Excitatory Input
excitatory input
The circadian clock and neural circuits maintaining body fluid homeostasis
Neurons in the suprachiasmatic nucleus (SCN, the brain’s master circadian clock) display a 24 hour cycle in the their rate of action potential discharge whereby firing rates are high during the light phase and lower during the dark phase. Although it is generally agreed that this cycle of activity is a key mediator of the clock’s neural and humoral output, surprisingly little is known about how changes in clock electrical activity can mediate scheduled physiological changes at different times of day. Using opto- and chemogenetic approaches in mice we have shown that the onset of electrical activity in vasopressin releasing SCN neurons near Zeitgeber time 22 (ZT22) activates glutamatergic thirst-promoting neurons in the OVLT (organum vasculosum lamina terminalis) to promote water intake prior to sleep. This effect is mediated by activity-dependent release of vasopressin from the axon terminals of SCN neurons which acts as a neurotransmitter on OVLT neurons. More recently we found that the clock receives excitatory input from a different subset of sodium sensing neurons in the OVLT. Activation of these neurons by a systemic salt load delivered at ZT19 stimulated the electrical activity of SCN neurons which are normally silent at this time. Remarkably, this effect induced an acute reduction in non-shivering thermogenesis and body temperature, which is an adaptive response to the salt load. These findings provide information regarding the mechanisms by which the SCN promotes scheduled physiological rhythms and indicates that the clock’s output circuitry can also be recruited to mediate an unscheduled homeostatic response.
Nonlinear spatial integration in retinal bipolar cells shapes the encoding of artificial and natural stimuli
Vision begins in the eye, and what the “retina tells the brain” is a major interest in visual neuroscience. To deduce what the retina encodes (“tells”), computational models are essential. The most important models in the retina currently aim to understand the responses of the retinal output neurons – the ganglion cells. Typically, these models make simplifying assumptions about the neurons in the retinal network upstream of ganglion cells. One important assumption is linear spatial integration. In this talk, I first define what it means for a neuron to be spatially linear or nonlinear and how we can experimentally measure these phenomena. Next, I introduce the neurons upstream to retinal ganglion cells, with focus on bipolar cells, which are the connecting elements between the photoreceptors (input to the retinal network) and the ganglion cells (output). This pivotal position makes bipolar cells an interesting target to study the assumption of linear spatial integration, yet due to their location buried in the middle of the retina it is challenging to measure their neural activity. Here, I present bipolar cell data where I ask whether the spatial linearity holds under artificial and natural visual stimuli. Through diverse analyses and computational models, I show that bipolar cells are more complex than previously thought and that they can already act as nonlinear processing elements at the level of their somatic membrane potential. Furthermore, through pharmacology and current measurements, I illustrate that the observed spatial nonlinearity arises at the excitatory inputs to bipolar cells. In the final part of my talk, I address the functional relevance of the nonlinearities in bipolar cells through combined recordings of bipolar and ganglion cells and I show that the nonlinearities in bipolar cells provide high spatial sensitivity to downstream ganglion cells. Overall, I demonstrate that simple linear assumptions do not always apply and more complex models are needed to describe what the retina “tells” the brain.
Deriving local synaptic learning rules for efficient representations in networks of spiking neurons
How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that input weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity only works under specific requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, inhibition learns to locally balance excitatory input in individual dendritic compartments, and thereby can modulate excitatory synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex, high-dimensional and correlated inputs. It also works in the presence of inhibitory transmission delays, where Hebbian-like plasticity typically fails. Our results draw a direct connection between dendritic excitatory-inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo, and suggest that both are crucial for representation learning.
A brain circuit for curiosity
Motivational drives are internal states that can be different even in similar interactions with external stimuli. Curiosity as the motivational drive for novelty-seeking and investigating the surrounding environment is for survival as essential and intrinsic as hunger. Curiosity, hunger, and appetitive aggression drive three different goal-directed behaviors—novelty seeking, food eating, and hunting— but these behaviors are composed of similar actions in animals. This similarity of actions has made it challenging to study novelty seeking and distinguish it from eating and hunting in nonarticulating animals. The brain mechanisms underlying this basic survival drive, curiosity, and novelty-seeking behavior have remained unclear. In spite of having well-developed techniques to study mouse brain circuits, there are many controversial and different results in the field of motivational behavior. This has left the functions of motivational brain regions such as the zona incerta (ZI) still uncertain. Not having a transparent, nonreinforced, and easily replicable paradigm is one of the main causes of this uncertainty. Therefore, we chose a simple solution to conduct our research: giving the mouse freedom to choose what it wants—double freeaccess choice. By examining mice in an experimental battery of object free-access double-choice (FADC) and social interaction tests—using optogenetics, chemogenetics, calcium fiber photometry, multichannel recording electrophysiology, and multicolor mRNA in situ hybridization—we uncovered a cell type–specific cortico-subcortical brain circuit of the curiosity and novelty-seeking behavior. We found in mice that inhibitory neurons in the medial ZI (ZIm) are essential for the decision to investigate an object or a conspecific. These neurons receive excitatory input from the prelimbic cortex to signal the initiation of exploration. This signal is modulated in the ZIm by the level of investigatory motivation. Increased activity in the ZIm instigates deep investigative action by inhibiting the periaqueductal gray region. A subpopulation of inhibitory ZIm neurons expressing tachykinin 1 (TAC1) modulates the investigatory behavior.
A fresh look at the bird retina
I am working on the vertebrate retina, with a main focus on the mouse and bird retina. Currently my work is focused on three major topics: Functional and molecular analysis of electrical synapses in the retina Circuitry and functional role of retinal interneurons: horizontal cells Circuitry for light-dependent magnetoreception in the bird retina Electrical synapses Electrical synapses (gap junctions) permit fast transmission of electrical signals and passage of metabolites by means of channels, which directly connect the cytoplasm of adjoining cells. A functional gap junction channel consists of two hemichannels (one provided by each of the cells), each comprised of a set of six protein subunits, termed connexins. These building blocks exist in a variety of different subtypes, and the connexin composition determines permeability and gating properties of a gap junction channel, thereby enabling electrical synapses to meet a diversity of physiological requirements. In the retina, various connexins are expressed in different cell types. We study the cellular distribution of different connexins as well as the modulation induced by transmitter action or change of ambient light levels, which leads to altered electrical coupling properties. We are also interested in exploiting them as therapeutic avenue for retinal degeneration diseases. Horizontal cells Horizontal cells receive excitatory input from photoreceptors and provide feedback inhibition to photoreceptors and feedforward inhibition to bipolar cells. Because of strong electrical coupling horizontal cells integrate the photoreceptor input over a wide area and are thought to contribute to the antagonistic organization of bipolar cell and ganglion cell receptive fields and to tune the photoreceptor–bipolar cell synapse with respect to the ambient light conditions. However, the extent to which this influence shapes retinal output is unclear, and we aim to elucidate the functional importance of horizontal cells for retinal signal processing by studying various transgenic mouse models. Retinal circuitry for light-dependent magnetoreception in the bird We are studying which neuronal cell types and pathways in the bird retina are involved in the processing of magnetic signals. Likely, magnetic information is detected in cryptochrome-expressing photoreceptors and leaves the retina through ganglion cell axons that project via the thalamofugal pathway to Cluster N, a part of the visual wulst essential for the avian magnetic compass. Thus, we aim to elucidate the synaptic connections and retinal signaling pathways from putatively magnetosensitive photoreceptors to thalamus-projecting ganglion cells in migratory birds using neuroanatomical and electrophysiological techniques.
Distinct limbic-hypothalamic circuits for the generation of social behaviors
The main pillars of social behaviors involve (1) mating, where males copulate with female partners to reproduce, and (2) aggression, where males fight conspecific male competitors in territory guarding. Decades of study have identified two key regions in the hypothalamus, the medial preoptic nucleus (MPN) and the ventrolateral part of ventromedial hypothalamus (VMHvl) , that are essential for male sexual and aggressive behaviors, respectively. However, it remains ambiguous what area directs excitatory control of the hypothalamic activity and generates the initiation signal for social behaviors. Through neural tracing, in vivo optical recording and functional manipulations, we identified the estrogen receptor alpha (Esr1)-expressing cells in the posterior amygdala (PA) as a main source of excitatory inputs to the MPN and VMHvl, and key hubs in mating and fighting circuits in males. Importantly, two spatially-distinct populations in the PA regulate male sexual and aggressive behaviors, respectively. Moreover, these two subpopulations in the PA display differential molecular phenotypes, projection patterns and in vivo neural responses. Our work also observed the parallels between these social behavior circuits and basal ganglia circuits to control motivated behaviors, which Larry Swanson (2000) originally proposed based on extensive developmental and anatomical evidence.
How Brain Circuits Function in Health and Disease: Understanding Brain-wide Current Flow
Dr. Rajan and her lab design neural network models based on experimental data, and reverse-engineer them to figure out how brain circuits function in health and disease. They recently developed a powerful framework for tracing neural paths across multiple brain regions— called Current-Based Decomposition (CURBD). This new approach enables the computation of excitatory and inhibitory input currents that drive a given neuron, aiding in the discovery of how entire populations of neurons behave across multiple interacting brain regions. Dr. Rajan’s team has applied this method to studying the neural underpinnings of behavior. As an example, when CURBD was applied to data gathered from an animal model often used to study depression- and anxiety-like behaviors (i.e., learned helplessness) the underlying biology driving adaptive and maladaptive behaviors in the face of stress was revealed. With this framework Dr. Rajan's team probes for mechanisms at work across brain regions that support both healthy and disease states-- as well as identify key divergences from multiple different nervous systems, including zebrafish, mice, non-human primates, and humans.
Untangling brain wide current flow using neural network models
Rajanlab designs neural network models constrained by experimental data, and reverse engineers them to figure out how brain circuits function in health and disease. Recently, we have been developing a powerful new theory-based framework for “in-vivo tract tracing” from multi-regional neural activity collected experimentally. We call this framework CURrent-Based Decomposition (CURBD). CURBD employs recurrent neural networks (RNNs) directly constrained, from the outset, by time series measurements acquired experimentally, such as Ca2+ imaging or electrophysiological data. Once trained, these data-constrained RNNs let us infer matrices quantifying the interactions between all pairs of modeled units. Such model-derived “directed interaction matrices” can then be used to separately compute excitatory and inhibitory input currents that drive a given neuron from all other neurons. Therefore different current sources can be de-mixed – either within the same region or from other regions, potentially brain-wide – which collectively give rise to the population dynamics observed experimentally. Source de-mixed currents obtained through CURBD allow an unprecedented view into multi-region mechanisms inaccessible from measurements alone. We have applied this method successfully to several types of neural data from our experimental collaborators, e.g., zebrafish (Deisseroth lab, Stanford), mice (Harvey lab, Harvard), monkeys (Rudebeck lab, Sinai), and humans (Rutishauser lab, Cedars Sinai), where we have discovered both directed interactions brain wide and inter-area currents during different types of behaviors. With this powerful framework based on data-constrained multi-region RNNs and CURrent Based Decomposition (CURBD), we ask if there are conserved multi-region mechanisms across different species, as well as identify key divergences.
Interacting synapses stabilise both learning and neuronal dynamics in biological networks
Distinct synapses influence one another when they undergo changes, with unclear consequences for neuronal dynamics and function. Here we show that synapses can interact such that excitatory currents are naturally normalised and balanced by inhibitory inputs. This happens when classical spike-timing dependent synaptic plasticity rules are extended by additional mechanisms that incorporate the influence of neighbouring synaptic currents and regulate the amplitude of efficacy changes accordingly. The resulting control of excitatory plasticity by inhibitory activation, and vice versa, gives rise to quick and long-lasting memories as seen experimentally in receptive field plasticity paradigms. In models with additional dendritic structure, we observe experimentally reported clustering of co-active synapses that depends on initial connectivity and morphology. Finally, in recurrent neural networks, rich and stable dynamics with high input sensitivity emerge, providing transient activity that resembles recordings from the motor cortex. Our model provides a general framework for codependent plasticity that frames individual synaptic modifications in the context of population-wide changes, allowing us to connect micro-level physiology with behavioural phenomena.
Inferring brain-wide current flow using data-constrained neural network models
Rajanlab designs neural network models constrained by experimental data, and reverse engineers them to figure out how brain circuits function in health and disease. Recently, we have been developing a powerful new theory-based framework for “in-vivo tract tracing” from multi-regional neural activity collected experimentally. We call this framework CURrent-Based Decomposition (CURBD). CURBD employs recurrent neural networks (RNNs) directly constrained, from the outset, by time series measurements acquired experimentally, such as Ca2+ imaging or electrophysiological data. Once trained, these data-constrained RNNs let us infer matrices quantifying the interactions between all pairs of modeled units. Such model-derived “directed interaction matrices” can then be used to separately compute excitatory and inhibitory input currents that drive a given neuron from all other neurons. Therefore different current sources can be de-mixed – either within the same region or from other regions, potentially brain-wide – which collectively give rise to the population dynamics observed experimentally. Source de-mixed currents obtained through CURBD allow an unprecedented view into multi-region mechanisms inaccessible from measurements alone. We have applied this method successfully to several types of neural data from our experimental collaborators, e.g., zebrafish (Deisseroth lab, Stanford), mice (Harvey lab, Harvard), monkeys (Rudebeck lab, Sinai), and humans (Rutishauser lab, Cedars Sinai), where we have discovered both directed interactions brain wide and inter-area currents during different types of behaviors. With this framework based on data-constrained multi-region RNNs and CURrent Based Decomposition (CURBD), we can ask if there are conserved multi-region mechanisms across different species, as well as identify key divergences.
Short-term plasticity profiles diversity of excitatory inputs on cerebellar nuclei neurons
FENS Forum 2024