Topic: neural computation

Ad Aertsen reflects on Valentino Braitenberg's contributions to neuroscience, including anatomy, synthetic psychology and theories of neural computation.

Neural oscillations are often proposed to support brain computation by routing information, organizing cell assemblies, or shaping coding dynamics. Yet these ideas usually assume rhythms that are strong, sustained, and regular, whereas in vivo oscillations are often weak, transient, noisy, and variable in frequency and phase. In this talk, I will argue that such “no-metronome” oscillations are not just noisy fluctuations, but coordinated complex dynamics with functional consequences. Combining analyses of neural activity recordings during actual behavior (mice and non-human-primate LFPs and hu

Liset de la Prida discusses hippocampal sharp-wave ripples and their relationship to replay and memory. The conversation connects different ripple patterns and neuron types to population activity, asking how particular cells help shape the lower-dimensional dynamics used to describe neural computation.

Predictive processing offers a unifying view of neural computation, proposing that brains continuously anticipate sensory input and update internal models based on prediction errors. In this talk, I will present converging evidence for the computational mechanisms underlying this framework across human neuroscience and deep neural networks. I will begin with recent work showing that large-scale distributed prediction-error encoding in the human brain directly predicts how sensory representations reorganize through predictive learning. I will then turn to PredNet, a popular predictive coding in

Neural computation is inextricably bound to decisions that must be made under time pressure and uncertainty. At the level of neural circuits, single neurons need to decide whether to spike. On longer timescales, the component circuitry needs to decide whether to reconfigure to store memories and adapt to novel situations. In this talk I will focus on two fun ideas in each of these contexts by showing how nonlinearities in neural components naturally form excitable switches that enable reliable decisions to be made in fluctuating environments. I will also issue propaganda that the kind of high

Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits, involving a comparison of the signals from neighboring photoreceptors over time. The exact nature of this process represents a classic example of neural computation and has been a longstanding question in the field. Much progress has been made in recent years

Neuronal computation is mediated by spikes, yet it is unclear what the benefits of discrete spiking dynamics over continuous firing rates are. Many theoretical and computational studies treat single neurons as continuous units and their output as an effective firing rate. These works view spiking dynamics as a biophysical constraint, e.g., for energy efficiency. Conversely, other theories suggest that the exact timings of single spikes are meaningful. We propose a novel theory that shows the benefits of spiking dynamics on neural computation. In particular, we show that spiking neural dynamics

Recurrent neural networks, a ubiquitous construct in machine intelligence, have become a powerful hypothesis-generating tool in theoretical neuroscience. Such networks can be used to examine potential circuit mechanisms associated with a variety of cognitive functions. Here, we use recurrent networks to engage a new theoretical question: the potential role of astrocytes in neural computation. Astrocytes are highly abundant cells in the cortex that are capable of modulating many facets of neural dynamics, including excitability, synaptic efficacy and plasticity. However, despite this modulatory

Physiological experiments have highlighted how the dendrites of biological neurons can nonlinearly process distributed synaptic inputs. However, it is unclear how qualitative aspects of a dendritic tree, such as its branched morphology, its repetition of presynaptic inputs, voltage-gated ion channels, electrical properties and complex synapses, determine neural computation beyond this apparent nonlinearity. While it has been speculated that the dendritic tree of a neuron can be seen as a multi-layer neural network and it has been shown that such an architecture could be computationally strong,

Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits. The exact nature of this process represents a classic example of neural computation and has been a longstanding question in the field. Our results obtained in the fruit fly Drosophila demonstrate that the local direction of motion is computed in two parallel

The Interdisciplinary College is an annual spring school which offers a dense state-of-the-art course program in neurobiology, neural computation, cognitive science/psychology, artificial intelligence, machine learning, robotics and philosophy. It is aimed at students, postgraduates and researchers from academia and industry. This year's focus theme "Flexibility" covers (but not be limited to) the nervous system, the mind, communication, and AI & robotics. All this will be packed into a rich, interdisciplinary program of single- and multi-lecture courses, and less traditional formats.

The potential of machine learning and deep learning to advance artificial intelligence is driving a quest to build dedicated computers, such as neuromorphic hardware that emulate the biological processes of the brain. While the hardware technologies already exist, their application to real-world tasks is hindered by the lack of suitable programming methods. Advances at the interface of neural computation and machine learning showed that key aspects of deep learning models and tools can be transferred to biologically plausible neural circuits. Building on these advances, I will show that differ

We can now measure the connectivity of every neuron in a neural circuit, but we are still blind to other biological details, including the dynamical characteristics of each neuron. The degree to which connectivity measurements alone can inform understanding of neural computation is an open question. Here we show that with experimental measurements of only the connectivity of a biological neural network, we can predict the neural activity underlying a specified neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the moti

Current theoretical frameworks in neuroscience focus on attractor-based formalism, where the neural dynamics asymptotically converge to one of the system’s stable states, and these states are subsequently associated with the behavioral states of the system [1,2]. Although this framework explains a wide range of phenomena, it cannot explicitly account for transiently stable sequential neuronal activity dynamics, generated for example in olfactory encoding [3]. Recently, we have proposed a complementary theoretical framework that does not rely on (un)stable attractors, but rather on transiently

Most models of neural computation used in artificial intelligence and neuroscience use point neurons, attributing computational complexity to the network level. This approach overlooks the computational complexity of single biological neurons. Neural dendrites exhibit many different nonlinear dynamics, temporal-spatial filtering, and internal feedback and amplification mechanisms (London & Häusser 2005). Abstracting the entire dendritic arbor into a single compartment thus omits useful computational properties (Poirazi et al. 2003). Three main factors contribute to the integration process: (1)

The importance of dendritic structures for neural computation has been recognized since the early days of neuroscience. However, it is only recently, with the increasing availability of data and advances in modelling techniques, that researchers have begun to map out the precise roles of different branching structures and synaptic distributions [1, 2]. Here, we investigate how the nonlinear integration of excitatory and inhibitory currents in dendritic branches can enhance the ability of a neuron to develop input selectivity and E/I balance via Hebbian synaptic plasticity. We model a single ra

In the brain, the collective activity of neurons form trajectories on low dimensional neural manifolds. The neural computation underlying flexible cognition and behavior relies on dynamic control of these structures. For example, different tasks or behaviors are represented on different subspaces, requiring fast timescale subspace rotation to move from one behavior to the next. For flexibility in a particular behavior, the neural trajectory must be dynamically controllable within that behaviorally determined subspace. Here we propose mechanisms for fast timescale control of the neural subspace

The brain's ability to process information through temporal sequences of spiking activity is crucial for complex cognitive tasks. These sequences arise from both sensory inputs and inherent network dynamics, spanning multiple, dynamically changing timescales. For instance, the syllables in the word ‘com-pu-ter’ would produce spike pattern sequences with different timings when spoken slowly or quickly, yet the semantic meaning remains invariant. What matters is the rank order of the sequence elements. Recent biological findings[1] and computational models suggest that dendritic plateau potentia