Topic: synaptic weights

A central problem in biological learning is how information about the outcome of a decision or behavior can be used to reliably guide learning across distributed neural circuits while obeying biological constraints. This “credit assignment” problem is commonly solved in artificial neural networks through supervised gradient descent and the backpropagation algorithm. In contrast, biological learning is typically modelled using unsupervised Hebbian learning rules. While these rules only use local information to update synaptic weights, and are sometimes combined with weight constraints to reflec

Transcranial Magnetic Stimulation (TMS) offers a non-invasive clinical treatment for intractable neuropsychiatric disorders, including Major Depressive Disorder (MDD). Repetitive TMS (rTMS) involves administering a series of patterned magnetic pulses through the skull to modulate cortical brain activity and induce synaptic plasticity in aberrant neural circuitry. When successful, rTMS results in lasting therapeutic changes that may reflect an amelioration in cognitive symptoms. The salient parameters that comprise rTMS protocols, such as pulse frequency, amplitude, etc., interact to determine

Sensory neural circuits rely on a common set of motifs to process inputs, including convergence of multiple inputs to a single neuron, divergence of inputs into parallel pathways, and nonlinearities that are selective for some inputs over others. Past work has detailed how optimized response nonlinearities and synaptic weights can maximize encoded information, but these solutions depend on tightly tuned response functions and connectivities. Our study found that incorporating generic, non-invertible, selectivity-inducing nonlinearities into a circuit with divergent and convergent structure can

The accumulation and storage of information in memory is a fundamental computation underlying animal behavior. In many brain regions and task paradigms, ranging from motor control to navigation to decision-making, such accumulation is accomplished through neural integrator circuits that enable external inputs to move a system’s population-wide patterns of neural activity along a continuous attractor. In the first portion of the talk, I will discuss our efforts to dissect the circuit mechanisms underlying a neural integrator from a rich array of anatomical, physiological, and perturbation expe

Neural systems are remarkably robust against various perturbations, a phenomenon that still requires a clear explanation. Here, we graphically illustrate how neural networks can become robust. We study spiking networks that generate low-dimensional representations, and we show that the neurons’ subthreshold voltages are confined to a convex region in a lower-dimensional voltage subspace, which we call a ‘bounding box.’ Any changes in network parameters (such as number of neurons, dimensionality of inputs, firing thresholds, synaptic weights, or transmission delays) can all be understood as def

Since the theories of Marr, Ito, and Albus, the cerebellum has provided an attractive well-characterized model system to investigate biological mechanisms of learning. In recent years, theories have been developed that provide a normative account for many features of the anatomy and function of cerebellar cortex and cerebellum-like systems, including the distribution of parallel fiber-Purkinje cell synaptic weights, the expansion in neuron number of the granule cell layer and their synaptic in-degree, and sparse coding by granule cells. Typically, these theories focus on the learning of random

Rhythmic activity in the brain has been reported in relation to a wide range of cognitive processes. Changes in the rhythmic activity have been related to pathological states. These observations raise the question of the origin of these rhythms: can the mechanisms responsible for generation of these rhythms and that allow the propagation of the rhythmic signal be acquired via a process of learning? In my talk I will focus on spike timing dependent plasticity (STDP) and examine under what conditions this unsupervised learning rule can facilitate the propagation of rhythmic activity downstream i

The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function. However, quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state r

Spiking neural networks (SNNs) inherently rely on the timing of signals for representing and processing information. Transmission delays play an important role in shaping these temporal characteristics. Recent work [1, 2] has demonstrated the substantial advantages of learning these delays along with synaptic weights, both in terms of accuracy and memory efficiency. However, these approaches suffer from drawbacks in terms of precision and learning efficiency, as they operate in discrete time and with approximate gradients, while also requiring membrane potential recordings for calculating para

Spiking neural networks (SNNs) are characterized by low power consumption, highly parallel computation and event-driven information processing [1]. Many of the concepts that promise to improve the efficiency of SNNs compared to conventional deep neural networks, such as spikes, sparsity, and static suppression, are inspired by our understanding of how the brain processes information. Hence, we can further advance the development of SNNs by improving our current knowledge of how biological neural networks learn through spike-based local plasticity mechanisms. In recent years, many spike-based

Background: Many animals use celestial cues to determine their heading. In the insect brain, the central complex is crucial for spatial orientation. In desert locusts and fruit flies, compass neurons in the central complex encode the animal’s heading relative to the sun [2, 5]. However, the neural computations underlying the transformations of visual inputs to a compass signal remain elusive. In fruit flies, it is hypothesised that visual information is relayed from tangential neu- rons to compass neurons through likely inhibitory all-to-all connections with plastic synapses [1]. We aim to exp

Animals possess complex innate computational capabilities essential for their survival, such as recognizing poisonous food or walking right after birth. However, specifying the precise wiring diagram for neuronal circuits to achieve these behaviors is too complex to be compressed through the "genomic bottleneck" [3]. Experimental data on cortical microcircuits [1][2] suggest that the genetic code determines synaptic connection probabilities between neurons based on their genetic types and spatial distances. We propose that these connection probabilities, rather than individual synaptic weight

Biological neural network models with synaptic plasticity pose challenges for both theoretical and numerical analyses. The numerical bottleneck arises from the $N^2$ scaling of synapses. Additionally, the intricate coupling between neuron and synapse dynamics, along with plasticity-induced heterogeneity, hinders the use of classical tools from theory. One approach to address these challenges has been to assume plasticity to be very slow compared to neural activity and leverage slow-fast theory [1]. However, this slowness is not universally applicable in the brain, leaving unclear how to analys