Neuroscience seminars

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).

Internal organs constantly exchange signals, and can respond with striking anatomical and functional transformations, even in fully developed organisms. We are exploring the mechanisms that drive and sustain such plasticity using the intestine and its neurons as experimental systems. I will present some of our recent work, which has characterised the enteric nervous system of Drosophila, and has explored its physiological plasticity as well as that of the intestine itself. This work has uncovered unexpected sexual dimorphisms, intestinal contributions to reproductive success and metabolic crosstalk between the gut and the brain. Interestingly, this crosstalk appears to be spatially constrained by the three dimensional arrangement of viscera, revealing a previously unrecognised layer of inter-organ signalling regulation. I may also describe our attempts to explore how broadly applicable our findings may be using mammalian systems.

Using sounds for social interactions is common across many taxa. Humans engaged in conversation, for example, take rapid turns to go back and forth. This ability to act upon sensory information to generate a desired motor output is a fundamental feature of animal behavior. How the brain enables such flexible sensorimotor transformations, for example during vocal interactions, is a central question in neuroscience. Seeking a rodent model to fill this niche, we are investigating neural mechanisms of vocal interaction in Alston’s singing mouse (Scotinomys teguina) – a neotropical rodent native to the cloud forests of Central America. We discovered sub-second temporal coordination of advertisement songs (counter-singing) between males of this species – a behavior that requires the rapid modification of motor outputs in response to auditory cues. We leveraged this natural behavior to probe the neural mechanisms that generate and allow fast and flexible vocal communication. Using causal manipulations, we recently showed that an orofacial motor cortical area (OMC) in this rodent is required for vocal interactions (Okobi*, Banerjee* et. al, 2019). Subsequently, in electrophysiological recordings, I find neurons in OMC that track initiation, termination and relative timing of songs. Interestingly, persistent neural dynamics during song progression stretches or compresses on every trial to match the total song duration (Banerjee et al, in preparation). These results demonstrate robust cortical control of vocal timing in a rodent and upends the current dogma that motor cortical control of vocal output is evolutionarily restricted to the primate lineage.

Spontaneous firing, observed in many neurons, is often attributed to ion channel or network level noise. Cortical cells during slow wave sleep exhibit transitions between so called Up and Down states. In this sleep state, with limited sensory stimuli, neurons fire in the Up state. Spontaneous firing is also observed in slices of cholinergic interneurons, cerebellar Purkinje cells and even brainstem inspiratory neurons. In such in vitro preparations, where the functional relevance is long lost, neurons continue to display a rich repertoire of firing properties. It is perplexing that these neurons, instead of saving their energy during information downtime and functional irrelevance, are eager to fire. We propose that spontaneous firing is not a chance event but instead, a vital activity for the well-being of a neuron. We postulate that neurons, in anticipation of synaptic inputs, keep their ATP levels at maximum. As recovery from inputs requires most of the energy resources, neurons are ATP surplus and ADP scarce during synaptic quiescence. With ADP as the rate-limiting step, ATP production stalls in the mitochondria when ADP is low. This leads to toxic Reactive Oxygen Species (ROS) formation, which are known to disrupt many cellular processes. We hypothesize that spontaneous firing occurs at these conditions - as a release valve to spend energy and to restore ATP production, shielding the neuron against ROS. By linking a mitochondrial metabolism model to a conductance-based neuron model, we show that spontaneous firing depends on baseline ATP usage and on ATP-cost-per-spike. From our model, emerges a mitochondrial mediated homeostatic mechanism that provides a recipe for different firing patterns. Our findings, though mostly affecting intracellular dynamics, may have large knock-on effects on the nature of neural coding. Hitherto it has been thought that the neural code is optimised for energy minimisation, but this may be true only when neurons do not experience synaptic quiescence.

Seizure onset is a critically important brain state transition that has proved very difficult to predict accurately from recordings of brain activity. I will present new data acquired using a range of optogenetic and imaging tools to characterize exactly how cortical networks change in the build-up to a seizure. I will show how intermittent optogenetic stimulation ("active probing") reveals a latent change in dendritic excitability that is tightly correlated to the onset of seizure activity. This data relates back to old work from the 1980s suggesting a critical role in epileptic pathophysiology for dendritic plateau potentials. Our data show how the precipitous nature of the transition can be understood in terms of multiple, synergistic positive feedback mechanisms.

Drug addiction is a chronically relapsing disorder characterized by compulsive drug use despite catastrophic personal consequences (e.g., loss of family, job) and even when the substance is no longer perceived as pleasurable. In this talk, I will present results of human neuroimaging studies, utilizing a multimodal approach (neuropsychology, functional magnetic resonance imaging, event-related potentials recordings), to explore the neurobiology underlying the core psychological impairments in drug addiction (impulsivity, drive/motivation, insight/awareness) as associated with its clinical symptomatology (intoxication, craving, bingeing, withdrawal). The focus of this talk is on understanding the role of the dopaminergic mesocorticolimbic circuit, and especially the prefrontal cortex, in higher-order executive dysfunction (e.g., disadvantageous decision-making such as trading a car for a couple of cocaine hits) in drug addicted individuals. The theoretical model that guides the presented research is called iRISA (Impaired Response Inhibition and Salience Attribution), postulating that abnormalities in the orbitofrontal cortex and anterior cingulate cortex, as related to dopaminergic dysfunction, contribute to the core clinical symptoms in drug addiction. Specifically, our multi-modality program of research is guided by the underlying working hypothesis that drug addicted individuals disproportionately attribute reward value to their drug of choice at the expense of other potentially but no-longer-rewarding stimuli, with a concomitant decrease in the ability to inhibit maladaptive drug use. In this talk I will also explore whether treatment (as usual) and 6-month abstinence enhance recovery in these brain-behavior compromises in treatment seeking cocaine addicted individuals. Promising neuroimaging studies, which combine pharmacological (i.e., oral methylphenidate, or RitalinTM) and salient cognitive tasks or functional connectivity during resting-state, will be discussed as examples for using neuroimaging for empirically guiding the development of effective neurorehabilitation strategies (encompassing cognitive reappraisal and transcranial direct current stimulation) in drug addiction.

Engineered pattern-recognition systems strive for short time-to-solution and low energy-to-solution characteristics. This represents one of the main driving forces behind the development of neuromorphic devices. For both them and their biological archetypes, this corresponds to using as few spikes as early as possible. The concept of few and early spikes is used as the founding principle in the time-to-first-spike coding scheme. Within this framework, we have developed a spike-timing-based learning algorithm, which we used to train neuronal networks on the mixed-signal neuromorphic platform BrainScaleS-2. We derive, from first principles, error-backpropagation-based learning in networks of leaky integrate-and-fire (LIF) neurons relying only on spike times, for specific configurations of neuronal and synaptic time constants. We explicitly examine applicability to neuromorphic substrates by studying the effects of reduced weight precision and range, as well as of parameter noise. We demonstrate the feasibility of our approach on continuous and discrete data spaces, both in software simulations and on BrainScaleS-2. This narrows the gap between previous models of first-spike-time learning and biological neuronal dynamics and paves the way for fast and energy-efficient neuromorphic applications.

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well-established that it depends on pre and postsynaptic activity. However, models that rely solely on pre and postsynaptic activity for synaptic changes have, to date, not been able to account for learning complex tasks that demand hierarchical networks. Here, we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then neurons higher in the hierarchy can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites, and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses, and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits.

The timing of individual neuronal spikes is essential for biological brains to make fast responses to sensory stimuli. However, conventional artificial neural networks lack the intrinsic temporal coding ability present in biological networks. We propose a spiking neural network model that encodes information in the relative timing of individual neuron spikes. In classification tasks, the output of the network is indicated by the first neuron to spike in the output layer. This temporal coding scheme allows the supervised training of the network with backpropagation, using locally exact derivatives of the postsynaptic spike times with respect to presynaptic spike times. The network operates using a biologically-plausible alpha synaptic transfer function. Additionally, we use trainable synchronisation pulses that provide bias, add flexibility during training and exploit the decay part of the alpha function. We show that such networks can be trained successfully on noisy Boolean logic tasks and on the MNIST dataset encoded in time. The results show that the spiking neural network outperforms comparable spiking models on MNIST and achieves similar quality to fully connected conventional networks with the same architecture. We also find that the spiking network spontaneously discovers two operating regimes, mirroring the accuracy-speed trade-off observed in human decision-making: a slow regime, where a decision is taken after all hidden neurons have spiked and the accuracy is very high, and a fast regime, where a decision is taken very fast but the accuracy is lower. These results demonstrate the computational power of spiking networks with biological characteristics that encode information in the timing of individual neurons. By studying temporal coding in spiking networks, we aim to create building blocks towards energy-efficient and more complex biologically-inspired neural architectures.

Cases with visuospatial abnormalities provide opportunities for understanding the underlying cognitive mechanisms. Three cases of visual mirror-reversal have been reported: AH (McCloskey, 2009), TM (McCloskey, Valtonen, & Sherman, 2006) and PR (Pflugshaupt et al., 2007). This research reports a fourth case, BS -- with focal occipital cortical dysgenesis -- who displays highly unusual visuospatial abnormalities. They initially produced mirror reversal errors similar to those of AH, who -- like the patient in question -- showed a selective developmental deficit. Extensive examination of BS revealed phenomena such as: mirror reversal errors (sometimes affecting only parts of the visual fields) in both horizontal and vertical planes; subjective representation of visual objects and words in distinct left and right visual fields; subjective duplication of objects of visual attention (not due to diplopia); uncertainty regarding the canonical upright orientation of everyday objects; mirror reversals during saccadic eye movements on oculomotor tasks; and failure to integrate visual with other sensory inputs (e.g., they feel themself moving backwards when visual information shows they are moving forward). Fewer errors are produced under conditions of certain visual variables. These and other findings have led the researchers to conclude that BS draws upon a subjective representation of visual space that is structured phenomenally much as it is anatomically in early visual cortex (i.e., rotated through 180 degrees, split into left and right fields, etc.). Despite this, BS functions remarkably well in their everyday life, apparently due to extensive compensatory mechanisms deployed at higher (executive) processing levels beyond the visual modality.

The world confronts our senses with a continuous stream of rapidly changing information. Yet, we experience life as a series of episodes or events, and in memory these pieces seem to become even further organized. How do we recall and give structure to this complex information? Recent studies have begun to examine these questions using naturalistic stimuli and behavior: subjects view audiovisual movies and then freely recount aloud their memories of the events. We find brain activity patterns that are unique to individual episodes, and which reappear during verbal recollection; robust generalization of these patterns across people; and memory effects driven by the structure of links between events in a narrative. These findings construct a picture of how we comprehend and recall real-world events that unfold continuously across time.