Neural circuit flexibility in a small sensorimotor system

Neuronal circuits underlying rhythmic behaviors (central pattern generators: CPGs) can generate rhythmic motor output without sensory input. However, sensory input is pivotal for generating behaviorally relevant CPG output. Here we discuss recent work in the decapod crustacean stomatogastric nervous system (STNS) identifying cellular and synaptic mechanisms whereby sensory inputs select particular motor outputs from CPG circuits. This includes several examples in which sensory neurons regulate the impact of descending projection neurons on CPG circuits. This level of analysis is possible in the STNS due to the relatively unique access to identified circuit, projection, and sensory neurons. These studies are also revealing additional degrees of freedom in sensorimotor integration that underlie the extensive flexibility intrinsic to rhythmic motor systems.     

Learning to hear: plasticity of auditory cortical processing

Sensory experience and auditory cortex plasticity are intimately related. This relationship is most striking during infancy when changes in sensory input can have profound effects on the functional organization of the developing cortex. But a considerable degree of plasticity is retained throughout life, as demonstrated by the cortical reorganization that follows damage to the sensory periphery or by the more controversial changes in response properties that are thought to accompany perceptual learning. Recent studies in the auditory system have revealed the remarkably adaptive nature of sensory processing and provided important insights into the way in which cortical circuits are shaped by experience and learning.     

A comparison of experience-dependent plasticity in the visual and somatosensory systems

In the visual and somatosensory systems, maturation of neuronal circuits continues for days to weeks after sensory stimulation occurs. Deprivation of sensory input at various stages of development can induce physiological, and often structural, changes that modify the circuitry of these sensory systems. Recent studies also reveal a surprising degree of plasticity in the mature visual and somatosensory pathways. Here, we compare and contrast the effects of sensory experience on the connectivity and function of these pathways and discuss what is known to date concerning the structural, physiological, and molecular mechanisms underlying their plasticity.     

Songbird: a unique animal model for studying the molecular basis of disorders of vocal development and communication

Like humans, songbirds are one of the few animal groups that learn vocalization. Vocal learning requires coordination of auditory input and vocal output using auditory feedback to guide one’s own vocalizations during a specific developmental stage known as the critical period. Songbirds are good animal models for understand the neural basis of vocal learning, a complex form of imitation, because they have many parallels to humans with regard to the features of vocal behavior and neural circuits dedicated to vocal learning. In this review, we will summarize the behavioral, neural, and genetic traits of birdsong. We will also discuss how studies of birdsong can help us understand how the development of neural circuits for vocal learning and production is driven by sensory input (auditory information) and motor output (vocalization).     

Computing with neural synchrony

Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations. However, it is unclear what role synchrony may play in neural computation, and what functional advantage it may provide. With a theoretical approach, I show that the computational interest of neural synchrony appears when neurons have heterogeneous properties. In this context, the relationship between stimuli and neural synchrony is captured by the concept of synchrony receptive field, the set of stimuli which induce synchronous responses in a group of neurons. In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties.     

Pattern orthogonalization via channel decorrelation by adaptive networks

The early processing of sensory information by neuronal circuits often includes a reshaping of activity patterns that may facilitate further processing in the brain. For instance, in the olfactory system the activity patterns that related odors evoke at the input of the olfactory bulb can be highly similar. Nevertheless, the corresponding activity patterns of the mitral cells, which represent the output of the olfactory bulb, can differ significantly from each other due to strong inhibition by granule cells and peri-glomerular cells. Motivated by these results we study simple adaptive inhibitory networks that aim to separate or even orthogonalize activity patterns representing similar stimuli. Since the animal experiences the different stimuli at different times it is difficult for the network to learn the connectivity based on their similarity; biologically it is more plausible that learning is driven by simultaneous correlations between the input channels. We investigate the connection between pattern orthogonalization and channel decorrelation and demonstrate that networks can achieve effective pattern orthogonalization through channel decorrelation if they simultaneously equalize their output levels. In feedforward networks biophysically plausible learning mechanisms fail, however, for even moderately similar input patterns. Recurrent networks do not have that limitation; they can orthogonalize the representations of highly similar input patterns. Even when they are optimized for linear neuronal dynamics they perform very well when the dynamics are nonlinear. These results provide insights into fundamental features of simplified inhibitory networks that may be relevant for pattern orthogonalization by neuronal circuits in general.     

Forward and Backward Inference in Spatial Cognition

This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of ‘lower-level’ computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus.     

Evolution of sensory systems

Sensory systems evolve and enable organisms to perceive their sensory Umwelt, the unique set of cues relevant for their survival. The multiple components that comprise sensory systems — the receptors, cells, organs, and dedicated high-order circuits — can vary greatly across species. Sensory receptor gene families can expand and contract across lineages, resulting in enormous sensory diversity. Comparative studies of sensory receptor function have uncovered the molecular basis of receptor properties and identified novel sensory receptor classes and noncanonical sensory strategies. Phylogenetically informed comparisons of sensory systems across multiple species can pinpoint when sensory changes evolve and highlight the role of contingency in sensory system evolution.     

From Chloroplasts to Chaperones: How One Thing Led to Another

Two lessons I have learned during my research career are the importance of following up unexpected observations and realizing that the most obvious interpretation of such observations can be rational but wrong. When you carry out an experiment there is usually an expectation that the result will fall within a range of predictable outcomes, and it is natural to feel pleased when this turns out to be the case. In my view this response is a mistake. What you should be hoping for is a puzzling result that was not anticipated since with persistence and luck further experiments may uncover something new. In this article I give a personal account of how studies of the synthesis of proteins by isolated intact chloroplasts from pea leaves eventually led to the discovery of the chaperonins and the formulation of the general concept of the molecular chaperone function that is now seen to be a fundamental aspect of how all cells work.     

A nonlinear systems approach to Fechner's Paradox

It is possible to predict the topology of isointensity plots under conditions of extreme imbalance of the stimulus inputs, without making any assumptions specific to the circumstances in which Fechner's Paradox is sometimes observed. This is done by extending a nonlinear model for a sensory channel, by postulating a form of cross-coupling or interference between two channels which represents other phenomena in psychophysics. It is noted that the form in which data are usually reported is not an adequate basis for testing all the predictions of a nonlinear model in sensory psychophysics. The physiologist Panum (1958), and later Fechner (1860) reported that the apparent brightness of an object viewed binocularly could, under conditions where the input to one eye was diminished by filtering, be less than its brightness viewed monocularly by the unfiltered eye. To a first approximation, binocular brightness is more like an averaging of two monocular inputs than a summation of those same inputs. For over 120 years this phenomenon, which came to be called Fechner's Paradox, though Panum should perhaps have had some credit, has been the subject of experimental investigation and associated mathematical modelling. If one consults a dictionary of psychological terms, for example Evans (1978), one may read something like Fechner's Paradox: The name give to the observation that something [which is] viewed binocularly seems to increase in brightness when one eye is closed. And yet we now know that this definition is misleading, because the same phenomenon in pooling two sensory inputs has its analogues in audition (Lehky 1983) and in olfaction (Gregson 1986). Gilchrist and McIver (1985) have now shown an analogue of the paradox exists in ocular contrast sensitivity. The definition also goes awry when the input luminance to one eye is zero, or when the luminance and ocular adaptation are closely matched for the two eyes. It is wiser, in the light of results reporting individual differences in the existence and extent of the paradox, and its sensitivity to stimulus conditions, to side with Blake and Fox (1973) when they observed that it is not unreasonable to suppose that various stimulus conditions might yield varying amounts of summation or even inhibition. Empirical reviews of relevant data in vision have been given by Roelofs and Zeeman (1914), Blake and Fox (1973), and Blake et al. (1981), but a theoretical model of interest as a starting point is that of Lehky (1983).     

Olfactory map formation in the Drosophila brain: genetic specificity and neuronal variability

The development of the Drosophila olfactory system is a striking example of how genetic programs specify a large number of different neuron types and assemble them into functional circuits. To ensure precise odorant perception, each sensory neuron has to not only select a single olfactory receptor (OR) type out of a large genomic repertoire but also segregate its synaptic connections in the brain according to the OR class identity. Specification and patterning of second-order interneurons in the olfactory brain center occur largely independent of sensory input, followed by a precise point-to-point matching of sensory and relay neurons. Here we describe recent progress in the understanding of how cell-intrinsic differentiation programs and context-dependent cellular interactions generate a stereotyped sensory map in the Drosophila brain. Recent findings revealed an astonishing morphological diversity among members of the same interneuron class, suggesting an unexpected variability in local microcircuits involved in insect sensory processing.     

Mixed Signal Learning by Spike Correlation Propagation in Feedback Inhibitory Circuits

The brain can learn and detect mixed input signals masked by various types of noise, and spike-timing-dependent plasticity (STDP) is the candidate synaptic level mechanism. Because sensory inputs typically have spike correlation, and local circuits have dense feedback connections, input spikes cause the propagation of spike correlation in lateral circuits; however, it is largely unknown how this secondary correlation generated by lateral circuits influences learning processes through STDP, or whether it is beneficial to achieve efficient spike-based learning from uncertain stimuli. To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve. We derive analytical conditions at which neurons detect minor signals with STDP, and show that depending on the origin of the noise, different correlation timescales are useful for learning. In particular, we show that non-precise spike correlation is beneficial for learning in the presence of cross-talk noise. We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. In addition, we demonstrate that the model performs blind source separation in a manner similar to the sequential sampling approximation of the Bayesian independent component analysis algorithm. Our results provide a basic understanding of STDP learning in feedback circuits by integrating analyses from both dynamical systems and information theory.     

The knowledge used in vision and where it comes from.

Knowledge is often thought to be something brought from outside to act upon the visual messages received from the eye in a ''top-down'' fashion, but this is a misleadingly narrow view. First, the visual system is a multilevel heterarchy with connections acting in all directions so it has no ''top''; and second, knowledge is provided through innately determined structure and by analysis of the redundancy in sensory messages themselves, as well as from outside. This paper gives evidence about mechanisms analysing sensory redundancy in biological vision. Automatic gain controls for luminance and contrast depend upon feedback from the input, and there are strong indications that the autocorrelation function, and other associations between input variables, affect the contrast sensitivity function and our subjective experience of the world. The associative structure of sensory message can provide much knowledge about the world we live in, and neural mechanisms that discount established associative structure in the input messages by recoding them can improve survival by making new structure more easily detectable. These mechanisms may be responsible for illusions, such as those produced by a concave face-mask, that are classically attributed to top-down influences.     

A memory system in the monkey

A neural model is presented, based largely on evidence from studies in monkeys, postulating that coded representation of stimuli are stored in the higher-order sensory (i.e. association) areas of the cortex whenever stimulus activation of these areas also triggers a cortico-limbo-thalamo-cortical circuit. This circuit, which could act as either an imprinting or rehearsal mechanism, may actually consist of two parallel circuits, one involving the amygdala and the dorsomedial nucleus of the thalamus, and the other the hippocampus and the anterior nuclei. The stimulus representation stored in cortex by action of these circuits is seen as mediating three different memory processes: recognition, which occurs when the stored representation is reactivated via the original sensory pathway; recall, when it is reactivated via any other pathway; and association, when it activates other stored representations (sensory, affective, spatial, motor) via the outputs of the higher-order sensory areas to the relevant structures.     

A single microcircuit with multiple functions: state dependent information processing in the hippocampus

Many neural circuits process information in multiple distinct modes. For example, the hippocampus is involved in memory encoding, retrieval, and consolidation processes. These different mnemonic computations require processing of differing balances of current sensory input and previously stored associations. Here we explore patterns of activity in hippocampal output area CA1 associated with different information processing states. We discuss the evidence linking these patterns to specific inputs to CA1 and describe behavioral factors that are related to the balance of synaptic drive. We suggest that understanding the factors that influence information flow in the hippocampal circuit could provide important new insights into how neural circuits are reconfigured on the fly to perform different functions at different times.     

Short-term depression and transient memory in sensory cortex

Persistent neuronal activity is usually studied in the context of short-term memory localized in central cortical areas. Recent studies show that early sensory areas also can have persistent representations of stimuli which emerge quickly (over tens of milliseconds) and decay slowly (over seconds). Traditional positive feedback models cannot explain sensory persistence for at least two reasons: (i) They show attractor dynamics, with transient perturbations resulting in a quasi-permanent change of system state, whereas sensory systems return to the original state after a transient. (ii) As we show, those positive feedback models which decay to baseline lose their persistence when their recurrent connections are subject to short-term depression, a common property of excitatory connections in early sensory areas. Dual time constant network behavior has also been implemented by nonlinear afferents producing a large transient input followed by much smaller steady state input. We show that such networks require unphysiologically large onset transients to produce the rise and decay observed in sensory areas. Our study explores how memory and persistence can be implemented in another model class, derivative feedback networks. We show that these networks can operate with two vastly different time courses, changing their state quickly when new information is coming in but retaining it for a long time, and that these capabilities are robust to short-term depression. Specifically, derivative feedback networks with short-term depression that acts differentially on positive and negative feedback projections are capable of dynamically changing their time constant, thus allowing fast onset and slow decay of responses without requiring unrealistically large input transients.     

Regulation of afferent transmission in the feeding circuitry of Aplysia

Although feeding in Aplysia is mediated by a central pattern generator (CPG), the activity of this CPG is modified by afferent input. To determine how afferent activity produces the widespread changes in motor programs that are necessary if behavior is to be modified, we have studied two classes of feeding sensory neurons. We have shown that afferent-induced changes in activity are widespread because sensory neurons make a number of synaptic connections. For example, sensory neurons make monosynaptic excitatory connections with feeding motor neurons. Sensori-motor transmission is, however, regulated so that changes in the periphery do not disrupt ongoing activity. This results from the fact that sensory neurons are also electrically coupled to feeding interneurons. During motor programs sensory neurons are, therefore, rhythmically depolarized via central input. These changes in membrane potential profoundly affect sensori-motor transmission. For example, changes in membrane potential alter spike propagation in sensory neurons so that spikes are only actively transmitted to particular output regions when it is behaviorally appropriate. To summarize, afferent activity alters motor output because sensory neurons make direct contact with motor neurons. Sensori-motor transmission is, however, centrally regulated so that changes in the periphery alter motor programs in a phase-dependent manner.     

Logarithmic and Power Law Input-Output Relations in Sensory Systems with Fold-Change Detection

Two central biophysical laws describe sensory responses to input signals. One is a logarithmic relationship between input and output, and the other is a power law relationship. These laws are sometimes called the Weber-Fechner law and the Stevens power law, respectively. The two laws are found in a wide variety of human sensory systems including hearing, vision, taste, and weight perception; they also occur in the responses of cells to stimuli. However the mechanistic origin of these laws is not fully understood. To address this, we consider a class of biological circuits exhibiting a property called fold-change detection (FCD). In these circuits the response dynamics depend only on the relative change in input signal and not its absolute level, a property which applies to many physiological and cellular sensory systems. We show analytically that by changing a single parameter in the FCD circuits, both logarithmic and power-law relationships emerge; these laws are modified versions of the Weber-Fechner and Stevens laws. The parameter that determines which law is found is the steepness (effective Hill coefficient) of the effect of the internal variable on the output. This finding applies to major circuit architectures found in biological systems, including the incoherent feed-forward loop and nonlinear integral feedback loops. Therefore, if one measures the response to different fold changes in input signal and observes a logarithmic or power law, the present theory can be used to rule out certain FCD mechanisms, and to predict their cooperativity parameter. We demonstrate this approach using data from eukaryotic chemotaxis signaling.     

Cholinergic efferent synaptic transmission regulates the maturation of auditory hair cell ribbon synapses

Spontaneous electrical activity generated by developing sensory cells and neurons is crucial for the maturation of neural circuits. The full maturation of mammalian auditory inner hair cells (IHCs) depends on patterns of spontaneous action potentials during a ‘critical period’ of development. The intrinsic spiking activity of IHCs can be modulated by inhibitory input from cholinergic efferent fibres descending from the brainstem, which transiently innervate immature IHCs. However, it remains unknown whether this transient efferent input to developing IHCs is required for their functional maturation. We used a mouse model that lacks the α9-nicotinic acetylcholine receptor subunit (α9nAChR) in IHCs and another lacking synaptotagmin-2 in the efferent terminals to remove or reduce efferent input to IHCs, respectively. We found that the efferent system is required for the developmental linearization of the Ca²⁺-sensitivity of vesicle fusion at IHC ribbon synapses, without affecting their general cell development. This provides the first direct evidence that the efferent system, by modulating IHC electrical activity, is required for the maturation of the IHC synaptic machinery. The central control of sensory cell development is unique among sensory systems.     

Possible role of cooperative action of NMDA receptor and GABA function in developmental plasticity

The maturation of cortical circuits is strongly influenced by sensory experience during a restricted critical period. The developmental alteration in the subunit composition of NMDA receptors (NMDARs) has been suggested to be involved in regulating the timing of such plasticity. However, this hypothesis does not explain the evidence that enhancing GABA inhibition triggers a critical period in the visual cortex. Here, to investigate how the NMDAR and GABA functions influence synaptic organization, we examine an spike-timing-dependent plasticity (STDP) model that incorporates the dynamic modulation of LTP, associated with the activity- and subunit-dependent desensitization of NMDARs, as well as the background inhibition by GABA. We show that the competitive interaction between correlated input groups, required for experience-dependent synaptic modifications, may emerge when both the NMDAR subunit expression and GABA inhibition reach a sufficiently mature state. This may suggest that the cooperative action of these two developmental mechanisms can contribute to embedding the spatiotemporal structure of input spikes in synaptic patterns and providing the trigger for experience-dependent cortical plasticity.