Quantitative Biology

Learning depends on changes in synaptic connections deep inside the brain. In multilayer networks, these changes are triggered by error signals fed back from the output, generally through a stepwise inversion of the feedforward processing steps. The gold standard for this process -- backpropagation -- works well in artificial neural networks, but is biologically implausible. Several recent proposals have emerged to address this problem, but many of these biologically-plausible schemes are based on learning an independent set of feedback connections. This complicates the assignment of errors to each synapse by making it dependent upon a second learning problem, and by fitting inversions rather than guaranteeing them. Here, we show that feedforward network transformations can be effectively inverted through dynamics. We derive this dynamic inversion from the perspective of feedback control, where the forward transformation is reused and dynamically interacts with fixed or random feedback to propagate error signals during the backward pass. Importantly, this scheme does not rely upon a second learning problem for feedback because accurate inversion is guaranteed through the network dynamics. We map these dynamics onto generic feedforward networks, and show that the resulting algorithm performs well on several supervised and unsupervised datasets. Finally, we discuss potential links between dynamic inversion and second-order optimization. Overall, our work introduces an alternative perspective on credit assignment in the brain, and proposes a special role for temporal dynamics and feedback control during learning.

Body sway responses evoked by a horizontal acceleration of a level and firm support surface are particular in that the vestibular information on body-space angle BS resembles the proprioceptive information on body-foot angle BF. We compared corresponding eyes-closed responses of vestibular-able (VA) and vestibular-loss (VL) subjects, postulating a close correspondence. In contradistinction to previous studies, we used an unpredictable (pseudorandom) stimulus and found that the eyes-closed and eyes-open responses of the VA closely resembled those of the VL subjects, as expected. We further conclude that the vestibular signals coding head linear translation in VA subjects has in this case too little functional relevance to cause a notable difference between the subject groups.

There is a strong nexus between the network size and the computational resources available, which may impede a neuroscience study. In the meantime, rescaling the network while maintaining its behavior is not a trivial mission. Additionally, modeling patterns of connections under topographic organization presents an extra challenge: to solve the network boundaries or mingled with an unwished behavior. This behavior, for example, could be an inset oscillation due to the torus solution; or a blend with/of unbalanced neurons due to a lack (or overdose) of connections. We detail the network rescaling method able to sustain behavior statistical utilized in Romaro et al. (2018) and present a boundary solution method based on the previous statistics recoup idea.

Databases of directed- and weighted- connectivity for mouse, macaque and marmoset monkeys, have recently become available and begun to be used to build dynamical models. A hierarchical organization can be defined based on laminar patterns of cortical connections, possibly improved by thalamocortical projections. A large-scale model of the macaque cortex endowed with a laminar structure accounts for layer-dependent and frequency-modulated interplays between bottom-up and top-down processes. Signal propagation in a version of the model with spiking neurons displays a threshold of stimulus amplitude for the activity to gain access to the prefrontal cortex, reminiscent of the ignition phenomenon associated with conscious perception. These two examples illustrate how connectomics may inform theory leading to discoveries. Computational modeling raises open questions for future empirical research, in a back-and-forth collaboration of experimentalists and theorists.

Attempting to imitate the brain functionalities, researchers have bridged between neuroscience and artificial intelligence for decades; however, experimental neuroscience has not directly advanced the field of machine learning. Here, using neuronal cultures, we demonstrate that increased training frequency accelerates the neuronal adaptation processes. This mechanism was implemented on artificial neural networks, where a local learning step-size increases for coherent consecutive learning steps and tested on a simple dataset of handwritten digits, MNIST. Based on our online learning results with a few handwriting examples, success rates for brain-inspired algorithms substantially outperform the commonly used machine learning algorithms. We speculate this emerging bridge from slow brain function to machine learning will promote ultrafast decision making under limited examples, which is the reality in many aspects of human activity, robotic control, and network optimization.

Objective: To determine whether brain volume is associated with functional outcome after acute ischemic stroke (AIS). Methods: We analyzed cross-sectional data of the multi-site, international hospital-based MRI-GENetics Interface Exploration (MRI-GENIE) study (July 1, 2014- March 16, 2019) with clinical brain magnetic resonance imaging (MRI) obtained on admission for index stroke and functional outcome assessment. Post-stroke outcome was determined using the modified Rankin Scale (mRS) score (0-6; 0: asymptomatic; 6 death) recorded between 60-190 days after stroke. Demographics and other clinical variables including acute stroke severity (measured as National Institutes of Health Stroke Scale score), vascular risk factors, and etiologic stroke subtypes (Causative Classification of Stroke) were recorded during index admission. Results: Utilizing the data from 912 acute ischemic stroke (AIS) patients (65+/-15 years of age, 58% male, 57% history of smoking, and 65% hypertensive) in a generalized linear model, brain volume (per 155.1cm^3 ) was associated with age (beta -0.3 (per 14.4 years)), male sex (beta 1.0) and prior stroke (beta -0.2). In the multivariable outcome model, brain volume was an independent predictor of mRS (beta -0.233), with reduced odds of worse long-term functional outcomes (OR: 0.8, 95% CI 0.7-0.9) in those with larger brain volumes. Conclusions: Larger brain volume quantified on clinical MRI of AIS patients at time of stroke purports a protective mechanism. The role of brain volume as a prognostic, protective biomarker has the potential to forge new areas of research and advance current knowledge of mechanisms of post-stroke recovery.

In this paper we present a brain-inspired cognitive architecture that incorporates sensory processing, classification, contextual prediction, and emotional tagging. The cognitive architecture is implemented as three modular web-servers, meaning that it can be deployed centrally or across a network for servers. The experiments reveal two distinct operations of behaviour, namely high- and low-salience modes of operations, which closely model attention in the brain. In addition to modelling the cortex, we have demonstrated that a bio-inspired architecture introduced processing efficiencies. The software has been published as an open source platform, and can be easily extended by future research teams. This research lays the foundations for bio-realistic attention direction and sensory selection, and we believe that it is a key step towards achieving a bio-realistic artificial intelligent system.

The connection between brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. Particularly in artificial intelligence research, behavior is generated by a black box approximating the brain. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. This model generates outputs and behaviors from a priori associations, yet this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We will introduce our approach, which is to use Braitenberg Vehicles (BVs) to model the development of an artificial nervous system. The resulting developmental BVs will generate behaviors that range from stimulus responses to group behavior that resembles collective motion. Next, we will situate this work in the domain of artificial brain networks. Then we will focus on broader themes such as embodied cognition, feedback, and emergence. Our perspective will then be exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we will revisit concepts related to our approach and how they might guide future development.

For constructing neuronal network models computational neuroscientists have access to wide-ranging anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined. Finding and interpreting the most relevant data, estimating missing values, and combining the data and estimates from various sources into a coherent whole is a daunting task. With this chapter we aim to provide guidance to modelers by describing the main types of anatomical data that may be useful for informing neuronal network models. We further discuss aspects of the underlying experimental techniques relevant to the interpretation of the data, list particularly comprehensive data sets, and describe methods for filling in the gaps in the experimental data. Such methods of `predictive connectomics' estimate connectivity where the data are lacking based on statistical relationships with known quantities. It is instructive, and in certain cases necessary, to use organizational principles that link the plethora of data within a unifying framework where regularities of brain structure can be exploited to inform computational models. In addition, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on the mammalian cerebral cortex. Given the still existing need for modelers to navigate a complex data landscape full of holes and stumbling blocks, it is vital that the field of neuroanatomy is moving toward increasingly systematic data collection, representation, and publication.

Modern recording technologies now enable simultaneous recording from large numbers of neurons. This has driven the development of new statistical models for analyzing and interpreting neural population activity. Here we provide a broad overview of recent developments in this area. We compare and contrast different approaches, highlight strengths and limitations, and discuss biological and mechanistic insights that these methods provide.