We are seeking a highly motivated and creative postdoctoral researcher to work on a collaborative project between the labs of Yashar Ahmadian at the Computational and Biological Learning Lab (CBL), Department of Engineering (cbl-cambridge.org), and Zoe Kourtzi (www.abg.psychol.cam.ac.uk) at the Psychology Department, both at the University of Cambridge. The project is fully funded by the UKRI BBSRC and investigates the computational principles and circuit mechanisms underlying human visual perceptual learning, particularly the role of adaptative changes in the balance of cortical excitation and inhibition in this kind of learning. We aim to integrate a few lines of research in our labs, exemplified by the following key publications: Y Ahmadian and KD Miller (2021). What is the dynamical regime of cerebral cortex? Neuron 109 (21), 3373-3391. K Jia, ..., Z Kourtzi (2020). Recurrent Processing Drives Perceptual Plasticity. Current Biology 30 (21), 4177-4187. P Frangou, ..., Z Kourtzi (2019). Learning to optimize perceptual decisions through suppressive interactions in the human brain. Nature Communications 10, 474. Y Ahmadian, DB Rubin, KD Miller (2013). Analysis of the stabilized supralinear network. Neural Computation 25, 1994-2037. T Arakaki, GBarello, Y Ahmadian (2019). Inferring neural circuit structure from datasets of heterogeneous tuning curves. PLOS Comp Bio, 15(4): e1006816. The postdoc will be based in CBL, with free access to the Kourtzi lab in the Psychology department.

We are seeking a highly motivated and creative postdoctoral researcher to work on a collaborative project between the labs of Yashar Ahmadian at the Computational and Biological Learning Lab (CBL), Department of Engineering (cbl-cambridge.org), and Zoe Kourtzi (www.abg.psychol.cam.ac.uk) at the Psychology Department, both at the University of Cambridge. The project is fully funded by the UKRI BBSRC and investigates the computational principles and circuit mechanisms underlying human visual perceptual learning, particularly the role of adaptative changes in the balance of cortical excitation and inhibition in this kind of learning. We aim to integrate a few lines of research in our labs, exemplified by the following key publications: Y Ahmadian and KD Miller (2021). What is the dynamical regime of cerebral cortex? Neuron 109 (21), 3373-3391. K Jia, ..., Z Kourtzi (2020). Recurrent Processing Drives Perceptual Plasticity. Current Biology 30 (21), 4177-4187. P Frangou, ..., Z Kourtzi (2019). Learning to optimize perceptual decisions through suppressive interactions in the human brain. Nature Communications 10, 474. Y Ahmadian, DB Rubin, KD Miller (2013). Analysis of the stabilized supralinear network. Neural Computation 25, 1994-2037. T Arakaki, GBarello, Y Ahmadian (2019). Inferring neural circuit structure from datasets of heterogeneous tuning curves. PLOS Comp Bio, 15(4): e1006816. The postdoc will be based in CBL, with free access to the Kourtzi lab in the Psychology department. Apply at:https://www.jobs.ac.uk/job/DBD626/research-assistant-associate-in-computational-neuroscience-fixed-term

We are seeking a highly motivated and creative postdoctoral researcher to work on a collaborative project between the labs of Yashar Ahmadian (https://www.cbl-cambridge.org/ahmadian) at the Computational and Biological Learning Lab (CBL -- https://cbl-cambridge.org, Engineering Department), and Zoe Kourtzi (https://www.abg.psychol.cam.ac.uk/) at the Psychology Department, both at the University of Cambridge. The project is funded by the UKRI BBSRC and investigates the computational principles and circuit mechanisms underlying human visual perceptual learning, particularly the role of adaptive changes in the balance of cortical excitation and inhibition resulting from perceptual learning. We aim to integrate a few lines of research in our labs, exemplified by the following key publications: Y Ahmadian and KD Miller (2021). What is the dynamical regime of cerebral cortex? Neuron 109 (21), 3373-3391. K Jia, ..., Z Kourtzi (2020). Recurrent Processing Drives Perceptual Plasticity. Current Biology 30 (21), 4177-4187. P Frangou, ..., Z Kourtzi (2019). Learning to optimize perceptual decisions through suppressive interactions in the human brain. Nature Communications 10, 474. Y Ahmadian, DB Rubin, KD Miller (2013). Analysis of the stabilized supralinear network. Neural Computation 25, 1994-2037. T Arakaki, GBarello, Y Ahmadian (2019). Inferring neural circuit structure from datasets of heterogeneous tuning curves. PLOS Comp Bio, 15(4): e1006816. The postdoc will be based in CBL, with free access to the Kourtzi lab in the Psychology department.

We are seeking a highly motivated and creative postdoctoral researcher to work on a collaborative project between the labs of Yashar Ahmadian at the Computational and Biological Learning Lab (CBL), Department of Engineering (cbl-cambridge.org), and Zoe Kourtzi (www.abg.psychol.cam.ac.uk) at the Psychology Department, both at the University of Cambridge. The project is fully funded by the UKRI BBSRC and investigates the computational principles and circuit mechanisms underlying human visual perceptual learning, particularly the role of adaptative changes in the balance of cortical excitation and inhibition in this kind of learning. We aim to integrate a few lines of research in our labs, exemplified by the following key publications: Y Ahmadian and KD Miller (2021). What is the dynamical regime of cerebral cortex? Neuron 109 (21), 3373-3391. K Jia, ..., Z Kourtzi (2020). Recurrent Processing Drives Perceptual Plasticity. Current Biology 30 (21), 4177-4187. P Frangou, ..., Z Kourtzi (2019). Learning to optimize perceptual decisions through suppressive interactions in the human brain. Nature Communications 10, 474. Y Ahmadian, DB Rubin, KD Miller (2013). Analysis of the stabilized supralinear network. Neural Computation 25, 1994-2037. T Arakaki, GBarello, Y Ahmadian (2019). Inferring neural circuit structure from datasets of heterogeneous tuning curves. PLOS Comp Bio, 15(4): e1006816. The postdoc will be based in CBL, with free access to the Kourtzi lab in the Psychology department.

I'm happy to announce the Architectures of Biological Learning Lab is Hiring! I'm looking for exceptional candidates at the MSc, PhD or post-doc level to work on "Dendritic Algorithms for Perceptual Learning". The project will employ simulations of pyramidal neurons and plasticity, and deep convolutional networks to study representation learning in the neocortex. Prior experience with Python, NEURON, and/or PyTorch would be an asset. This project will be undertaken in collaboration with Profs. Yoshua Bengio (UdeM, Mila), Roberto Araya (UdeM, CRCHUSJ), and Blake Richards (McGill, Mila). Montreal, Canada is a thriving international hub for Artificial Intelligence and Neuroscience research, with a booming AI industry. It's where Donald O. Hebb originally formulated Hebbian Learning. It's also a vibrant, funky, cosmopolitan yet affordable city of over 4 million, referred to as "Canada's Cultural Capital" by Monocle magazine. In 2017, Montreal was ranked the 12th-most liveable city in the world by the Economist Intelligence Unit in its annual Global Liveability Ranking, and the best city in the world to be a university student in the QS World University Rankings. For more details and instructions how to apply, please visit: https://bit.ly/3l1PGFH

The brain and its inherent functions can be modified by various forms of learning. Learning-induced changes are seen even in basic perceptual functions. In particular, repeated training in a perceptual task can lead to a significant improvement in the trained task—a phenomenon known as perceptual learning. There has been a long-standing debate about the mechanisms of perceptual learning. In this talk, I will present results from our series of electrophysiological studies. These studies have consistently shown that perceptual learning is mediated by concerted changes in both perceptual and cognitive processes, resulting in improved sensory representation, enhanced top-down influences, and refined readout process.

Sleep is crucial for the continuity and development of life. Sleep-related problems can alter brain function, and cause potentially severe psychological and behavioral consequences. However, the role of sleep in our mind and behavior is far from clear. In this talk, I will present our research on how sleep may play a role in visual perceptual learning (VPL) by using simultaneous magnetic resonance spectroscopy and polysomnography in human subjects. We measured the concentrations of neurotransmitters in the early visual areas during sleep and obtained the excitation/inhibition (E/I) ratio which represents the amount of plasticity in the visual system. We found that the E/I ratio significantly increased during NREM sleep while it decreased during REM sleep. The E/I ratio during NREM sleep was correlated with offline performance gains by sleep, while the E/I ratio during REM sleep was correlated with the amount of learning stabilization. These suggest that NREM sleep increases plasticity, while REM sleep decreases it to solidify once enhanced learning. NREM and REM sleep may play complementary roles, reflected by significantly different neurochemical processing, in VPL.

Visual perceptual learning (VPL) is defined as improved performance on a visual task due to visual experience. It was once argued that attention to a visual feature is necessary for VPL of the feature to occur. Contrary to this view, a phenomenon called task-irrelevant VPL demonstrated that VPL can occur due to exposure to a feature which is sub-threshold and task-irrelevant, and therefore, unattended. A series of findings based on task-irrelevant VPL has indicated the following two mechanisms. First, attention to a feature facilitates VPL of the feature while inhibiting VPL of unattended and supra-threshold features. Second, reward paired with a feature enables VPL of the feature irrespective of whether the feature is attended or not. However, we recently found an additional twist; VPL of a task-irrelevant and supra-threshold feature embedded in a natural scene is not subject to the inhibition of attention. This new finding suggests a need to revise the current view or add a new mechanism as to how VPL occurs.

Sensory experience and perceptual learning changes receptive field properties of cortical pyramidal neurons possibly mediated by long-term potentiation (LTP) of synapses. We have previously shown in the mouse somatosensory cortex (S1) that sensory-driven LTP in layer (L) 2/3 pyramidal neurons is dependent on higher order thalamic feedback from the posteromedial nucleus (POm), which is thought to convey contextual information from various cortical regions integrated with sensory input. We have followed up on this work by dissecting the cortical microcircuitry that underlies this form of LTP. We found that repeated pairing of Pom thalamocortical and intracortical pathway activity in brain slices induces NMDAr-dependent LTP of the L2/3 synapses that are driven by the intracortical pathway. Repeated pairing also recruits activity of vasoactive intestinal peptide (VIP) interneurons, whereas it reduces the activity of somatostatin (SST) interneurons. VIP interneuron-mediated inhibition of SST interneurons has been established as a motif for the disinhibition of pyramidal neurons. By chemogenetic interrogation we found that activation of this disinhibitory microcircuit motif by higher-order thalamic feedback is indispensable for eliciting LTP. Preliminary results in vivo suggest that VIP neuron activity also increases during sensory-evoked LTP. Together, this suggests that the higherorder thalamocortical feedback may help modifying the strength of synaptic circuits that process first-order sensory information in S1. To start characterizing the relationship between higher-order feedback and cortical plasticity during learning in vivo, we adapted a perceptual learning paradigm in which head-fixed mice have to discriminate two types of textures in order to obtain a reward. POm axons or L2/3 pyramidal neurons labeled with the genetically encoded calcium indicator GCaMP6s were imaged during the acquisition of this task as well as the subsequent learning of a new discrimination rule. We found that a subpopulation of the POm axons and L2/3 neurons dynamically represent textures. Moreover, upon a change in reward contingencies, a fraction of the L2/3 neurons re-tune their selectivity to the texture that is newly associated with the reward. Altogether, our data indicates that higher-order thalamic feedback can facilitate synaptic plasticity and may be implicated in dynamic sensory stimulus representations in S1, which depends on higher-order features that are associated with the stimuli.

Perceptual learning (PL) is defined as long-term performance improvement on a perceptual task as a result of perceptual experience (Sasaki, Nanez& Watanabe, 2011, Nat Rev Neurosci, 2011). We first found that PL occurs for task-irrelevant and subthreshold features and that pairing task-irrelevant features with rewards is the key to form task-irrelevant PL (TIPL) (Watanabe, Nanez & Sasaki, Nature, 2001; Watanabe et al, 2002, Nature Neuroscience; Seitz & Watanabe, Nature, 2003; Seitz, Kim & Watanabe, 2009, Neuron; Shibata et al, 2011, Science). These results suggest that PL occurs as a result of interactions between reinforcement and bottom-up stimulus signals (Seitz & Watanabe, 2005, TICS). On the other hand, fMRI study results indicate that lateral prefrontal cortex fails to detect and thus to suppress subthreshold task-irrelevant signals. This leads to the paradoxical effect that a signal that is below, but close to, one’s discrimination threshold ends up being stronger than suprathreshold signals (Tsushima, Sasaki & Watanabe, 2006, Science). We confirmed this mechanism with the following results: Task-irrelevant learning occurs only when a presented feature is under and close to the threshold with younger individuals (Tsushima et al, 2009, Current Biol), whereas with older individuals who tend to have less inhibitory control task-irrelevant learning occurs with a feature whose signal is much greater than the threshold (Chang et al, 2014, Current Biol). From all of these results, we conclude that attention and reward play important but different roles in PL. I will further discuss different stages and phases in mechanisms of PL (Seitz et al, 2005, PNAS; Yotsumoto, Watanabe & Sasaki, Neuron, 2008; Yotsumoto et al, Curr Biol, 2009; Watanabe & Sasaki, 2015, Ann Rev Psychol; Shibata et al, 2017, Nat Neurosci; Tamaki et al, 2020, Nat Neurosci).

To uncover the circuit-level alterations that underlie atypical sensory processing associated with autism, we have adopted a symptom-to-circuit approach in theFmr1-/- mouse model of Fragile X syndrome (FXS). Using a go/no-go task and in vivo 2-photon calcium imaging, we find that impaired visual discrimination in Fmr1-/- mice correlates with marked deficits in orientation tuning of principal neurons in primary visual cortex, and a decrease in the activity of parvalbumin (PV) interneurons. Restoring visually evoked activity in PV cells in Fmr1-/- mice with a chemogenetic (DREADD) strategy was sufficient to rescue their behavioural performance. Strikingly, human subjects with FXS exhibit similar impairments in visual discrimination as Fmr1-/- mice. These results suggest that manipulating inhibition may help sensory processing in FXS. More recently, we find that the ability of Fmr1-/- mice to perform the visual discrimination task is also drastically impaired in the presence of visual or auditory distractors, suggesting that sensory hypersensitivity may affect perceptual learning in autism.

Single neurons in visual cortex provide unreliable measurements of visual features due to their high trial-to-trial variability. It is not known if this “noise” extends its effects over large neural populations to impair the global encoding of stimuli. We recorded simultaneously from ∼20,000 neurons in mouse primary visual cortex (V1) and found that the neural populations had discrimination thresholds of ∼0.34° in an orientation decoding task. These thresholds were nearly 100 times smaller than those reported behaviourally in mice. The discrepancy between neural and behavioural discrimination could not be explained by the types of stimuli we used, by behavioural states or by the sequential nature of perceptual learning tasks. Furthermore, higher-order visual areas lateral to V1 could be decoded equally well. These results imply that the limits of sensory perception in mice are not set by neural noise in sensory cortex, but by the limitations of downstream decoders.