IBYME   02675
INSTITUTO DE BIOLOGIA Y MEDICINA EXPERIMENTAL
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
A Biologically Plausible Model for Same/Different Discrimination
Autor/es:
HERN´AN G. REY, DIEGO GUTNISKY, AND B. SILVANO ZANUTTO
Lugar:
Buenos Aires
Reunión:
Congreso; 32nd Annual International Conference of the IEEE EMBS; 2010
Institución organizadora:
IEEE
Resumen:
AbstractAbstract rules can be learned by several species
(not only humans). We propose a biologically plausible model
for same/different discrimination, that can point towards the
neural basis of abstract concept learning. By including a neural
adaptation mechanism to a discriminator model formerly
introduced in the literature, selective clusters of neurons fire
depending on whether or not the stimuli compared are the same
or not. These selective neurons are consistent with experimental
findings in the literature. Moreover, reward and attention can
modulate the relative strength of each attribute/feature of the
stimulus, so more complex abstract discriminations can be
achieved using the proposed model as a building block. As
a formal model, it can be easily incorporated into several
applications in robotics and intelligent machines.
(not only humans). We propose a biologically plausible model
for same/different discrimination, that can point towards the
neural basis of abstract concept learning. By including a neural
adaptation mechanism to a discriminator model formerly
introduced in the literature, selective clusters of neurons fire
depending on whether or not the stimuli compared are the same
or not. These selective neurons are consistent with experimental
findings in the literature. Moreover, reward and attention can
modulate the relative strength of each attribute/feature of the
stimulus, so more complex abstract discriminations can be
achieved using the proposed model as a building block. As
a formal model, it can be easily incorporated into several
applications in robotics and intelligent machines.
(not only humans). We propose a biologically plausible model
for same/different discrimination, that can point towards the
neural basis of abstract concept learning. By including a neural
adaptation mechanism to a discriminator model formerly
introduced in the literature, selective clusters of neurons fire
depending on whether or not the stimuli compared are the same
or not. These selective neurons are consistent with experimental
findings in the literature. Moreover, reward and attention can
modulate the relative strength of each attribute/feature of the
stimulus, so more complex abstract discriminations can be
achieved using the proposed model as a building block. As
a formal model, it can be easily incorporated into several
applications in robotics and intelligent machines.
(not only humans). We propose a biologically plausible model
for same/different discrimination, that can point towards the
neural basis of abstract concept learning. By including a neural
adaptation mechanism to a discriminator model formerly
introduced in the literature, selective clusters of neurons fire
depending on whether or not the stimuli compared are the same
or not. These selective neurons are consistent with experimental
findings in the literature. Moreover, reward and attention can
modulate the relative strength of each attribute/feature of the
stimulus, so more complex abstract discriminations can be
achieved using the proposed model as a building block. As
a formal model, it can be easily incorporated into several
applications in robotics and intelligent machines.
(not only humans). We propose a biologically plausible model
for same/different discrimination, that can point towards the
neural basis of abstract concept learning. By including a neural
adaptation mechanism to a discriminator model formerly
introduced in the literature, selective clusters of neurons fire
depending on whether or not the stimuli compared are the same
or not. These selective neurons are consistent with experimental
findings in the literature. Moreover, reward and attention can
modulate the relative strength of each attribute/feature of the
stimulus, so more complex abstract discriminations can be
achieved using the proposed model as a building block. As
a formal model, it can be easily incorporated into several
applications in robotics and intelligent machines.
Abstract rules can be learned by several species
(not only humans). We propose a biologically plausible model
for same/different discrimination, that can point towards the
neural basis of abstract concept learning. By including a neural
adaptation mechanism to a discriminator model formerly
introduced in the literature, selective clusters of neurons fire
depending on whether or not the stimuli compared are the same
or not. These selective neurons are consistent with experimental
findings in the literature. Moreover, reward and attention can
modulate the relative strength of each attribute/feature of the
stimulus, so more complex abstract discriminations can be
achieved using the proposed model as a building block. As
a formal model, it can be easily incorporated into several
applications in robotics and intelligent machines.