Abstract The stability of ecological systems has been a long-standing focus ecology. Recently, tools from random matrix theory have identified the main drivers in communities whose network structure is random. However, empirical food webs differ greatly graphs. For example, their degree distribution broader, they contain few trophic cycles, and are almost interval. Here we derive an approximation for generated by cascade model, which ‘larger’ species consume ‘smaller’ ones. We predict these...

In neural circuits, statistical connectivity rules strongly depend on cell-type identity. We study dynamics of networks with cell-type-specific by extending the dynamic mean-field method and find that these exhibit a phase transition between silent chaotic activity. By analyzing locus this transition, we derive new result in random matrix theory: spectral radius block-structured variances. apply our results to show how small group hyperexcitable neurons within network can significantly...

Significance Spike-timing–dependent plasticity (STDP) is a form of synaptic modification thought to be primary mechanism underlying formation new memories. Despite triggering tremendous interest since its discovery, it still unclear whether rules inferred from in vitro experiments are correct physiological conditions. While STDP induction depends on intracellular calcium influx, all previous studies used an abnormally high concentration extracellular calcium. Here, we study the influence...

Coding for space in the mammalian brain Nearly all mammals navigate over large spatial scales environments that span hundreds of meters to many kilometers. However, very little is known about neural representations underlie coding such spaces. Eliav et al. recorded from place cells hippocampus bats as they flew back and forth on an extremely long track (see Perspective by Wood Dudchenko). Many had multiple fields within this environment. The field sizes ranged less than 1 meter up 32 meters,...

Significance Are olfactory receptor neurons (ORNs) arranged in a functionally meaningful manner to facilitate information processing? Here, we address this long-standing question by uncovering valence map the periphery of Drosophila . Within sensory hairs, find that neighboring ORNs antagonistically regulate behaviors: stereotypically compartmentalized large- and small-spike ORNs, recognized their characteristic spike amplitudes, either promote or inhibit same type behavior, respectively....

Attractor networks are an influential theory for memory storage in brain systems. This has recently been challenged by the observation of strong temporal variability neuronal recordings during tasks. In this work, we study a sparsely connected attractor network where memories learned according to Hebbian synaptic plasticity rule. After recapitulating known results continuous, Hopfield model, investigate model which new continuously and old forgotten, using online We show that forgetting...

The cerebellum aids the learning of fast, coordinated movements. According to current consensus, erroneously active parallel fibre synapses are depressed by complex spikes signalling movement errors. However, this theory cannot solve

Throughout their daily lives, animals and humans often switch between different behaviours. However, neuroscience research typically studies the brain while animal is performing one behavioural task at a time, little known about how circuits represent switches Here we tested this question using an ethological setting: two bats flew together in long 135 m tunnel, switched navigation when flying alone (solo) collision avoidance as they past each other (cross-over). Bats increased echolocation...

Using a generalized random recurrent neural network model, and by extending our recently developed mean-field approach [J. Aljadeff, M. Stern, T. Sharpee, Phys. Rev. Lett. 114, 088101 (2015)], we study the relationship between connectivity structure its low dimensional dynamics. Each connection in is number with mean 0 variance that depends on pre- post-synaptic neurons through sufficiently smooth function $g$ of their identities. We find these networks undergo phase transition from silent...

We study the spectrum of an asymmetric random matrix with block structured variances. The rows and columns square are divided into $D$ partitions arbitrary size (linear in $N$). parameters model variances elements each block, summarized $g\in\mathbb{R}^{D\times D}_+$. Using Hermitization approach by studying matrix-valued Stieltjes transform we show that these matrices have a circularly symmetric spectrum, give explicit formula for their spectral radius set implicit equations full density...

Abstract Ethologically relevant stimuli are often multidimensional. In many brain systems, neurons with “pure” tuning to one stimulus dimension found along “conjunctive” that encode several dimensions, forming an apparently redundant representation. Here we show using theoretical analysis a mixed-dimensionality code can efficiently represent in different behavioral regimes: encoding by conjunctive cells is more robust when the changes quickly, whereas on long timescales pure fewer neurons....

Many neurodegenerative diseases are characterized by malfunction of the DNA damage response. Therefore, it is important to understand connection between system level neural network behavior and DNA. Neural networks drawn from genetically engineered animals, interfaced with micro-electrode arrays (MEA) allowed us uncover connections networks' activity properties such genome instability. We discovered that Atm protein deficiency, which in humans leads progressive motor impairment, a reduced...

The mammalian brain implements sophisticated sensory processing algorithms along multilayered (“deep”) neural networks. Strategies that insects use to meet similar computational demands, while relying on smaller nervous systems with shallow architectures, remain elusive. Using Drosophila as a model, we uncover the algorithmic role of odor preprocessing by network compartmentalized olfactory receptor neurons. Each compartment operates ratiometric unit for specific odor-mixtures. This...

Abstract Neural circuits construct internal ‘world-models’ to guide behavior. The predictive processing framework posits that neural activity signaling sensory predictions and concurrently computing prediction-errors is a signature of those models. Here, understand how the brain generates for complex sensorimotor signals, we investigate emergence high-dimensional, multi-modal representations in recurrent networks. We find robust arises network with loose excitatory/inhibitory balance....

Fluctuations of synaptic weights, among many other physical, biological, and ecological quantities, are driven by coincident events two "parent" processes. We propose a multiplicative shot-noise model that can capture the behaviors broad range such natural phenomena, analytically derive an approximation accurately predicts its statistics. apply our results to study effects plasticity rule was recently extracted from measurements in physiological conditions. Using mean-field theory analysis...

The cortex learns to make associations between stimuli and spiking activity which supports behaviour. It does this by adjusting synaptic weights. complexity of these transformations implies that synapses have change without access the full error information, a problem typically referred as "credit-assignment". However, it remains unknown how solves problem. We propose combination plasticity rules, 1) Hebbian, 2) acetylcholine-dependent 3) noradrenaline-dependent excitatory plasticity,...

Abstract The cerebellum aids the learning and execution of fast coordinated movements, with acquired information being stored by plasticity parallel fibre—Purkinje cell synapses. According to current consensus, erroneously active fibre synapses are depressed complex spikes arising when climbing fibres signal movement errors. However, this theory cannot solve credit assignment problem using limited from a global evaluation optimise behaviour guiding in numerous neurones. We identify possible...

Many biological systems perform computations on inputs that have very large dimensionality. Determining the relevant input combinations for a particular computation is often key to understanding its function. A common way find dimensions examine difference in variance between distribution and of associated with certain outputs. In neuroscience, corresponding method known as spike-triggered covariance (STC). This has been highly successful characterizing neurons variety sensory systems. So...

There is accumulating evidence that biological neural networks posses optimal computational capacity when they are at or near a critical point in which the network transitions to chaotic regime. We derive formula for of general heterogeneous network. This relates structure its point. The heterogeneity may describe spatial structure, multiplicity cell types any selective connectivity rules. To define we divide N neurons into D groups such ∑d=1...DNd=N. synaptic weight between i,j (the...

We study the activity of a recurrent neural network consisting multiple cell groups through structure its correlations by showing how rules that govern strengths connections between different shape average autocorrelation found in each group.We derive an analytical expression for number independent modes can concurrently sustain.Each mode corresponds to non-zero