Biology subjects

Pereira, U.

Publications and source records attributed to Pereira, U..

4 recordsLinked to original sources

bioRxiv2019

Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning

Sequential activity has been observed in multiple neuronal circuits across species, neural structures, and behaviors. It has been hypothesized that sequences could arise from unsupervised learning processes. However, it is still unclear whether biologically plausible synaptic plasticity rules can organize neuronal activity to form sequences whose statistics match experimental observations. Here we investigate temporally asymmetric Hebbian rules in sparsely connected recurrent rate networks, and develop a theory of the transient sequential activity observed after learning. These rules transform a sequence of random input patterns into synaptic weight updates. After learning, recalled sequential activity is reflected in the transient correlation of network activity with each of the stored input patterns. Using mean-field theory, we derive a low-dimensional description of the network dynamics and compute the storage capacity of these networks. Multiple temporal characteristics of the recalled sequential activity are consistent with experimental observations. We find that the degree of sparseness of the recalled sequences can be controlled by non-linearities in the learning rule. Furthermore, sequences maintain robust decoding, but display highly labile dynamics, when synaptic connectivity is continuously modified due to noise or storage of other patterns, similar to recent observations in hippocampus and parietal cortex. Finally, we demonstrate that our results also hold in recurrent networks of spiking neurons with separate excitatory and inhibitory populations.

neuroscience↗

bioRxiv2018

Unsupervised learning of persistent and sequential activity

Two strikingly distinct types of activity have been observed in various brain structures during delay periods of delayed response tasks: Persistent activity (PA), in which a sub-population of neurons maintains an elevated firing rate throughout an entire delay period; and Sequential activity (SA), in which sub-populations of neurons are activated sequentially in time. It has been hypothesized that both types of dynamics can be learned by the relevant networks from the statistics of their inputs, thanks to mechanisms of synaptic plasticity. However, the necessary conditions for a synaptic plasticity rule and input statistics to learn these two types of dynamics in a stable fashion are still unclear. In particular, it is unclear whether a single learning rule is able to learn both types of activity patterns, depending on the statistics of the inputs driving the network. Here, we first characterize the complete bifurcation diagram of a firing rate model of multiple excitatory populations with an inhibitory mechanism, as a function of the parameters characterizing its connectivity. We then investigate how an unsupervised temporally asymmetric Hebbian plasticity rule shapes the dynamics of the network. Consistent with previous studies, we find that for stable learning of PA and SA, an additional stabilization mechanism, such as multiplicative homeostatic plasticity, is necessary. Using the bifurcation diagram derived for fixed connectivity, we study analytically the temporal evolution and the steady state of the learned recurrent architecture as a function of parameters characterizing the external inputs. Slow changing stimuli lead to PA, while fast changing stimuli lead to SA. Our network model shows how a network with plastic synapses can stably and flexibly learn PA and SA in an unsupervised manner.

neuroscience↗

bioRxiv2017

Neuronal resonance in the theta (4-10 Hz) frequency range is modulated by dynamic changes in the input resistance

Most neurons of the mammalian brain display intrinsic resonance with frequency selectivity (fR) for inputs within the theta-range (4-10 Hz). Variations in network oscillation along this range depend on the animal behavior; however, whether neurons can dynamically tune their fR has not been addressed. Using slice electrophysiology, dynamic clamping and computer modeling we studied three types of cortical neurons, finding that the input resistance (Rin) inversely sets fR into the theta range, following a power law. We demonstrate that physiological changes in Rin modulate fR and response phase, serving as a mechanism that instantaneously tunes oscillatory responses. Moreover, these modulations are translated into spiking regimes, modifying spike frequency and timing. Since synaptic inputs reduce Rin, this modulation provides a mean for adjusting the frequency and timing of firing of individual neurons in interplay with the network fluctuations. This might be a widespread property among resonant neurons.

neuroscience↗

bioRxiv2017

Attractor dynamics in networks with learning rules inferred from in vivo data

The attractor neural network scenario is a popular scenario for memory storage in association cortex, but there is still a large gap between models based on this scenario and experimental data. We study a recurrent network model in which both learning rules and distribution of stored patterns are inferred from distributions of visual responses for novel and familiar images in inferior temporal cortex (ITC). Unlike classical attractor neural network models, our model exhibits graded activity in retrieval states, with distributions of firing rates that are close to lognormal. Inferred learning rules are close to maximizing the number of stored patterns within a family of unsupervised Hebbian learning rules, suggesting learning rules in ITC are optimized to store a large number of attractor states. Finally, we show that there exists two types of retrieval states: one in which firing rates are constant in time, another in which firing rates fluctuate chaotically.

neuroscience↗