Neural coding

The comprehensive production of detailed bathymetric maps is important for disaster prevention, resource exploration, safe navigation, marine salvage, and monitoring of marine organisms. However, owing to observation difficulties, the amount of data on the world’s seabed topography is scarce. Therefore, it is essential to develop methods that effectively use the limited data. In this study, based on dictionary learning and sparse coding, we modified the super-resolution technique and applied it to seafloor topographical maps. Improving on the conventional method, before dictionary learning, we performed pre-processing to separate the teacher image into a low-frequency component that has a general structure and a high-frequency component that captures the detailed topographical features. We learn the topographical features by training the dictionary. As a result, the root-mean-square error (RMSE) was reduced by 30% compared with bicubic interpolation and accuracy was improved, especi...

In this paper, we document lognormal distributions for spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas.
The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears as a functional property that is present everywhere.
Secondly, we created a generic neural model to show that Hebbian learning will create and maintain lognormal distributions.
We could prove with the model that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This settles a long-standing question about the type of plasticity exhibited by intrinsic excitability.

Neurons rely on action potentials, or spikes, to encode information. But spikes can encode different stimulus features in different neurons. We show here through simulations and experiments how neurons encode the integral or derivative of their input based on the distinct tuning properties conferred upon them by subthreshold currents. Slow-activating subthreshold inward (depolarizing) current mediates positive feedback control of subthreshold voltage, sustaining depolarization and allowing the neuron to spike on the basis of its integrated stimulus waveform. Slow-activating subthreshold outward (hyperpolarizing) current mediates negative feedback control of subthreshold voltage, truncating depolarization and forcing the neuron to spike on the basis of its differentiated stimulus waveform. Depending on its direction, slow-activating subthreshold current cooperates or competes with fast-activating inward current during spike initiation. This explanation predicts that sensitivity to th...

The analysis of the brain in terms of integrated neural networks may offer insights on the reciprocal relation between structure and information processing. Even with inherent technical limits, many studies acknowledge neuron spatial arrangements and communication modes as key factors. In this perspective, we investigated the functional organization of neuronal networks by explicitly assuming a specific functional topology, the small-world network. We developed two different computational approaches. Firstly, we asked whether neuronal populations actually express small-world properties during a definite task, such as a learning task. For this purpose we developed the Inductive Conceptual Network (ICN), which is a hierarchical bio-inspired spiking network, capable of learning invariant patterns by using variable-order Markov models implemented in its nodes. As a result, we actually observed small-world topologies during learning in the ICN. Speculating that the expression of small-world networks is not solely related to learning tasks, we then built a de facto network assuming that the information processing in the brain may occur through functional small-world topologies. In this de facto network, synchronous spikes reflected functional small-world network dependencies. In order to verify the consistency of the assumption, we tested the null-hypothesis by replacing the small-world networks with random networks. As a result, only small world networks exhibited functional biomimetic characteristics such as timing and rate codes, conventional coding strategies and neuronal avalanches, which are cascades of bursting activities with a power-law distribution. Our results suggest that small-world functional configurations are liable to underpin brain information processing at neuronal level.

Understanding the complex brain computations leading to object recognition requires quantitatively characterizing the information represented in inferior temporal cortex (IT), the highest stage of the primate visual stream. A read-out technique based on a trainable classifier is used to ...

Abstract: Basic principles of the theory of harmony reflect physiological and anatomical properties of the auditory nervous system and related cognitive systems. This hypothesis is motivated by observations from several different disciplines, including ethnomusicology, developmental psychology, and animal behavior. Over the past several years, we and our colleagues have been investigating the vertical dimension of harmony from the perspective of neurobiology using physiological, psychoacoustic, and neurological methods. Properties of the auditory system that govern harmony perception include (1) the capacity of peripheral auditory neurons to encode temporal regularities in acoustic fine structure and (2) the differential tuning of many neurons throughout the auditory system to a narrow range of frequencies in the audible spectrum. Biologically determined limits on these properties constrain the range of notes used in music throughout the world and the way notes are combined to form intervals and chords in popular Western music. When a harmonic interval is played, neurons throughout the auditory system that are sensitive to one or more frequencies (partials) contained in the interval respond by firing action potentials. For consonant intervals, the fine timing of auditory nerve fiber responses contains strong representations of harmonically related pitches implied by the interval (e.g., Rameau's fundamental bass) in addition to the pitches of notes actually present in the interval. Moreover, all or most of the partials can be resolved by finely tuned neurons throughout the auditory system. By contrast, dissonant intervals evoke auditory nerve fiber activity that does not contain strong representations of constituent notes or related bass notes. Furthermore, many partials are too close together to be resolved. Consequently, they interfere with one another, cause coarse fluctuations in the firing of peripheral and central auditory neurons, and give rise to perception of roughness and dissonance. The effects of auditory cortex lesions on the perception of consonance, pitch, and roughness, combined with a critical reappraisal of published psychoacoustic data on the relationship between consonance and roughness, lead us to conclude that consonance is first and foremost a function of the pitch relationships among notes. Harmony in the vertical dimension is a positive phenomenon, not just a negative phenomenon that depends on the absence of roughness—a view currently held by many psychologists, musicologists, and physiologists.

Reception of certain environmental energy patterns can allow organisms to successfully respond to present or future environmental conditions. Sensory systems are the means by which this reception occurs. The bioelectric components of this process are typically characterized as "sensory coding." "Coding" logically requires subsequent "decoding," and implies that the decoder has a special, commanding role. In contrast, the term "transformation," which is sometimes applied to the prebioelectric components of sensory systems, can connote a series of changes in the medium, form, or content of sensory processes. Neither coding nor decoding are implied, no notions of "representation" or "reconstruction" of the world are introduced, and distributed, parallel processing is a natural corollary of transformations. It is proposed that the problematic construct of sensory coding, with its concomitant decoding, be replaced with the more ...

"Sensory input signals can span many orders of magnitude while dynamically changing, and this requires neurons to adapt their bandwidth-limited response behaviour efficiently1,2,3. We derive from first principles a Bayesian model of neural adaptation to the instantaneous input statistics, and show how existing intracellular data can be explained by its biophysical implementation.
We assume that cortical neurons' spiking activity can be described by inhomogeneous Poisson processes4, whose instantaneous rate depends both on the received input and on the internal adaptation state. We show how neurons can exploit the information contained in their own spiking history to infer the instantaneous input statistics and to adapt their input-output behaviour accordingly in order to optimise information transfer. Specifically, we prove that the posterior of the instantaneous input rate can be well approximated using a Gamma distribution for various considered temporal dynamics of the input statistics. We derive temporal update equations for the parameters of this distribution. Under the ecologically reasonable assumption that the input stimulus changes on a timescale longer than the average inter spike interval, we find the dynamics of the update equations to mimic the known time course of Ca++ and of some neurotransmitter concentrations5. We argue that these quantities can encode the updated belief about the instantaneous input statistics and can regulate neural response accordingly6. We also show how the information encoded by a presynaptic Bayesian adapting neuron c!an be decoded by postsynaptic neurons by weighted temporal integration.
Using our first principles approach we link the temporal statistics of the stimulus with the dynamics of neural adaptation. The neural mechanism we propose allows a stochastically spiking neuron to implement temporal predictive coding7,8 and, when implemented by both the pre and postsynaptic neurons of a cortical population, produces global adaptation behaviours at the population level which m!imic experimental results9."

Neural spikes are an evolutionarily ancient innovation that remains nature’s unique mechanism for rapid, long distance information transfer. It is now known that neural spikes sub serve a wide variety of functions and essentially all of the basic questions about the communication role of spikes have been answered. Current efforts focus on the neural communication of probabilities and utility values

The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and...

A decision is a commitment to a proposition among multiple options. Often this commitment leads to a particular course of action. It might be said that the life of an organism consists of a series of decisions made in time [29]. Leaving aside the moral dimension of choices, it is reasonable to view even pedestrian decisions as a window on higher cognitive function, for they offer a glimpse of how the brain connects information to behavior in a contingent manner. In-deed, even simple visual decisions are based on a sophisticated confluence of available sensory information, prior knowledge, and the potential costs and benefits associated with the possible courses of action. Thus, understanding the neural basis of these decisions provides a model for the principles that gov-ern higher brain function. An important step in making progress toward this goal is to develop frameworks for understanding simple decisions. In this chapter, we describe one such framework, sequential sampling, whi...

'Filling-in’ occurs when a retinally stabilized object subjectively appears to vanish following perceptual fading of its boundaries. The term ‘filling-in’ literally means that information about the apparently vanished object is lost and replaced solely by information arising from the surrounding background. How- ever, we find evidence that the mechanism of ‘filling-in’ can actually involve a process of ‘feature mixing’ rather than ‘feature replacement,’ whereby features on either side of a perceptually faded boundary merge. Here we investigate the properties of feature mixing and specify certain conditions under which such mixing occurs. Our results show that, when using visual stimuli composed of spatially alternating stripes containing different luminances or motion signals, and when using the neon-color-spread- ing paradigm, the filled-in luminance, motion, or color is approximately the area and magnitude weighted average of the background and the foreground luminance, motion, or color, respectively. Together, these results demonstrate that, under at least certain conditions, ‘filling-in’ may involve a process of feature mixing or feature averaging rather than one of feature replacement.

The spatiotemporal pattern of synaptic inputs to the dendritic tree is crucial for synaptic integration and plasticity. However, it is not known if input patterns driven by sensory stimuli are structured or random. Here we investigate the spatial patterning of synaptic inputs by directly monitoring presynaptic activity in the intact mouse brain on the micron scale. Using in vivo calcium imaging of multiple neighbouring cerebellar parallel fibre axons, we find evidence for clustered patterns of axonal activity during sensory processing. The clustered parallel fibre input we observe is ideally suited for driving dendritic spikes, postsynaptic calcium signalling, and synaptic plasticity in downstream Purkinje cells, and is thus likely to be a major feature of cerebellar function during sensory processing.

Likelihood-based encoding models founded on point processes have received significant attention in the literature because of their ability to reveal the information encoded by spiking neural populations. We propose an approximation to the likelihood of a point-process model of neurons that holds under assumptions about the continuous time process that are physiologically reasonable for neural spike trains: the presence of a refractory period, the predictability of the conditional intensity function, and its integrability. These are properties that apply to a large class of point processes arising in applications other than neuroscience. The proposed approach has several advantages over conventional ones. In particular, one can use standard fitting procedures for generalized linear models based on iteratively reweighted least squares while improving the accuracy of the approximation to the likelihood and reducing bias in the estimation of the parameters of the underlying continuous-time model. As a result, the proposed approach can use a larger bin size to achieve the same accuracy as conventional approaches would with a smaller bin size. This is particularly important when analyzing neural data with high mean and instantaneous firing rates. We demonstrate these claims on simulated and real neural spiking activity. By allowing a substantive increase in the required bin size, our algorithm has the potential to lower the barrier to the use of point-process methods in an increasing number of applications.

The comprehensive production of detailed bathymetric maps is important for disaster prevention, resource exploration, safe navigation, marine salvage, and monitoring of marine organisms. However, owing to observation difficulties, the amount of data on the world’s seabed topography is scarce. Therefore, it is essential to develop methods that effectively use the limited data. In this study, based on dictionary learning and sparse coding, we modified the super-resolution technique and applied it to seafloor topographical maps. Improving on the conventional method, before dictionary learning, we performed pre-processing to separate the teacher image into a low-frequency component that has a general structure and a high-frequency component that captures the detailed topographical features. We learn the topographical features by training the dictionary. As a result, the root-mean-square error (RMSE) was reduced by 30% compared with bicubic interpolation and accuracy was improved, especi...