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Article ; Online: Probing the structure-function relationship with neural networks constructed by solving a system of linear equations.

Mininni, Camilo J / Zanutto, B Silvano

2021 Volume 11, Issue 1, Page(s) 3808

Abstract: Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully such ... ...

Abstract	Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully such that networks reproduce anatomical, behavioural, and neurophysiological data. These parameters are usually fitted with off-the-shelf optimization algorithms that iteratively change network parameters and simulate the network to evaluate its performance and improve fitting. Here we propose to invert the fitting process by proceeding from the network dynamics towards network parameters. Firing state transitions are chosen according to the transition graph associated with the solution of a task. Then, a system of linear equations is constructed from the network firing states and membrane potentials, in a way that guarantees the consistency of the system. This allows us to uncouple the dynamical features of the model, like its neurons firing rate and correlation, from the structural features, and the task-solving algorithm implemented by the network. We employed our method to probe the structure-function relationship in a sequence memory task. The networks obtained showed connectivity and firing statistics that recapitulated experimental observations. We argue that the proposed method is a complementary and needed alternative to the way neural networks are constructed to model brain function.
Language	English
Publishing date	2021-02-15
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-82964-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Serotonergic modulation of basolateral amygdala nucleus in the extinction of reward-driven learning: The role of 5-HT bioavailability and 5-HT

Pereyra, A Ezequiel / Mininni, Camilo J / Zanutto, B Silvano

Behavioural brain research

2021 Volume 404, Page(s) 113161

Abstract: Serotonin (5-HT) neurotransmission has been associated with reward-related behaviour. Moreover, the serotonergic system modulates the basolateral amygdala (BLA), a structure involved in reward encoding, and reward prediction error. However, the role ... ...

Abstract	Serotonin (5-HT) neurotransmission has been associated with reward-related behaviour. Moreover, the serotonergic system modulates the basolateral amygdala (BLA), a structure involved in reward encoding, and reward prediction error. However, the role played by 5-HT on BLA during a reward-driven task has not been fully elucidated. In this paper, we investigated whether serotonergic modulation of the BLA is involved in reward-driven learning. To this end, we trained Long Evans rats in an operant conditioning task, and examined the effects of fluoxetine treatment (a selective serotonin reuptake inhibitor, 10 mg/kg) in combination with BLA lesions with NMDA (20 mg/mL) on extinction learning. We also investigated whether intra-BLA injection of the serotonergic 5-HT
MeSH term(s)	8-Hydroxy-2-(di-n-propylamino)tetralin/pharmacology ; Animals ; Basolateral Nuclear Complex/drug effects ; Basolateral Nuclear Complex/metabolism ; Basolateral Nuclear Complex/physiology ; Biological Availability ; Conditioning, Operant/drug effects ; Conditioning, Operant/physiology ; Extinction, Psychological/drug effects ; Extinction, Psychological/physiology ; Fluoxetine/pharmacology ; Male ; Piperazines/pharmacology ; Pyridines/pharmacology ; Rats ; Rats, Long-Evans ; Receptor, Serotonin, 5-HT1A/drug effects ; Receptor, Serotonin, 5-HT1A/metabolism ; Receptor, Serotonin, 5-HT1A/physiology ; Reward ; Serotonin/pharmacokinetics ; Serotonin/pharmacology ; Serotonin 5-HT1 Receptor Agonists/pharmacology ; Serotonin 5-HT1 Receptor Antagonists/pharmacology
Chemical Substances	Piperazines ; Pyridines ; Serotonin 5-HT1 Receptor Agonists ; Serotonin 5-HT1 Receptor Antagonists ; Fluoxetine (01K63SUP8D) ; Receptor, Serotonin, 5-HT1A (112692-38-3) ; Serotonin (333DO1RDJY) ; N-(2-(4-(2-methoxyphenyl)-1-piperazinyl)ethyl)-N-(2-pyridinyl)cyclohexanecarboxamide (71IH826FEG) ; 8-Hydroxy-2-(di-n-propylamino)tetralin (78950-78-4)
Language	English
Publishing date	2021-02-08
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	449927-x
ISSN	1872-7549 ; 0166-4328
ISSN (online)	1872-7549
ISSN	0166-4328
DOI	10.1016/j.bbr.2021.113161
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Information capacity and robustness of encoding in the medial prefrontal cortex are modulated by the bioavailability of serotonin and the time elapsed from the cue during a reward-driven task.

Pereyra, A Ezequiel / Mininni, Camilo J / Zanutto, B Silvano

Scientific reports

2021 Volume 11, Issue 1, Page(s) 13882

Abstract: Serotonin (5-HT) is a key neuromodulator of medial prefrontal cortex (mPFC) functions. Pharmacological manipulation of systemic 5-HT bioavailability alters the electrical activity of mPFC neurons. However, 5-HT modulation at the population level is not ... ...

Abstract	Serotonin (5-HT) is a key neuromodulator of medial prefrontal cortex (mPFC) functions. Pharmacological manipulation of systemic 5-HT bioavailability alters the electrical activity of mPFC neurons. However, 5-HT modulation at the population level is not well characterized. In the present study, we made single neuron extracellular recordings in the mPFC of rats performing an operant conditioning task, and analyzed the effect of systemic administration of fluoxetine (a selective serotonin reuptake inhibitor) on the information encoded in the firing activity of the neural population. Chronic (longer than 15 days), but not acute (less than 15 days), fluoxetine administration reduced the firing rate of mPFC neurons. Moreover, fluoxetine treatment enhanced pairwise entropy but diminished noise correlation and redundancy in the information encoded, thus showing how mPFC differentially encodes information as a function of 5-HT bioavailability. Information about the occurrence of the reward-predictive stimulus was maximized during reward consumption, around 3 to 4 s after the presentation of the cue, and it was higher under chronic fluoxetine treatment. However, the encoded information was less robust to noise corruption when compared to control conditions.
MeSH term(s)	Action Potentials/drug effects ; Action Potentials/physiology ; Animals ; Biological Availability ; Conditioning, Operant ; Cues ; Entropy ; Fluoxetine/pharmacology ; Male ; Prefrontal Cortex/physiology ; Rats, Long-Evans ; Reward ; Serotonin/metabolism ; Task Performance and Analysis ; Rats
Chemical Substances	Fluoxetine (01K63SUP8D) ; Serotonin (333DO1RDJY)
Language	English
Publishing date	2021-07-06
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-93313-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Probing the structure–function relationship with neural networks constructed by solving a system of linear equations

Camilo J. Mininni / B. Silvano Zanutto

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 18

Abstract: Abstract Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully ...

Abstract	Abstract Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully such that networks reproduce anatomical, behavioural, and neurophysiological data. These parameters are usually fitted with off-the-shelf optimization algorithms that iteratively change network parameters and simulate the network to evaluate its performance and improve fitting. Here we propose to invert the fitting process by proceeding from the network dynamics towards network parameters. Firing state transitions are chosen according to the transition graph associated with the solution of a task. Then, a system of linear equations is constructed from the network firing states and membrane potentials, in a way that guarantees the consistency of the system. This allows us to uncouple the dynamical features of the model, like its neurons firing rate and correlation, from the structural features, and the task-solving algorithm implemented by the network. We employed our method to probe the structure–function relationship in a sequence memory task. The networks obtained showed connectivity and firing statistics that recapitulated experimental observations. We argue that the proposed method is a complementary and needed alternative to the way neural networks are constructed to model brain function.
Keywords	Medicine ; R ; Science ; Q
Subject code	612
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Information capacity and robustness of encoding in the medial prefrontal cortex are modulated by the bioavailability of serotonin and the time elapsed from the cue during a reward-driven task

A. Ezequiel Pereyra / Camilo J. Mininni / B. Silvano Zanutto

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 13

Abstract: Abstract Serotonin (5-HT) is a key neuromodulator of medial prefrontal cortex (mPFC) functions. Pharmacological manipulation of systemic 5-HT bioavailability alters the electrical activity of mPFC neurons. However, 5-HT modulation at the population level ...

Abstract	Abstract Serotonin (5-HT) is a key neuromodulator of medial prefrontal cortex (mPFC) functions. Pharmacological manipulation of systemic 5-HT bioavailability alters the electrical activity of mPFC neurons. However, 5-HT modulation at the population level is not well characterized. In the present study, we made single neuron extracellular recordings in the mPFC of rats performing an operant conditioning task, and analyzed the effect of systemic administration of fluoxetine (a selective serotonin reuptake inhibitor) on the information encoded in the firing activity of the neural population. Chronic (longer than 15 days), but not acute (less than 15 days), fluoxetine administration reduced the firing rate of mPFC neurons. Moreover, fluoxetine treatment enhanced pairwise entropy but diminished noise correlation and redundancy in the information encoded, thus showing how mPFC differentially encodes information as a function of 5-HT bioavailability. Information about the occurrence of the reward-predictive stimulus was maximized during reward consumption, around 3 to 4 s after the presentation of the cue, and it was higher under chronic fluoxetine treatment. However, the encoded information was less robust to noise corruption when compared to control conditions.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2021-07-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Exploring the limits of learning: Segregation of information integration and response selection is required for learning a serial reversal task.

Mininni, Camilo Juan / Zanutto, B Silvano

PloS one

2017 Volume 12, Issue 10, Page(s) e0186959

Abstract: Animals are proposed to learn the latent rules governing their environment in order to maximize their chances of survival. However, rules may change without notice, forcing animals to keep a memory of which one is currently at work. Rule switching can ... ...

Abstract	Animals are proposed to learn the latent rules governing their environment in order to maximize their chances of survival. However, rules may change without notice, forcing animals to keep a memory of which one is currently at work. Rule switching can lead to situations in which the same stimulus/response pairing is positively and negatively rewarded in the long run, depending on variables that are not accessible to the animal. This fact raises questions on how neural systems are capable of reinforcement learning in environments where the reinforcement is inconsistent. Here we address this issue by asking about which aspects of connectivity, neural excitability and synaptic plasticity are key for a very general, stochastic spiking neural network model to solve a task in which rules change without being cued, taking the serial reversal task (SRT) as paradigm. Contrary to what could be expected, we found strong limitations for biologically plausible networks to solve the SRT. Especially, we proved that no network of neurons can learn a SRT if it is a single neural population that integrates stimuli information and at the same time is responsible of choosing the behavioural response. This limitation is independent of the number of neurons, neuronal dynamics or plasticity rules, and arises from the fact that plasticity is locally computed at each synapse, and that synaptic changes and neuronal activity are mutually dependent processes. We propose and characterize a spiking neural network model that solves the SRT, which relies on separating the functions of stimuli integration and response selection. The model suggests that experimental efforts to understand neural function should focus on the characterization of neural circuits according to their connectivity, neural dynamics, and the degree of modulation of synaptic plasticity with reward.
Language	English
Publishing date	2017
Publishing country	United States
Document type	Journal Article
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0186959
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: High mutual cooperation rates in rats learning reciprocal altruism: The role of payoff matrix.

Delmas, Guillermo E / Lew, Sergio E / Zanutto, B Silvano

PloS one

2019 Volume 14, Issue 1, Page(s) e0204837

Abstract: Cooperation is one of the most studied paradigms for the understanding of social interactions. Reciprocal altruism -a special type of cooperation that is taught by means of the iterated prisoner dilemma game (iPD)- has been shown to emerge in different ... ...

Abstract	Cooperation is one of the most studied paradigms for the understanding of social interactions. Reciprocal altruism -a special type of cooperation that is taught by means of the iterated prisoner dilemma game (iPD)- has been shown to emerge in different species with different success rates. When playing iPD against a reciprocal opponent, the larger theoretical long-term reward is delivered when both players cooperate mutually. In this work, we trained rats in iPD against an opponent playing a Tit for Tat strategy, using a payoff matrix with positive and negative reinforcements, that is food and timeout respectively. We showed for the first time, that experimental rats were able to learn reciprocal altruism with a high average cooperation rate, where the most probable state was mutual cooperation (85%). Although when subjects defected, the most probable behavior was to go back to mutual cooperation. When we modified the matrix by increasing temptation rewards (T) or by increasing cooperation rewards (R), the cooperation rate decreased. In conclusion, we observe that an iPD matrix with large positive reward improves less cooperation than one with small rewards, shown that satisfying the relationship among iPD reinforcement was not enough to achieve high mutual cooperation behavior. Therefore, using positive and negative reinforcements and an appropriate contrast between rewards, rats have cognitive capacity to learn reciprocal altruism. This finding allows to infer that the learning of reciprocal altruism has early appeared in evolution.
MeSH term(s)	Altruism ; Animal Communication ; Animals ; Behavior Observation Techniques/methods ; Biological Evolution ; Cooperative Behavior ; Male ; Prisoner Dilemma ; Rats/psychology ; Rats, Long-Evans ; Reward
Language	English
Publishing date	2019-01-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0204837
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics.

Dematties, Dario / Rizzi, Silvio / Thiruvathukal, George K / Pérez, Mauricio David / Wainselboim, Alejandro / Zanutto, B Silvano

Frontiers in neural circuits

2020 Volume 14, Page(s) 12

Abstract: A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, ...

Abstract	A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches-on the other hand-contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited-bootstrapping from the features returned by Word Embedding mechanisms-to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.
MeSH term(s)	Afferent Pathways/physiology ; Computer Simulation ; Dendrites/physiology ; Humans ; Linguistics/methods ; Neocortex/physiology ; Nerve Net/physiology ; Speech Perception/physiology
Language	English
Publishing date	2020-04-16
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2452968-0
ISSN	1662-5110 ; 1662-5110
ISSN (online)	1662-5110
ISSN	1662-5110
DOI	10.3389/fncir.2020.00012
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: High mutual cooperation rates in rats learning reciprocal altruism

Guillermo E Delmas / Sergio E Lew / B Silvano Zanutto

PLoS ONE, Vol 14, Iss 1, p e

The role of payoff matrix.

2019 Volume 0204837

Abstract	Cooperation is one of the most studied paradigms for the understanding of social interactions. Reciprocal altruism -a special type of cooperation that is taught by means of the iterated prisoner dilemma game (iPD)- has been shown to emerge in different species with different success rates. When playing iPD against a reciprocal opponent, the larger theoretical long-term reward is delivered when both players cooperate mutually. In this work, we trained rats in iPD against an opponent playing a Tit for Tat strategy, using a payoff matrix with positive and negative reinforcements, that is food and timeout respectively. We showed for the first time, that experimental rats were able to learn reciprocal altruism with a high average cooperation rate, where the most probable state was mutual cooperation (85%). Although when subjects defected, the most probable behavior was to go back to mutual cooperation. When we modified the matrix by increasing temptation rewards (T) or by increasing cooperation rewards (R), the cooperation rate decreased. In conclusion, we observe that an iPD matrix with large positive reward improves less cooperation than one with small rewards, shown that satisfying the relationship among iPD reinforcement was not enough to achieve high mutual cooperation behavior. Therefore, using positive and negative reinforcements and an appropriate contrast between rewards, rats have cognitive capacity to learn reciprocal altruism. This finding allows to infer that the learning of reciprocal altruism has early appeared in evolution.
Keywords	Medicine ; R ; Science ; Q
Subject code	650
Language	English
Publishing date	2019-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Exploring the limits of learning

Camilo Juan Mininni / B Silvano Zanutto

PLoS ONE, Vol 12, Iss 10, p e

Segregation of information integration and response selection is required for learning a serial reversal task.

2017 Volume 0186959

Abstract	Animals are proposed to learn the latent rules governing their environment in order to maximize their chances of survival. However, rules may change without notice, forcing animals to keep a memory of which one is currently at work. Rule switching can lead to situations in which the same stimulus/response pairing is positively and negatively rewarded in the long run, depending on variables that are not accessible to the animal. This fact raises questions on how neural systems are capable of reinforcement learning in environments where the reinforcement is inconsistent. Here we address this issue by asking about which aspects of connectivity, neural excitability and synaptic plasticity are key for a very general, stochastic spiking neural network model to solve a task in which rules change without being cued, taking the serial reversal task (SRT) as paradigm. Contrary to what could be expected, we found strong limitations for biologically plausible networks to solve the SRT. Especially, we proved that no network of neurons can learn a SRT if it is a single neural population that integrates stimuli information and at the same time is responsible of choosing the behavioural response. This limitation is independent of the number of neurons, neuronal dynamics or plasticity rules, and arises from the fact that plasticity is locally computed at each synapse, and that synaptic changes and neuronal activity are mutually dependent processes. We propose and characterize a spiking neural network model that solves the SRT, which relies on separating the functions of stimuli integration and response selection. The model suggests that experimental efforts to understand neural function should focus on the characterization of neural circuits according to their connectivity, neural dynamics, and the degree of modulation of synaptic plasticity with reward.
Keywords	Medicine ; R ; Science ; Q
Subject code	612
Language	English
Publishing date	2017-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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