."A Hebbian form of synaptic plasticity at inhibitory synapses generates balanced input currents and sparse neuronal responses that stabilize memory traces in neuronal networks" Cortical neurons receive balanced excitatory and inhibitory membrane currents.Here, we show that such a balance can be established and maintained in an experiencedependent manner by synaptic plasticity at inhibitory synapses. The mechanism we put forward provides an explanation for the sparse firing patterns observed in response to natural stimuli and fits well with a recently observed interaction of excitatory and inhibitory receptive field plasticity. We show that the introduction of inhibitory plasticity in suitable recurrent networks provides a homeostatic mechanism that leads to asynchronous irregular network states. Further, it can accommodate synaptic memories with activity patterns that become indiscernible from the background state, but can be re-activated by external stimuli. Our results suggest an essential role of inhibitory plasticity in the formation and maintenance of functional cortical circuitry. 1The balance of excitatory and inhibitory membrane currents a neuron experiences during stimulated and ongoing activity has been the topic of many recent studies (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14). This balance, first defined as equal average amounts of de-and hyperpolarizing membrane currents (from hereon referred to as "global balance") is thought to be essential for maintaining stability of cortical networks (1, 2). In the balanced state networks display asynchronous irregular (AI) dynamics that mimic activity patterns observed in cortical neurons. Such asynchronous network states facilitate rapid responses to small changes in the input (2-4), providing an ideal substrate for cortical signal processing (5,15,16). Pathologies that disrupt the balance of excitation and inhibition have often been implicated in neurological diseases such as epilepsy or schizophrenia (17, 18).Moreover, the input currents to a given cortical neuron are not merely globally balanced. Excitatory and inhibitory inputs are coupled also in time (6-8) and co-tuned for different stimulus features (9,10). The tight coupling of excitation and inhibition suggests a more precise, detailed balance, in which each excitatory input arrives at the cell together with an inhibitory counterpart, supposedly supplied through feedforward inhibition (Fig. 1 A). These observations fit well with models of cortical processing in which balanced sensory inputs are left unattended, but can be transiently (11), or persistently turned on by targeted disruptions of the balance (12-14).Although it is widely thought that the excitatory-inhibitory balance plays an important role for stability and information processing in cortical networks, it is still not understood by which mechanisms this balance is established and maintained in the presence of ongoing sensory experiences. Inspired by recent experimental results (9), we investigate the hypothesis that synaptic plastic...

Populations of neurons in motor cortex engage in complex transient dynamics of large amplitude during the execution of limb movements. Traditional network models with stochastically assigned synapses cannot reproduce this behavior. Here we introduce a class of cortical architectures with strong and random excitatory recurrence that is stabilized by intricate, fine-tuned inhibition, optimized from a control theory perspective. Such networks transiently amplify specific activity states and can be used to reliably execute multidimensional movement patterns. Similar to the experimental observations, these transients must be preceded by a steady-state initialization phase from which the network relaxes back into the background state by way of complex internal dynamics. In our networks, excitation and inhibition are as tightly balanced as recently reported in experiments across several brain areas, suggesting inhibitory control of complex excitatory recurrence as a generic organizational principle in cortex.

Transmission of signals within the brain is essential for cognitive function, but it is not clear how neural circuits support reliable and accurate signal propagation over a sufficiently large dynamic range. Two modes of propagation have been studied: synfire chains, in which synchronous activity travels through feedforward layers of a neuronal network, and the propagation of fluctuations in firing rate across these layers. In both cases, a sufficient amount of noise, which was added to previous models from an external source, had to be included to support stable propagation. Sparse, randomly connected networks of spiking model neurons can generate chaotic patterns of activity. We investigate whether this activity, which is a more realistic noise source, is sufficient to allow for signal transmission. We find that, for rate-coded signals but not for synfire chains, such networks support robust and accurate signal reproduction through up to six layers if appropriate adjustments are made in synaptic strengths. We investigate the factors affecting transmission and show that multiple signals can propagate simultaneously along different pathways. Using this feature, we show how different types of logic gates can arise within the architecture of the random network through the strengthening of specific synapses.

Neural network modeling is often concerned with stimulus-driven responses, but most of the activity in the brain is internally generated. Here, we review network models of internally generated activity, focusing on three types of network dynamics: (a) sustained responses to transient stimuli, which provide a model of working memory; (b) oscillatory network activity; and (c) chaotic activity, which models complex patterns of background spiking in cortical and other circuits. We also review propagation of stimulus-driven activity through spontaneously active networks. Exploring these aspects of neural network dynamics is critical for understanding how neural circuits produce cognitive function.

Recent theoretical work has provided a basic understanding of signal propagation in networks of spiking neurons, but mechanisms for gating and controlling these signals have not been investigated previously. Here we introduce an idea for the gating of multiple signals in cortical networks that combines principles of signal propagation with aspects of balanced networks. Specifically, we study networks in which incoming excitatory signals are normally cancelled by locally evoked inhibition, leaving the targeted layer unresponsive. Transmission can be gated on by modulating excitatory and inhibitory gains to upset this detailed balance. We illustrate gating through detailed balance in large networks of integrate-and-fire neurons. We show successful gating of multiple signals and study failure modes that produce effects reminiscent of clinically observed pathologies. Provided that the individual signals are detectable, detailed balance has an enormous capacity for gating multiple signals.