neurons in multiple sensory areas respond more strongly to novel in comparison to familiar stimuli. Yet, it remains unclear which circuit, cellular, and synaptic mechanisms underlie those responses. Here, we show that spike-timing-dependent plasticity of inhibitory-to-excitatory synapses generates novelty responses in a recurrent spiking network model. Inhibitory plasticity increases the inhibition onto excitatory neurons tuned to familiar stimuli, while inhibition for novel stimuli remains low, leading to a network novelty response. The generation of novelty responses does not depend on the periodicity but rather on the distribution of presented stimuli. By including tuning of inhibitory neurons, the network further captures stimulus-specific adaptation. Finally, we suggest that disinhibition can control the amplification of novelty responses. Therefore, inhibitory plasticity provides a flexible, biologically plausible mechanism to detect the novelty of bottom-up stimuli, enabling us to make experimentally testable predictions.
Relearning to see with a damaged V1
Immersive Neuroscience: Bringing Cognitive Neuroscience Closer to the Real World
What Art can tell us about the Brain
Artists have been doing experiments on vision longer than neurobiologists. Some major works of art have provided insights as to how we see; some of these insights are so undamental that they can be understood in terms of the underlying neurobiology. For example, artists have long realized that color and luminance can play independent roles in visual perception. Picasso said, "Colors are only symbols. Reality is to be found in luminance alone." This observation has a parallel in the functional subdivision of our visual systems, where color and luminance are processed by the evolutionarily newer, primate-specific What system, and the older, colorblind, Where (or How) system. Many techniques developed over the centuries by artists can be understood in terms of the parallel organization of our visual systems. I will explore how the segregation of color and luminance processing are the basis for why some Impressionist paintings seem to shimmer, why some op art paintings seem to move, some principles of Matisse's use of color, and how the Impressionists painted "air". Central and peripheral vision are distinct, and I will show how the differences in resolution across our visual field make the Mona Lisa's smile elusive, and produce a dynamic illusion in Pointillist paintings, Chuck Close paintings, and photomosaics. I will explore how artists have figured out important features about how our brains extract relevant information about faces and objects, and I will discuss why learning disabilities may be associated with artistic talent.
Information Dynamics in the Hippocampus and Cortex and their alterations in epilepsy
Neurological disorders share common high-level alterations, such as cognitive deficits, anxiety, and depression. This raises the possibility of fundamental alterations in the way information conveyed by neural firing is maintained and dispatched in the diseased brain. Using experimental epilepsy as a model of neurological disorder we tested the hypothesis of altered information processing, analyzing how neurons in the hippocampus and the entorhinal cortex store and exchange information during slow and theta oscillations. We equate the storage and sharing of information to low level, or primitive, information processing at the algorithmic level, the theoretical intermediate level between structure and function. We find that these low-level processes are organized into substates during brain states marked by theta and slow oscillations. Their internal composition and organization through time are disrupted in epilepsy, losing brain state-specificity, and shifting towards a regime of disorder in a brain region dependent manner. We propose that the alteration of information processing at an algorithmic level may be a mechanism behind the emergent and widespread co-morbidities associated with epilepsy, and perhaps other disorders.
Multisensory encoding of self-motion in the retrosplenial cortex and beyond
In order to successfully navigate through the environment, animals must accurately estimate the status of their motion with respect to the surrounding scene and objects. In this talk, I will present our recent work on how retrosplenial cortical (RSC) neurons combine vestibular and visual signals to reliably encode the direction and speed of head turns during passive motion and active navigation. I will discuss these data in the context of RSC long-range connectivity and further show our ongoing work on building population-level models of motion representation across cortical and subcortical networks.
Encoding local stimulus attributes and higher visual functions in V1 of behaving monkeys
A dynamical model of the visual cortex
In the past several years, I have been involved in building a biologically realistic model of the monkey visual cortex. Work on one of the input layers (4Ca) of the primary visual cortex (V1) is now nearly complete, and I would like to share some of what I have learned with the community. After a brief overview of the model and its capabilities, I would like to focus on three sets of results that represent three different aspects of the modeling. They are: (i) emergent E-I dynamics in local circuits; (ii) how visual cortical neurons acquire their ability to detect edges and directions of motion, and (iii) a view across the cortical surface: nonequilibrium steady states (in analogy with statistical mechanics) and beyond.
Distinct forms of cortical plasticity underlie difficulties to reliably detect sounds in noisy environments"; "Acoustic context modulates natural sound discrimination in auditory cortex through frequency specific adaptation
Predictive processing in the macaque frontal cortex during time estimation
According to the theory of predictive processing, expectations modulate neural activity so as to optimize the processing of sensory inputs expected in the current environment. While there is accumulating evidence that the brain indeed operates under this principle, most of the attention has been placed on mechanisms that rely on static coding properties of neurons. The potential contribution of dynamical features, such as those reflected in the evolution of neural population dynamics, has thus far been overlooked. In this talk, I will present evidence for a novel mechanism for predictive processing in the temporal domain which relies on neural population dynamics. I will use recordings from the frontal cortex of macaques trained on a time interval reproduction task and show how neural dynamics can be directly related to animals’ temporal expectations, both in a stationary environment and during learning.
How does the cortex integrate conflicting time-information? A model of temporal averaging
In daily life, we consistently make decisions in pursuit of some goal. Many decisions are informed by multiple sources of information. Unfortunately, these sources often provide ambiguous information about what course of action to take. Therefore, determining how the brain integrates information to resolve this ambiguity is key to understanding the neural mechanisms of decision-making. In the domain of time, this topic can be studied by training subjects to predict when a future event will occur based on distinct cues (e.g., tone, light, etc.). If multiple cues are presented simultaneously and their cue-to-event intervals differ (e.g., tone-10s + light-30s), subjects will often expect the event to occur at the average of their intervals. This ‘temporal averaging’ effect is presumably how the timing system resolves ambiguous time-information. The neural mechanisms of temporal averaging are currently unclear. Here, we will propose how temporal averaging could emerge in cortical circuits using a simple modification of a ‘drift-diffusion’ model of timing.
Learning-induced changes in visual cortical processing
Cellular-resolution gene expression profiling in the neonatal marmoset brain reveals dynamic species- and region-specific differences
How to make a Hippocampus
The assembly of a functional neocortex
A computational explanation for domain specificity in the human brain
Many regions of the human brain conduct highly specific functions, such as recognizing faces, understanding language, and thinking about other people’s thoughts. Why might this domain specific organization be a good design strategy for brains, and what is the origin of domain specificity in the first place? In this talk, I will present recent work testing whether the segregation of face and object perception in human brains emerges naturally from an optimization for both tasks. We trained artificial neural networks on face and object recognition, and found that networks were able to perform both tasks well by spontaneously segregating them into distinct pathways. Critically, networks neither had prior knowledge nor any inductive bias about the tasks. Furthermore, networks optimized on tasks which apparently do not develop specialization in the human brain, such as food or cars, and object categorization showed less task segregation. These results suggest that functional segregation can spontaneously emerge without a task-specific bias, and that the domain-specific organization of the cortex may reflect a computational optimization for the real-world tasks humans solve.
Neural basis of metacognitive monitoring
Using marmosets for the study of the visual cortex: unique opportunities, and some pitfalls
Marmosets (Callithrix jacchus) are small South American monkeys which are being increasingly becoming adopted as animal models in neuroscience. Knowledge about the marmoset visual system has developed rapidly over the last decade. But what are the comparative advantages, and disadvantages involved in adopting this emerging model, as opposed to the more traditionally used macaque monkey? In this talk I will present case studies where the simpler brain morphology and short developmental cycle of the marmoset have been key factors in facilitating discoveries about the anatomy and physiology of the visual system. Although no single species provides the “ideal” animal model for invasive studies of the neural bases of visual processing, I argue that the development of robust methodologies for the study of the marmoset brain provides exciting opportunities to address long-standing problems in neuroscience.
Cortical estimation of current and future bodily states
Interoception, the sense of internal bodily signals, is essential for physiological homeostasis, cognition, and emotions. Human neuroimaging studies suggest insular cortex plays a central role in interoception, yet the cellular and circuit mechanisms of its involvement remain unclear. We developed a microprism-based cellular imaging approach to monitor insular cortex activity in behaving mice across different physiological need states. We combine this imaging approach with manipulations of peripheral physiology, circuit-mapping, cell type-specific and circuit-specific manipulation approaches to investigate the underlying circuit mechanisms. I will present our recent data investigating insular cortex activity during two physiological need states – hunger and thirst. These wereinduced naturally by caloric/fluid deficiency, or artificially by activation of specific hypothalamic “hunger neurons” and “thirst neurons”. We found that insular cortex ongoing activity faithfully represents current physiological state, independently of behavior or arousal levels. In contrast, transient responses to learned food- or water-predicting cues reflect a population-level “simulation” of future predicted satiety. Together with additional circuit-mapping and manipulation experiments, our findings suggest that insular cortex integrates visceral-sensory inputs regarding current physiological state with hypothalamus-gated amygdala inputs signaling availability of food/water. This way, insular cortex computes a prediction of future physiological state that can be used to guide behavioral choice.
Genetic evolution of cerebral cortex size determinants
From axon navigation to neuroblastoma disease : the two faces of axon guidance genes
Recurrent corticothalamic feedback in the auditory system: perceptual salience and dopaminergic modulation
The integration of parvalbumin and somatostatin interneurons into cortical networks:both nature and nurture
A new computational framework for understanding vision in our brain
Visual attention selects only a tiny fraction of visual input information for further processing. Selection starts in the primary visual cortex (V1), which creates a bottom-up saliency map to guide the fovea to selected visual locations via gaze shifts. This motivates a new framework that views vision as consisting of encoding, selection, and decoding stages, placing selection on center stage. It suggests a massive loss of non-selected information from V1 downstream along the visual pathway. Hence, feedback from downstream visual cortical areas to V1 for better decoding (recognition), through analysis-by- synthesis, should query for additional information and be mainly directed at the foveal region. Accordingly, non-foveal vision is not only poorer in spatial resolution, but also more susceptible to many illusions.
Neural coding in the auditory cortex - "Emergent Scientists Seminar Series
Dr Jennifer Lawlor Title: Tracking changes in complex auditory scenes along the cortical pathway Complex acoustic environments, such as a busy street, are characterised by their everchanging dynamics. Despite their complexity, listeners can readily tease apart relevant changes from irrelevant variations. This requires continuously tracking the appropriate sensory evidence while discarding noisy acoustic variations. Despite the apparent simplicity of this perceptual phenomenon, the neural basis of the extraction of relevant information in complex continuous streams for goal-directed behavior is currently not well understood. As a minimalistic model for change detection in complex auditory environments, we designed broad-range tone clouds whose first-order statistics change at a random time. Subjects (humans or ferrets) were trained to detect these changes.They were faced with the dual-task of estimating the baseline statistics and detecting a potential change in those statistics at any moment. To characterize the extraction and encoding of relevant sensory information along the cortical hierarchy, we first recorded the brain electrical activity of human subjects engaged in this task using electroencephalography. Human performance and reaction times improved with longer pre-change exposure, consistent with improved estimation of baseline statistics. Change-locked and decision-related EEG responses were found in a centro-parietal scalp location, whose slope depended on change size, consistent with sensory evidence accumulation. To further this investigation, we performed a series of electrophysiological recordings in the primary auditory cortex (A1), secondary auditory cortex (PEG) and frontal cortex (FC) of the fully trained behaving ferret. A1 neurons exhibited strong onset responses and change-related discharges specific to neuronal tuning. PEG population showed reduced onset-related responses, but more categorical change-related modulations. Finally, a subset of FC neurons (dlPFC/premotor) presented a generalized response to all change-related events only during behavior. We show using a Generalized Linear Model (GLM) that the same subpopulation in FC encodes sensory and decision signals, suggesting that FC neurons could operate conversion of sensory evidence to perceptual decision. All together, these area-specific responses suggest a behavior-dependent mechanism of sensory extraction and generalization of task-relevant event. Aleksandar Ivanov Title: How does the auditory system adapt to different environments: A song of echoes and adaptation
Predictive processing in cortical circuits
Cerebral Cortex Connectomics
Densely mapping neuronal circuits at synaptic resolution is providing unprecedented insight into the formation and structure of the cerebral cortex. I’ll present recent advances and discuss what we can learn about precision, plasticity and possible patterns in mammalian neuronal circuits.
The Fabric of the Neocortex
The recruitment of spatial cells in large-scale space & an AI approach to neural discovery
Prof Caswell Barry, Professorial Research Fellow, Cell & Developmental Biology, Division of Biosciences, University College London. He and his team are trying to understand how the brain works - how it creates that experience of being human, and more specifically, how the brain creates, stores, and updates memories for places and events. They are trying to answer this is by studying areas of the brain linked to memory, the hippocampus and associated sections of cortex – by recording the activity of neurons in these areas we can visualise and hopefully understand the processes the trigger memory formation and retrieval.
Playing the piano with the cortex: role of neuronal ensembles and pattern completion in perception
The design of neural circuits, with large numbers of neurons interconnected in vast networks, strongly suggest that they are specifically build to generate emergent functional properties (1). To explore this hypothesis, we have developed two-photon holographic methods to selective image and manipulate the activity of neuronal populations in 3D in vivo (2). Using them we find that groups of synchronous neurons (neuronal ensembles) dominate the evoked and spontaneous activity of mouse primary visual cortex (3). Ensembles can be optogenetically imprinted for several days and some of their neurons trigger the entire ensemble (4). By activating these pattern completion cells in ensembles involved in visual discrimination paradigms, we can bi-directionally alter behavioural choices (5). Our results demonstrate that ensembles are necessary and sufficient for visual perception and are consistent with the possibility that neuronal ensembles are the functional building blocks of cortical circuits. 1. R. Yuste, From the neuron doctrine to neural networks. Nat Rev Neurosci 16, 487-497 (2015). 2. L. Carrillo-Reid, W. Yang, J. E. Kang Miller, D. S. Peterka, R. Yuste, Imaging and Optically Manipulating Neuronal Ensembles. Annu Rev Biophys, 46: 271-293 (2017). 3. J. E. Miller, I. Ayzenshtat, L. Carrillo-Reid, R. Yuste, Visual stimuli recruit intrinsically generated cortical ensembles. Proceedings of the National Academy of Sciences of the United States of America 111, E4053-4061 (2014). 4. L. Carrillo-Reid, W. Yang, Y. Bando, D. S. Peterka, R. Yuste, Imprinting and recalling cortical ensembles. Science 353, 691-694 (2016). 5. L. Carrillo-Reid, S. Han, W. Yang, A. Akrouh, R. Yuste, (2019). Controlling visually-guided behaviour by holographic recalling of cortical ensembles. Cell 178, 447-457. DOI:https://doi.org/10.1016/j.cell.2019.05.045.
Working Memory 2.0
Working memory is the sketchpad of consciousness, the fundamental mechanism the brain uses to gain volitional control over its thoughts and actions. For the past 50 years, working memory has been thought to rely on cortical neurons that fire continuous impulses that keep thoughts “online”. However, new work from our lab has revealed more complex dynamics. The impulses fire sparsely and interact with brain rhythms of different frequencies. Higher frequency gamma (> 35 Hz) rhythms help carry the contents of working memory while lower frequency alpha/beta (~8-30 Hz) rhythms act as control signals that gate access to and clear out working memory. In other words, a rhythmic dance between brain rhythms may underlie your ability to control your own thoughts.