Logarithmic distributions prove that intrinsic learning is Hebbian

In this paper, we present data for the lognormal distributions of spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas examined. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears to be a general, functional property in all cases analyzed. We then created a generic neural model to investigate adaptive learning rules that create and maintain lognormal distributions. We conclusively demonstrate that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This provides a solution to the long-standing question about the type of plasticity exhibited by intrinsic excitability

Prediction in cultured cortical neural networks

Theory suggest that networks of neurons may predict their input. Prediction may underlie most aspects of information processing and is believed to be involved in motor and cognitive control and decision-making. Retinal cells have been shown to be capable of predicting visual stimuli, and there is some evidence for prediction of input in the visual cortex and hippocampus. However, there is no proof that the ability to predict is a generic feature of neural networks. We investigated whether random in vitro neuronal networks can predict stimulation, and how prediction is related to short- and long-term memory. To answer these questions, we applied two different stimulation modalities. Focal electrical stimulation has been shown to induce long-term memory traces, whereas global optogenetic stimulation did not. We used mutual information to quantify how much activity recorded from these networks reduces the uncertainty of upcoming stimuli (prediction) or recent past stimuli (short-term memory). Cortical neural networks did predict future stimuli, with the majority of all predictive information provided by the immediate network response to the stimulus. Interestingly, prediction strongly depended on short-term memory of recent sensory inputs during focal as well as global stimulation. However, prediction required less short-term memory during focal stimulation. Furthermore, the dependency on short-term memory decreased during 20 h of focal stimulation, when long-term connectivity changes were induced. These changes are fundamental for long-term memory formation, suggesting that besides short-term memory the formation of long-term memory traces may play a role in efficient prediction.</p

Can Computers overcome Humans? Consciousness interaction and its implications

Can computers overcome human capabilities? This is a paradoxical and controversial question, particularly because there are many hidden assumptions. This article focuses on that issue putting on evidence some misconception related with future generations of machines and the understanding of the brain. It will be discussed to what extent computers might reach human capabilities, and how it could be possible only if the computer is a conscious machine. However, it will be shown that if the computer is conscious, an interference process due to consciousness would affect the information processing of the system. Therefore, it might be possible to make conscious machines to overcome human capabilities, which will have limitations as well as humans. In other words, trying to overcome human capabilities with computers implies the paradoxical conclusion that a computer will never overcome human capabilities at all, or if the computer does, it should not be considered as a computer anymore.Comment: 16th IEEE Cognitive Informatics and Cognitive Computing preprint, 8 pages; Added references and short discussion for section

EEG correlates of learning from speech presented in environmental noise

How the human brain retains relevant vocal information while suppressing irrelevant sounds is one of the ongoing challenges in cognitive neuroscience. Knowledge of the underlying mechanisms of this ability can be used to identify whether a person is distracted during listening to a target speech, especially in a learning context. This paper investigates the neural correlates of learning from the speech presented in a noisy environment using an ecologically valid learning context and electroencephalography (EEG). To this end, the following listening tasks were performed while 64-channel EEG signals were recorded: (1) attentive listening to the lectures in background sound, (2) attentive listening to the background sound presented alone, and (3) inattentive listening to the background sound. For the first task, 13 lectures of 5 min in length embedded in different types of realistic background noise were presented to participants who were asked to focus on the lectures. As background noise, multi-talker babble, continuous highway, and fluctuating traffic sounds were used. After the second task, a written exam was taken to quantify the amount of information that participants have acquired and retained from the lectures. In addition to various power spectrum-based EEG features in different frequency bands, the peak frequency and long-range temporal correlations (LRTC) of alpha-band activity were estimated. To reduce these dimensions, a principal component analysis (PCA) was applied to the different listening conditions resulting in the feature combinations that discriminate most between listening conditions and persons. Linear mixed-effect modeling was used to explain the origin of extracted principal components, showing their dependence on listening condition and type of background sound. Following this unsupervised step, a supervised analysis was performed to explain the link between the exam results and the EEG principal component scores using both linear fixed and mixed-effect modeling. Results suggest that the ability to learn from the speech presented in environmental noise can be predicted by the several components over the specific brain regions better than by knowing the background noise type. These components were linked to deterioration in attention, speech envelope following, decreased focusing during listening, cognitive prediction error, and specific inhibition mechanisms

The Role of Bottom-Up and Top-Down Cortical Interactions in Adaptation to Natural Scene Statistics

Adaptation is a mechanism by which cortical neurons adjust their responses according to recently viewed stimuli. Visual information is processed in a circuit formed by feedforward (FF) and feedback (FB) synaptic connections of neurons in different cortical layers. Here, the functional role of FF-FB streams and their synaptic dynamics in adaptation to natural stimuli is assessed in psychophysics and neural model. We propose a cortical model which predicts psychophysically observed motion adaptation aftereffects (MAE) after exposure to geometrically distorted natural image sequences. The model comprises direction selective neurons in V1 and MT connected by recurrent FF and FB dynamic synapses. Psychophysically plausible model MAEs were obtained from synaptic changes within neurons tuned to salient direction signals of the broadband natural input. It is conceived that, motion disambiguation by FF-FB interactions is critical to encode this salient information. Moreover, only FF-FB dynamic synapses operating at distinct rates predicted psychophysical MAEs at different adaptation time-scales which could not be accounted for by single rate dynamic synapses in either of the streams. Recurrent FF-FB pathways thereby play a role during adaptation in a natural environment, specifically in inducing multilevel cortical plasticity to salient information and in mediating adaptation at different time-scales

Targeting the endocannabinoid system for therapeutic purposes

The endocannabinoid system is an endogenous neuromodulatory system that regulates a plethora of physiological functions, including the modulation of memory, anxiety, pain, synaptic plasticity and neuronal excitability, among others. The activation of this system through exogenous or endogenous cannabinoid agonists has been proposed as a therapeutic strategy in different pathological states, although an important caveat to their use is the possible central adverse effects, such as memory impairment, anxiety and tolerance. The activity of the endocannabinoid system has been recently found involved in the pathophysiological conditions leading to obesity and fragile X syndrome, and the blockade of this system has also been investigated as a possible therapeutic approach. This thesis mainly focuses on the behavioral, paying more attention on the cognitive effects, cellular and molecular effects of exogenous and endogenous cannabinoids in order to identify potential therapeutic effects minimizing the negative consequences associated to the cannabinoid activation. This experimental research has been centered on the modulation of the positive and negative effects of Δ9-tetrahydrocannabinol, the main psychoactive component of the Cannabis sativa plant, the possibility to enhance the endogenous tone of specific endocannabinoids to improve certain therapeutic applications of cannabinoids, and the effects of inhibiting the endocannabinoid system in the amelioration of different traits associated to fragile X syndrome. The combination of behavioral, cellular and molecular approaches allowed the elucidation of different important aspects of the endocannabinoid system as an interesting therapeutic target.El sistema endocannabinoid és un sistema neuromodulador endogen que regula diferents funcions fisiològiques com la memòria, l’ansietat, el dolor i l’excitabilitat neuronal entre altres. L’activació d’aquest sistema per agonistes exògens o endògens ha estat usada com a estratègica terapèutica en diferents estats patològics tot i que els efectes adversos, com la pèrdua de memòria, l’ansietat o la tolerància, són el principal problema pel seu ús. El sistema endocannabinoid també s’ha trobat alterat en malalties com la obesitat o la síndrome del cromosoma X fràgil i, per tant, el bloqueig d’aquest sistema també s’ha emprat com a aproximació terapèutica. Aquesta tesis es centra en els efectes comportamentals i moleculars de l’administració exògena del Δ9-Tetrahydrocannabinol, el component principal de la planta Cannabis sativa, i en la modulació endògena del sitema endocannabinoid per tal de potenciar els efectes terapèutics minimitzant els efectes adversos dels cannabinoids. A més, en aquesta tesis també hem estudiat els posibles efectes terapèutics del bloqueig dels receptors cannabinoides en la síndrome del cromosoma X fràgil. La combinació d’aproximacions moleculars, farmacològiques, electrofisiològiques i comportamentals han permès el descobriment de diferents aspectes importants que permeten demostrar que el sistema endocannabinoid és una diana terapèutica molt interessant

A second look at memory: Different Approaches to Understanding Diversity in Memory and Cognition

Memory lies at the heart of human cognitive abilities. Therefore, understanding it from neural, psychological and computational viewpoints is of key importance for computational neuroscience, psychology and beyond. In this thesis, I explore two prominent, but different, memory systems: episodic memory and working memory. First, I propose a modification to a recent reinforcement learning algorithm for decision making in which single memories of events, i.e., episodic memories, are integrated to compute the long run value of actions. I argue that these memories are recalled and that their contributions are weighted based on context. Further, I propose that predictions made by this algorithm are combined with those that come from a standard, model-free, reinforcement learning algorithm. I suggest that humans can flexibly choose between these two sources of information to make decisions and guide actions. I show that the resulting combined model best fits data on human choices, outperforming previously proposed models. To complement these algorithmic and psychological suggestions, I present a generative model of the world according to which this sort of episodic recall is an appropriate method for making inferences and predictions of future rewards. Contrary to other suggestions for reward-based learning, this generative model can model events that not only drift continuously in time, but can also suddenly change to new or repeated events. Turning to working memory, I use information theoretic analyses to show that dynamic synapses, whose strengths adjust with usage, can increase its capacity. I argue that these components should be included in the study of working memory. The thesis ends with an explanation of the connections between these memory systems

A review of learning in biologically plausible spiking neural networks

Artificial neural networks have been used as a powerful processing tool in various areas such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged researchers to improve artificial neural networks by investigating the biological brain. Neurological research has significantly progressed in recent years and continues to reveal new characteristics of biological neurons. New technologies can now capture temporal changes in the internal activity of the brain in more detail and help clarify the relationship between brain activity and the perception of a given stimulus. This new knowledge has led to a new type of artificial neural network, the Spiking Neural Network (SNN), that draws more faithfully on biological properties to provide higher processing abilities. A review of recent developments in learning of spiking neurons is presented in this paper. First the biological background of SNN learning algorithms is reviewed. The important elements of a learning algorithm such as the neuron model, synaptic plasticity, information encoding and SNN topologies are then presented. Then, a critical review of the state-of-the-art learning algorithms for SNNs using single and multiple spikes is presented. Additionally, deep spiking neural networks are reviewed, and challenges and opportunities in the SNN field are discussed

Interação dos sistemas endocanabinóde e colinérgico muscarínico no processamento de memórias aversivas no hipocampo dorsal e cortex infralímbico de ratos

Os sistemas endocanabinóide e colinérgico muscarínico têm um importante papel modulador sobre a atividade neural tanto excitatória, quanto inibitória no sistema nervoso central, participando de inúmeros processos, entre eles, os mecanismos de aprendizagem e memória. Partindo do fato de que os receptores canabinóides CB1 têm alta concentração em áreas relevantes para a memória, como o hipocampo (responsável pelo componente contextual), ou o córtex infralímbico (envolvido na manutenção da extinção), inicialmente investigamos os efeitos do potente agonista CB1, CP55,940 sobre a consolidação e a reconsolidação da memória: essa área cortical ainda não fora estudada nesta etapa do processamento de memórias, nem aquele fármaco havia sido adequadamente investigado. A seguir, visando compreender o possível sinergismo entre os sistemas endocanabinóide CB1 e colinérgico muscarínico M4 no processamento de memórias aversivas em ratos, estudamos os efeitos da infusão concomitante de concentrações subefetivas do agonista CB1 e da toxina muscarínica seletiva para M4 (MT3 - extraída da peçonha da serpente mamba verde africana), na área CA1 do hipocampo, sobre a consolidação da memória, verificando também seus efeitos sobre a plasticidade neural de ratos Wistar machos adultos. Os resultados do presente trabalho mostraram uma clara modulação endocanabinóide, tanto da consolidação quanto da reconsolidação da memória, envolvendo igualmente receptores CB1 no hipocampo e no córtex infralímbico: o efeito amnéstico duradouro foi diferente do obtido por nosso grupo usando a menos seletiva Anandamida em outra tarefa, aversiva inibitória (de Oliveira Alvares et al., 2008b): as diferenças farmacológicas e entre tarefas explicariam, em parte, esses achados contrastantes. Na segunda parte, onde estudamos o sinergismo apenas na consolidação, constatamos uma forte interação complementar dos subsistemas CB1 e M4 afetando a resposta comportamental, com efeito amnéstico observado apenas na presença dos dois fármacos juntos (CP55,940+MT3, cada qual em concentração subefetiva). A infusão concomitante de ambos, nessas mesmas concentrações, também foi capaz de inibir a indução e a manutenção da potenciação de longa duração (LTP) na mesma região CA1 hipocampal, reforçando a hipótese de que a semelhança farmacológica observada nos efeitos de cada subsistema, com agonistas ou antagonistas, em cada uma das diferentes fases da memória, sugere que cada subsistema atua como “sobressalente”, complementar ao outro.The endocannabinoid and muscarinic cholinergic systems play a pivotal role in the modulation of neural activity, both excitatory and inhibitory, in the central nervous system, taking part in numerous processes, among which are mechanisms of learning and memory. Based on the fact that cannabinoid receptors CB1 have high concentration in areas relevant to memory, such as the hippocampus (responsible for the contextual component), or the infralimbic cortex (involved in the maintenance of extinction), we initially investigated the effects of the potent CB1 agonist CP55,940 on the consolidation and reconsolidation of memory: neither has this cortical area been studied in this phase of memory processing nor has this drug been adequately investigated. In order to understand the possible synergism between the endocannabinoid CB1 and the muscarinic cholinergic M4 systems in the processing of aversive memories in rats, we studied the effects of the concomitant infusion of subthreshold concentrations of the CB1 agonist and the selective muscarinic toxin for M4 (MT3 – extracted from the venom of the African green mamba snake), in the CA1 region of the hippocampus, on the consolidation of memory, also verifying their effects on the neural plasticity of adult male Wistar rats. The results of the present study evidenced a clear endocannabinoid modulation, both in the consolidation and reconsolidation of memory, equally involving CB1 receptors in the hippocampus and infralimbic cortex: the long-lasting amestic effect differed from that obtained by our group using the less selective Anandamide in another behavioral experiment, the aversive inhibitory task (de Oliveira Alvares et al., 2008b): pharmacological differences and those between tasks would explain, in part, these contrasting results. In the second part, in which we studied the synergism only in consolidation, we found a strong complementary interaction between CB1 and M4 subsystems affecting the behavioral response, with an amestic effect only observed in the presence of both drugs together (CP55,940+MT3, each in a subthreshold concentration). The concomitant infusion of both, in these same concentrations, was also able to inhibit the induction and maintenance of long term potentiation (LTP) in the same CA1 region of the hippocampus, reinforcing the hypothesis that the pharmacological similarities observed in the effects of each subsystem, using agonists or antagonists, in each of the different memory phases, suggest that each subsystem acts as a “spare”, complementary to each other

Improved methods for functional neuronal imaging with genetically encoded voltage indicators

Voltage imaging has the potential to revolutionise neuronal physiology, enabling high temporal and spatial resolution monitoring of sub- and supra-threshold activity in genetically defined cell classes. Before this goal is reached a number of challenges must be overcome: novel optical, genetic, and experimental techniques must be combined to deal with voltage imaging’s unique difficulties. In this thesis three techniques are applied to genetically encoded voltage indicator (GEVI) imaging. First, I describe a multifocal two-photon microscope and present a novel source localisation control and reconstruction algorithm to increase scattering resistance in functional imaging. I apply this microscope to image population and single-cell voltage signals from voltage sensitive fluorescent proteins in the first demonstration of multifocal GEVI imaging. Second, I show that a recently described genetic technique that sparsely labels cortical pyramidal cells enables single-cell resolution imaging in a one-photon widefield imaging configuration. This genetic technique allows simple, high signal-to-noise optical access to the primary excitatory cells in the cerebral cortex. Third, I present the first application of lightfield microscopy to single cell resolution neuronal voltage imaging. This technique enables single-shot capture of dendritic arbours and resolves 3D localised somatic and dendritic voltage signals. These approaches are finally evaluated for their contribution to the improvement of voltage imaging for physiology.Open Acces